[
  {
    "path": ".gitignore",
    "content": "\n*.o\n*.d\n*.pyc\n*.gz\n*.csv\n*.pk\ndata/*\nbin/*\ndoc/latex/*\n.DS_Store\n**/.DS_Store\n*.pyc\n*.pyc\n!doc/latex/refman.pdf\n!doc/Doxyfile\n*.pyc\nbin/*\ndata/.DS_Store\n"
  },
  {
    "path": "BookConstructor.sh",
    "content": "#!/bin/bash\n\n# wrapper to the c++ executable BookConstructor.\n\ndisplay_usage() {\n    echo\n    echo \"usage:    $0 [-lfh] [-n #] data_folder mm/dd/yyyy venue ticker\"\n    echo \"          $0 [-l] data_folder\"\n    echo \"          $0 [-h]\"\n    echo\n    echo \" -h, --help     Display usage instructions\"\n    echo \" -l, --list     Display all the date venues available at data_folder/binary\"\n    echo \" -f, --force    To force program execution if output files already exists\"\n    echo \" -n,            Number of levels to store for the book, default is 5\"\n    echo\n}\n\n# fix naming convention\nfix_naming() {\n    #\n    # assumes that the files with _ITCH_50 have a format like yyyymmdd.VENUE_ITCH_50.gz\n    # instead of mmddyyy.VENUE_ITCH50.gz\n    # this happens, as of 04-29-2019, for PSX and BX\n    #\n    FLAG=$( ls \"$DATA_FOLDER\"binary | grep --quiet _ITCH_50);\n    if [ $FLAG ]; then echo; fi\n    for line in $( ls \"$DATA_FOLDER\"binary | grep _ITCH_50); do\n        read new_file < <(echo ${line:4:2}${line:6:2}${line:0:4}${line:8} | sed 's/_ITCH_50/_ITCH50/')\n        mv \"$DATA_FOLDER\"binary/$line \"$DATA_FOLDER\"binary/$new_file\n        echo $line has been renamed as $new_file\n    done\n    if [ $FLAG ]; then echo; fi\n}\n\ndisplay_list() {\n    # displays files in DATA_FOLDER as\n    fix_naming\n    echo\n    echo \"Available dates and venues: \"\n    echo\n    echo $'mm-dd-yyyy\\tVenue'\n\n    ls \"$DATA_FOLDER\"binary | egrep '^[0-9]{8}.(NASDAQ|BX|PSX)_ITCH50.gz' |\n    sed -n 's/[0-9]\\{2\\}/&-/p' |\n    sed -n 's/[0-9]\\{2\\}-[0-9]\\{2\\}/&-/p' |\n    sed -n 's/\\([0-9]\\{2\\}-[0-9]\\{2\\}-[0-9]\\{4\\}\\)\\.\\([^_]*\\)_ITCH50.gz/\\1\\t\\2/p' | tr t $'\\t'\n}\n\nPOSITIONAL=()\nDISPLAY_LIST_FLAG=0\nLEVELS_FLAG=0\nFORCE_FLAG=0\nwhile [[ $# -gt 0 ]]\ndo\nkey=\"$1\"\n\ncase $key in\n    -l|--list)\n    DISPLAY_LIST_FLAG=1\n    shift\n    ;;\n    -n)\n    LEVELS_FLAG=1\n    LEVELS=\"$2\"\n    shift\n    shift\n    ;;\n    -f|--force)\n    FORCE_FLAG=1\n    shift\n    ;;\n    -h|--help)\n    display_usage\n    shift\n    exit\n    ;;\n    -*)\n    echo\n    echo Unrecognied option: $key\n    echo\n    display_usage\n    exit\n    ;;\n    *)\n    POSITIONAL+=(\"$1\")\n    shift\n    ;;\nesac\ndone\n\nset -- \"${POSITIONAL[@]}\"\n\n# required arguments\nif [ ${#POSITIONAL[@]} -lt 4 -a $DISPLAY_LIST_FLAG -eq 0 ]; then\n    display_usage\n    exit\nelif [ $DISPLAY_LIST_FLAG -eq 1 -a ${#POSITIONAL[@]} -eq 1 ]; then\n    DATA_FOLDER=${POSITIONAL[0]}\n    [[ \"${DATA_FOLDER}\" != */ ]] && DATA_FOLDER=${DATA_FOLDER}\"/\"\n    display_list\n    exit\nfi\n\nDATA_FOLDER=${POSITIONAL[0]}\nDATE=${POSITIONAL[1]}\nVENUE=${POSITIONAL[2]}\nSTOCK=${POSITIONAL[3]}\n\nTMP=/tmp/\n\n# add trailing backslah if needed\n[[ \"${DATA_FOLDER}\" != */ ]] && DATA_FOLDER=${DATA_FOLDER}\"/\"\n\nif [[ ! -z $DATA_FOLDER ]]; then\n    :\nelse\n    echo No directory named $DATA_FOLDER\n    exit\nfi\n\n# delete backshlash from date\nread DATE < <(echo $DATE | tr -d /)\n\nif [ -e \"$DATA_FOLDER\"binary -a -e \"$DATA_FOLDER\"messages -a -e \"$DATA_FOLDER\"book ]; then\n    :\nelse\n    echo\n    echo folder structure should be:\n    echo\n    echo $DATA_FOLDER\n    echo \"|\"\n    echo \"|-binary\"\n    echo \"|-messages\"\n    echo \"|-book\"\n    exit\nfi\n\n# if -l is invoked\nif [ $DISPLAY_LIST_FLAG == 1 ]; then\n    display_list\n    exit\nfi\n\n# if -n is not invoked\nif [ $LEVELS_FLAG == 0 ]; then\n    LEVELS=5\nfi\n\nfix_naming\n\n# check if date and venues combination is present\nPROTOCOL_FORMAT=_ITCH50\n\n# date and venue strign to check if the combination is available n the data file\nSTR_DATE_VENUE=${DATE:0:2}-${DATE:2:2}-${DATE:4}$'\\t'$VENUE\n\n\n# Check if choosen stock exists in corresponding file\nSTR_STOCK_DATE=${DATE:4}${DATE:0:2}${DATE:2:2}\n\n# 1. option is to download data and then check in specific folder\n# FILE_STOCK_LOCATE=\"$DATA_FOLDER\"stock_locate_codes/\"$VENUE\"_stocklocate_\"$STR_STOCK_DATE\".txt\n\n# 2. option is check it directly on server\nif [ \\( $VENUE == NASDAQ \\) ]; then\n    URL_VENUE=ndq\nelse\n    URL_VENUE=$VENUE\nfi\n\nNAME=$URL_VENUE\"_stocklocate_\"$STR_STOCK_DATE\".txt\"\nBASE_URL=ftp://anonymous:@emi.nasdaq.com/ITCH/Stock_Locate_Codes/\nURL=$BASE_URL$NAME\n\nFOUND_URL=0;\n\nif curl --output /dev/null --silent --head --fail $URL; then\n  FOUND_URL=1\n  URL_OUT=$(curl --silent $URL 2>&1)\nelse\n  echo Unable to check if stock $STOCK is present on URL: $URL, because of invalid URL.\n  echo Still reconstructing the book. Attention: it might be empty.\n  echo\n\n  URL_OUT=$STOCK\nfi\n\nif display_list | grep --quiet \"$STR_DATE_VENUE\" ; then\n    # 2. option condition:\n    #if [ $FOUND_URL == 1 ] && $(echo $URL_OUT | grep -cq $STOCK ); then\n\n    # 1. option condition :\n    #if cat $FILE_STOCK_LOCATE | grep -cq $STOCK; then\n\n        FILE_NAME=$DATE.$VENUE$PROTOCOL_FORMAT.gz\n        INPUT_FILE_PATH=\"$DATA_FOLDER\"binary/$FILE_NAME\n\n        DECOMPRESSED_INPUT_FILE_PATH=$TMP$DATE.$VENUE$PROTOCOL_FORMAT\n\n        if [ -e $DECOMPRESSED_INPUT_FILE_PATH ]; then\n            :\n        else\n            echo\n            echo copying $INPUT_FILE_PATH into a temporary folder\n            cp $INPUT_FILE_PATH $TMP\n            echo\n        fi\n\n        if [ -e $DECOMPRESSED_INPUT_FILE_PATH ]; then\n            echo\n            echo $DECOMPRESSED_INPUT_FILE_PATH already exists. Skipping decompression\n            echo\n        else\n            echo decompressing $FILE_NAME\n            gzip -d $TMP$FILE_NAME\n        fi\n\n        GZIP_STATUS=$?\n        BOOK_DIR=\"$DATA_FOLDER\"book/\n        MESS_DIR=\"$DATA_FOLDER\"messages/\n\n        if (($GZIP_STATUS==0)); then\n            BOOK_FILE_NAME=\"$DATE.$VENUE$PROTOCOL_FORMAT\"_\"$STOCK\"_book_$LEVELS.csv\n            MESS_FILE_NAME=\"$DATE.$VENUE$PROTOCOL_FORMAT\"_\"$STOCK\"_message.csv\n            if [ -e $BOOK_DIR$BOOK_FILE_NAME -a -e $MESS_DIR$MESS_FILE_NAME -a $FORCE_FLAG -eq 0 ]; then\n                echo\n                echo $BOOK_FILE_NAME and $MESS_FILE_NAME already exists. To force Execution run with -f option.\n                echo\n                exit\n            else\n                DIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\n                \"$DIR\"/bin/BookConstructor $DECOMPRESSED_INPUT_FILE_PATH $TMP $TMP $LEVELS $STOCK\n                echo\n                echo moving output files to $BOOK_DIR and $MESS_DIR\n                echo\n                yes | mv $TMP$BOOK_FILE_NAME $BOOK_DIR\n                yes | mv $TMP$MESS_FILE_NAME $MESS_DIR\n                if [ -e $TMP$FILE_NAME ]; then\n                    rm $TMP$FILE_NAME\n                fi\n            fi\n        else\n            echo Decompression of $TMP$FILE_NAME not sucsesfull\n        fi\n    # else\n    #     echo\n    #     echo Non-existing stock ticker has been inserted.\n    #     echo For more information on existing tickers refer to:\n    #     # 1.option\n    #     #echo $FILE_STOCK_LOCATE\n\n    #     # 2. option\n    #     echo $URL\n    #     echo\n    #     exit\n    # fi\nelse\n    echo\n    echo $STR_DATE_VENUE not available\n    echo\n    display_list\n    exit\nfi\n"
  },
  {
    "path": "LICENSE.txt",
    "content": "\n                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   Copyright 2019 M.Bernasconi de Luca, O.Dragić, L. Fusco\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "Makefile",
    "content": "CXXFLAGS += -Wall -std=c++11 \\\n   -Werror \\\n   -Wextra \\\n   -Wconversion \\\n   -Wno-deprecated \\\n   -Winit-self \\\n   -Wsign-conversion \\\n   -Wredundant-decls \\\n   -Wvla -Wshadow -Wctor-dtor-privacy -Wnon-virtual-dtor -Woverloaded-virtual \\\n   -Winit-self \\\n   -Wpointer-arith \\\n   -Wcast-qual \\\n   -Wcast-align \\\n   -Wdouble-promotion \\\n   -Wold-style-cast -Wno-error=old-style-cast \\\n   -Wsign-promo \\\n   -Wswitch-enum \\\n   -Wswitch-default \\\n   -MMD \\\n   -O3 \\\n#   -Wundef\n\nCPPFLAGS += -I$(INC_DIR)\n\nLDFLAGS += \t-lgtest_main \\\n\t\t\t-lgtest \\\n\t\t\t-lpthread \\\n\t\t\t-pthread \\\n\t\t\t-lgmock\n\nEXE = BookConstructor\nEXE_TEST = executeTests\n\nSCR_DIR = src\nBIN_DIR = bin\nINC_DIR = include\nBUILD_DIR = build\nTEST_DIR = gtests\n\nSOURCES=$(wildcard $(SCR_DIR)/*.cpp)\nOBJECTS=$(SOURCES:$(SCR_DIR)/%.cpp=$(BUILD_DIR)/%.o)\n\nSOURCES_T=$(wildcard $(TEST_DIR)/*.cpp)\nOBJECTS_T=$(SOURCES_T:$(TEST_DIR)/%.cpp=$(BUILD_DIR)/%.o) $(filter-out $(BUILD_DIR)/main.o, $(OBJECTS))\n\n# --------------------------------------------------------------\n\n.PHONY: all clean distclean\n\n.DEFAULT_GOAL:= all\n\nall: directories $(BIN_DIR)/$(EXE)\n\n#Make the Directories\ndirectories:\n\t@mkdir -p $(BUILD_DIR) $(BIN_DIR)\n\n# --------------------------------------------------------------\n\n$(BIN_DIR)/$(EXE): $(OBJECTS)\n\t@$(CXX) $(CXXFLAGS) $^ -o $@\n\n# --------------------------------------------------------------\n\ntest: directories $(BIN_DIR)/$(EXE_TEST)\n\n$(BIN_DIR)/$(EXE_TEST): $(OBJECTS_T)\n\t@$(CXX) $(CXXFLAGS) $^ $(LDFLAGS) -o $@ #put LDFLAGS before objects and LDLIBS after\n\n# --------------------------------------------------------------\n\n$(BUILD_DIR)/%.o : $(SCR_DIR)/%.cpp #$(SCR_DIR)/%.h $(SCR_DIR)/%.d\n\t@$(CXX) $(CXXFLAGS) $(CPPFLAGS) -c $< -o $@\n\t@echo \"Compiling: $<\"\n\n$(BUILD_DIR)/%.o : $(TEST_DIR)/%.cpp\n\t@$(CXX) $(CXXFLAGS) $(CPPFLAGS) -c $< -o $@\n\t@echo \"Compiling: $<\"\n# --------------------------------------------------------------\n\n-include $(OBJECTS:%.o=%.d) #(.d are dependency files automatically produced by -MMD flag)\n\nclean:\n\t@$(RM) $(BUILD_DIR)/* #delete all files(.o, .d)\n\t@echo \"Removing: * from $(BUILD_DIR)\"\n\ndistclean: clean\n\t@$(RM) $(BIN_DIR)/$(EXE) $(BIN_DIR)/$(EXE_TEST)\n\t@echo \"Removing: * from $(BIN_DIR)\"\n\nprint-%  : ; @echo $* = $($*)\n"
  },
  {
    "path": "README.md",
    "content": "# NASDAQ ITCH 50 Book Constructor\n> Given the NASDAQ Total View ITCH 50 data feed, reconstruct the full depth order book and related messages.\n\nThis is an efficient c++ implementation of reconstructing a Limit Order Book from data feed messages issued by NASDAQ according to the ITCH 50 data protocol specified at <https://www.nasdaqtrader.com/content/technicalsupport/specifications/dataproducts/NQTVITCHSpecification.pdf>. Some samples data are publicly available through NASDAQ public ftp at <ftp://emi.nasdaq.com/ITCH/>. The program outputs two csv files containing the messages and the related limit order book for the relative stock.\n\n# Folder Structure\n\n```\nITCH\n│\n└───bin     (c++ executable)\n│\n└───build   (dependency and object files)\n│\n└───doc\n│   │\n│   └───latex       (LaTex-pdf documentation created with Doxygen)\n│\n└───data\n│   │\n│   └───binary      (ITCH binary gzip compressed files from NASDAQ)\n│   │\n│   └───book        (output of book csv)\n│   │\n│   └───messages    (output of messages csv)\n│   │\n│   └───pk          (pickle object files)\n│\n└───gtests    (.cpp for tests)\n│\n└───images    (images for README.md Markdown)\n│\n└───include   (.hpp for main program)\n│\n└───examples  (python implementation of some application example)\n│\n└───src       (.cpp for main program)\n\n```\n\n# Brief Description\n\n![](images/OB.png)\n\nThis program aims to facilitate research in High Frequency Trading (HFT) providing the maximum amount of data released by NASDAQ, ready for use.\\\nNASDAQ receives orders by traders and market participants, then its matching engine constructs the order book according to specific rules that may depend on the venue, and eventually sells to clients the data feed.\\\nWe were able to reconstruct the book with total depth at about 1Mio-2Mio messages per second, this figure also includes the parsing from the binary data.\\\nTo understand the difficulties of such an exercise is important to understand that the messages do not coincide with the orders received by NASDAQ, i.e. we do not need a matching engine to match supply and demand, but we are already given the result of the matching engine. Apart from interpreting the binary data according to the specification of the protocol we also have to retrieve information of past orders that new messages are referring to.\\\nWe can see this in the following example of submission and deletion of an order, according to the specifications the Add and Delete order would be:\n\n| Message Type | Locate | Tracking | ns since 00:00 | Order id | Buy/Sell | Size | Stock | Price   | MPID |\n|--------------|--------|----------|----------------|----------|----------|------|-------|---------|------|\n| A            | 8007   | 0        | 28802131792425 | 45785    | B        | 3000 | USO   | 14.7200 | id_1 |\n| ...          |        |          |                |          |          |      |       |         |      |\n| D            | 8007   | 0        | 28802131843697 | 45785    |          |      |       |         | id_1 |\n\nAs we can see once we observe the deletion order no information about the direction, size, stock and price are reported. Hence at each time we have to keep track of all the active orders in the book, in order to know what to do once we encounter the deletion order.\n\nThe output of the program are two .csv file with the following structure:\n\n##### messages of 08/30/2018 PSX AAPL\n\n| time           | type | id    | side | size | price  | cancSize | execSize | oldId | oldSize | oldPrice | MPID |\n|----------------|------|-------|------|------|--------|----------|----------|-------|---------|----------|------|\n| 29041353544851 | A    | 49816 | 1    | 100  | 223.39 |          |          |       |         |          | id_1 |\n| 29041495720727 | D    | 49816 | 1    | 0    | 223.39 | 100      |          |       |         |          | id_1 |\n| ...            | ...  |       |      |      |        |          |          |       |         |          |      |\n\n##### book of 08/30/2018 PSX AAPL\n\n| time           | 1_bid_price | 1_bid_vol | 1_ask_price | 1_ask_vol | ... | n_ask_vol |\n|----------------|-------------|-----------|-------------|-----------|-----|-----------|\n| 29041353544851 |             |           | 223.39      | 100       | ... | ...       |\n| 29041495720727 |             |           |             |           |     |           |\n| ...            |             |           |             |           |     |           |\n\n## Installation\n\nOS X & Linux:\n\n\n```sh\ngit clone https://github.com/martinobdl/ITCH\ncd ITCH\nmake\n```\n\nIn order to get some data needed to run the program **(800MB)**\n```sh\nwget ftp://anonymous:@emi.nasdaq.com/ITCH/Nasdaq\\ PSX\\ ITCH/20190327.PSX_ITCH_50.gz -P ./data/binary\n\n# this file exists at date 05/02/2019\n# otherwise download manually from the ftp and put the\n# .gz file in ITCH/data/binary\n```\n\n## Usage example\n\nTo reconstruct the book we can use the bash wrapper BookConstructor.sh which has the following usage:\n\n```\nusage:    ./BookConstructor.sh [-lfh] [-n #] data_folder mm/dd/yyyy venue ticker\n          ./BookConstructor.sh [-l] data_folder\n          ./BookConstructor.sh [-h]\n\n -h, --help     Display usage instructions\n -l, --list     Display all the date venues available at data_folder/binary\n -f, --force    To force program execution if output files already exists\n -n,            Number of levels to store for the book, default is 5\n```\n\nfor example `./BookConstructor.sh data_folder mm/dd/yyyy venue ticker -n N`\nwill produce two output files named:\n\n```\ndata_folder/book/mmddyyyy.venue_ITCH50_ticker_book_N.csv\ndata_folder/messages/mmddyyyy.venue_ITCH50_ticker_message.csv\n```\n\n###### example\n\nGiven that you have in `/../../ITCH/data/binary` the file `PSX_ITCH/20190327.PSX_ITCH_50.gz`\n\n``` sh\ncd /../../ITCH\n./BookConstructor.sh ./data 03/27/2019 PSX SPY\n```\n\nthis will create two output .csv files, namely:\n``` sh\n/../../ITCH/data/book/03272019.PSX_ITCH50_AAPL_book_5.csv\n/../../ITCH/data/messages/03272019.PSX_ITCH50_AAPL_message.csv\n```\n\n# Detailed output description\n\nHere we report detailed description of message file output of the program:\n\nAt discrete time T1,T2,T3,... an output will be released by NASDAQ, the field names may have different meaning depending on the type of message. Hence here we will give a detailed description of the message csv.\n\n| time | type | id     | side                   | size                                                        | price                                      | cancSize                      | execSize                    | oldId        | oldSize                           | oldPrice                          | MPID                                                 |\n|------|------|--------|------------------------|-------------------------------------------------------------|--------------------------------------------|-------------------------------|-----------------------------|--------------|-----------------------------------|-----------------------------------|------|\n| ...  | A    | ...    | ...                    | added size                                                  | limit price of the add order               | -                             | -                           | -            | -                                 | -                                 | mpid of the market partecipant who issued the order  |\n| ...  | D    | ...    | ...                    | remaining size after the deletion (may be total or partial) | price of the original order                | canceled size by the message | -                           | -            | -                                 | -                                 | mpid of the market partecipant who deleted the order |\n| ...  | E    | ...    | ...                    | remaining size after the execution                          | price of the order being executed          | -                             | size being executed         | -            | -                                 | -                                 | mpid of the mp whos resting order is being executed |\n| ...  | P    | ...    | ...                    | execution of an hidden order                                | price of the execution                     | -                             | size of the hidden execution | -            | -                                 | -                                 | - |\n| ...  | R    | new id | equal to the old order | new size                                                    | new limit size                             | -                             | -                           | old order id | size of the order being replaced  | price of the order being replaced | same as the old order (if any) |\n| ...  | C    | ...    | ...                    | remaining size after the execution                          | price at which the order is being executed | -                             | -                           | -            | -                                 | original price of the order       | the mpid of the executed order |\n\n\n## Development setup\n\nIn the gtest folders there are unit tests in order to test the principal components of the software, they are the Google c++ unittesting libraries. To compile and run the tests you would need the gtests and gmock libraries.\nThey can be installed in the following way. Refer to <https://github.com/google/googletest/blob/master/googletest/README.md> for more details about the installation.\n\nTo install gtest-gmock:\n\n```sh\n# gtests\nsudo apt-get install libgtest-dev\nsudo apt-get install cmake\ncd /usr/src/gtest\nsudo cmake CMakeLists.txt\nsudo make\nsudo cp *.a /usr/lib\n\n#gmock\nsudo apt-get install libgmock-dev\ncd /usr/src/gmock\nsudo mkdir build\nsudo cmake ..\nsudo make\nsudo cp *.a /usr/lib\n```\n\nTo run the tests:\n```sh\ncd /../../ITCH\nmake test\n./bin/executeTests\n```\n\n## Application Example\n\nIn the python folder there are some examples on how the program might be used to perform research on HFT.\\\n`environment.py` is the environment definition and `Algo.py` is the agent abstract class from which we would implement the strategy.\\\nThe environment definition provides also some visualization tools.\n\nrequirements:\n```\npython3\nnumpy\npandas\nmatplotlib\ntkinter\ntqdm\n```\n\n###### To run the environment visualization tool:\n\n```sh\npython3 /../../ITCH/python/environment.py mmddyyyy venue stock\n```\n\n###### To run a simple low regret fixed spread strategy:\n```sh\npython3 /../../ITCH/python/Algo_fixedSpread.py\n```\n\nThe implemented strategy can be found at\n\n[# 2013] Abernethy, J., Kale, S., Adaptive Market Making via Online Learning.\n\n## Screen shots\n\n### Implementation Class Diagram\n> For more details about the API see the documentation in the doc/ folder\n![](images/classDiagram.png)\n\n### Order Book visualization Tool (from environment.py)\n![](images/Figure_2.png)\n![](images/ezgif-1-77b8a25c6b7c.gif)\n\n\n--- \n### Citations\nIf you use this software in your research, please cite the paper using the following BibTeX:\n\n```\n@misc{https://doi.org/10.5281/zenodo.5209267,\n  doi = {10.5281/ZENODO.5209267},\n  url = {https://zenodo.org/record/5209267},\n  author = {Bernasconi-De-Luca,  Martino and Fusco,  Luigi and Dragić,  Ozrenka},\n  title = {martinobdl/ITCH: ITCH50Converter},\n  publisher = {Zenodo},\n  year = {2021},\n  copyright = {Open Access}\n}\n```\n\n\n\n## Contributors\n\n* Luigi Fusco                           <https://github.com/luigif9>\n* Ozrenka Dragić                        <https://github.com/oz-dr>\n* Martino Bernasconi-de-Luca            <https://github.com/martinobdl>\n\n"
  },
  {
    "path": "doc/Doxyfile",
    "content": "# Doxyfile 1.8.15\n\n# This file describes the settings to be used by the documentation system\n# doxygen (www.doxygen.org) for a project.\n#\n# All text after a double hash (##) is considered a comment and is placed in\n# front of the TAG it is preceding.\n#\n# All text after a single hash (#) is considered a comment and will be ignored.\n# The format is:\n# TAG = value [value, ...]\n# For lists, items can also be appended using:\n# TAG += value [value, ...]\n# Values that contain spaces should be placed between quotes (\\\" \\\").\n\n#---------------------------------------------------------------------------\n# Project related configuration options\n#---------------------------------------------------------------------------\n\n# This tag specifies the encoding used for all characters in the configuration\n# file that follow. The default is UTF-8 which is also the encoding used for all\n# text before the first occurrence of this tag. Doxygen uses libiconv (or the\n# iconv built into libc) for the transcoding. See\n# https://www.gnu.org/software/libiconv/ for the list of possible encodings.\n# The default value is: UTF-8.\n\nDOXYFILE_ENCODING      = UTF-8\n\n# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by\n# double-quotes, unless you are using Doxywizard) that should identify the\n# project for which the documentation is generated. This name is used in the\n# title of most generated pages and in a few other places.\n# The default value is: My Project.\n\nPROJECT_NAME           = \"NASDAQ ITCH50 Book Constructor\"\n\n# The PROJECT_NUMBER tag can be used to enter a project or revision number. This\n# could be handy for archiving the generated documentation or if some version\n# control system is used.\n\nPROJECT_NUMBER         =\n\n# Using the PROJECT_BRIEF tag one can provide an optional one line description\n# for a project that appears at the top of each page and should give viewer a\n# quick idea about the purpose of the project. Keep the description short.\n\nPROJECT_BRIEF          =\n\n# With the PROJECT_LOGO tag one can specify a logo or an icon that is included\n# in the documentation. The maximum height of the logo should not exceed 55\n# pixels and the maximum width should not exceed 200 pixels. Doxygen will copy\n# the logo to the output directory.\n\nPROJECT_LOGO           =\n\n# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path\n# into which the generated documentation will be written. If a relative path is\n# entered, it will be relative to the location where doxygen was started. If\n# left blank the current directory will be used.\n\nOUTPUT_DIRECTORY       =\n\n# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-\n# directories (in 2 levels) under the output directory of each output format and\n# will distribute the generated files over these directories. Enabling this\n# option can be useful when feeding doxygen a huge amount of source files, where\n# putting all generated files in the same directory would otherwise causes\n# performance problems for the file system.\n# The default value is: NO.\n\nCREATE_SUBDIRS         = NO\n\n# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII\n# characters to appear in the names of generated files. If set to NO, non-ASCII\n# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode\n# U+3044.\n# The default value is: NO.\n\nALLOW_UNICODE_NAMES    = NO\n\n# The OUTPUT_LANGUAGE tag is used to specify the language in which all\n# documentation generated by doxygen is written. Doxygen will use this\n# information to generate all constant output in the proper language.\n# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,\n# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),\n# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,\n# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),\n# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,\n# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,\n# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,\n# Ukrainian and Vietnamese.\n# The default value is: English.\n\nOUTPUT_LANGUAGE        = English\n\n# The OUTPUT_TEXT_DIRECTION tag is used to specify the direction in which all\n# documentation generated by doxygen is written. Doxygen will use this\n# information to generate all generated output in the proper direction.\n# Possible values are: None, LTR, RTL and Context.\n# The default value is: None.\n\nOUTPUT_TEXT_DIRECTION  = None\n\n# If the BRIEF_MEMBER_DESC tag is set to YES, doxygen will include brief member\n# descriptions after the members that are listed in the file and class\n# documentation (similar to Javadoc). Set to NO to disable this.\n# The default value is: YES.\n\nBRIEF_MEMBER_DESC      = YES\n\n# If the REPEAT_BRIEF tag is set to YES, doxygen will prepend the brief\n# description of a member or function before the detailed description\n#\n# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the\n# brief descriptions will be completely suppressed.\n# The default value is: YES.\n\nREPEAT_BRIEF           = YES\n\n# This tag implements a quasi-intelligent brief description abbreviator that is\n# used to form the text in various listings. Each string in this list, if found\n# as the leading text of the brief description, will be stripped from the text\n# and the result, after processing the whole list, is used as the annotated\n# text. Otherwise, the brief description is used as-is. If left blank, the\n# following values are used ($name is automatically replaced with the name of\n# the entity):The $name class, The $name widget, The $name file, is, provides,\n# specifies, contains, represents, a, an and the.\n\nABBREVIATE_BRIEF       = \"The $name class\" \\\n                         \"The $name widget\" \\\n                         \"The $name file\" \\\n                         is \\\n                         provides \\\n                         specifies \\\n                         contains \\\n                         represents \\\n                         a \\\n                         an \\\n                         the\n\n# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then\n# doxygen will generate a detailed section even if there is only a brief\n# description.\n# The default value is: NO.\n\nALWAYS_DETAILED_SEC    = NO\n\n# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all\n# inherited members of a class in the documentation of that class as if those\n# members were ordinary class members. Constructors, destructors and assignment\n# operators of the base classes will not be shown.\n# The default value is: NO.\n\nINLINE_INHERITED_MEMB  = NO\n\n# If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path\n# before files name in the file list and in the header files. If set to NO the\n# shortest path that makes the file name unique will be used\n# The default value is: YES.\n\nFULL_PATH_NAMES        = YES\n\n# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.\n# Stripping is only done if one of the specified strings matches the left-hand\n# part of the path. The tag can be used to show relative paths in the file list.\n# If left blank the directory from which doxygen is run is used as the path to\n# strip.\n#\n# Note that you can specify absolute paths here, but also relative paths, which\n# will be relative from the directory where doxygen is started.\n# This tag requires that the tag FULL_PATH_NAMES is set to YES.\n\nSTRIP_FROM_PATH        = ../\n\n# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the\n# path mentioned in the documentation of a class, which tells the reader which\n# header file to include in order to use a class. If left blank only the name of\n# the header file containing the class definition is used. Otherwise one should\n# specify the list of include paths that are normally passed to the compiler\n# using the -I flag.\n\nSTRIP_FROM_INC_PATH    =\n\n# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but\n# less readable) file names. This can be useful is your file systems doesn't\n# support long names like on DOS, Mac, or CD-ROM.\n# The default value is: NO.\n\nSHORT_NAMES            = NO\n\n# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the\n# first line (until the first dot) of a Javadoc-style comment as the brief\n# description. If set to NO, the Javadoc-style will behave just like regular Qt-\n# style comments (thus requiring an explicit @brief command for a brief\n# description.)\n# The default value is: NO.\n\nJAVADOC_AUTOBRIEF      = NO\n\n# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first\n# line (until the first dot) of a Qt-style comment as the brief description. If\n# set to NO, the Qt-style will behave just like regular Qt-style comments (thus\n# requiring an explicit \\brief command for a brief description.)\n# The default value is: NO.\n\nQT_AUTOBRIEF           = NO\n\n# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a\n# multi-line C++ special comment block (i.e. a block of //! or /// comments) as\n# a brief description. This used to be the default behavior. The new default is\n# to treat a multi-line C++ comment block as a detailed description. Set this\n# tag to YES if you prefer the old behavior instead.\n#\n# Note that setting this tag to YES also means that rational rose comments are\n# not recognized any more.\n# The default value is: NO.\n\nMULTILINE_CPP_IS_BRIEF = NO\n\n# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the\n# documentation from any documented member that it re-implements.\n# The default value is: YES.\n\nINHERIT_DOCS           = YES\n\n# If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new\n# page for each member. If set to NO, the documentation of a member will be part\n# of the file/class/namespace that contains it.\n# The default value is: NO.\n\nSEPARATE_MEMBER_PAGES  = NO\n\n# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen\n# uses this value to replace tabs by spaces in code fragments.\n# Minimum value: 1, maximum value: 16, default value: 4.\n\nTAB_SIZE               = 4\n\n# This tag can be used to specify a number of aliases that act as commands in\n# the documentation. An alias has the form:\n# name=value\n# For example adding\n# \"sideeffect=@par Side Effects:\\n\"\n# will allow you to put the command \\sideeffect (or @sideeffect) in the\n# documentation, which will result in a user-defined paragraph with heading\n# \"Side Effects:\". You can put \\n's in the value part of an alias to insert\n# newlines (in the resulting output). You can put ^^ in the value part of an\n# alias to insert a newline as if a physical newline was in the original file.\n# When you need a literal { or } or , in the value part of an alias you have to\n# escape them by means of a backslash (\\), this can lead to conflicts with the\n# commands \\{ and \\} for these it is advised to use the version @{ and @} or use\n# a double escape (\\\\{ and \\\\})\n\nALIASES                =\n\n# This tag can be used to specify a number of word-keyword mappings (TCL only).\n# A mapping has the form \"name=value\". For example adding \"class=itcl::class\"\n# will allow you to use the command class in the itcl::class meaning.\n\nTCL_SUBST              =\n\n# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources\n# only. Doxygen will then generate output that is more tailored for C. For\n# instance, some of the names that are used will be different. The list of all\n# members will be omitted, etc.\n# The default value is: NO.\n\nOPTIMIZE_OUTPUT_FOR_C  = NO\n\n# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or\n# Python sources only. Doxygen will then generate output that is more tailored\n# for that language. For instance, namespaces will be presented as packages,\n# qualified scopes will look different, etc.\n# The default value is: NO.\n\nOPTIMIZE_OUTPUT_JAVA   = NO\n\n# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran\n# sources. Doxygen will then generate output that is tailored for Fortran.\n# The default value is: NO.\n\nOPTIMIZE_FOR_FORTRAN   = NO\n\n# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL\n# sources. Doxygen will then generate output that is tailored for VHDL.\n# The default value is: NO.\n\nOPTIMIZE_OUTPUT_VHDL   = NO\n\n# Set the OPTIMIZE_OUTPUT_SLICE tag to YES if your project consists of Slice\n# sources only. Doxygen will then generate output that is more tailored for that\n# language. For instance, namespaces will be presented as modules, types will be\n# separated into more groups, etc.\n# The default value is: NO.\n\nOPTIMIZE_OUTPUT_SLICE  = NO\n\n# Doxygen selects the parser to use depending on the extension of the files it\n# parses. With this tag you can assign which parser to use for a given\n# extension. Doxygen has a built-in mapping, but you can override or extend it\n# using this tag. The format is ext=language, where ext is a file extension, and\n# language is one of the parsers supported by doxygen: IDL, Java, Javascript,\n# Csharp (C#), C, C++, D, PHP, md (Markdown), Objective-C, Python, Slice,\n# Fortran (fixed format Fortran: FortranFixed, free formatted Fortran:\n# FortranFree, unknown formatted Fortran: Fortran. In the later case the parser\n# tries to guess whether the code is fixed or free formatted code, this is the\n# default for Fortran type files), VHDL, tcl. For instance to make doxygen treat\n# .inc files as Fortran files (default is PHP), and .f files as C (default is\n# Fortran), use: inc=Fortran f=C.\n#\n# Note: For files without extension you can use no_extension as a placeholder.\n#\n# Note that for custom extensions you also need to set FILE_PATTERNS otherwise\n# the files are not read by doxygen.\n\nEXTENSION_MAPPING      =\n\n# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments\n# according to the Markdown format, which allows for more readable\n# documentation. See https://daringfireball.net/projects/markdown/ for details.\n# The output of markdown processing is further processed by doxygen, so you can\n# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in\n# case of backward compatibilities issues.\n# The default value is: YES.\n\nMARKDOWN_SUPPORT       = YES\n\n# When the TOC_INCLUDE_HEADINGS tag is set to a non-zero value, all headings up\n# to that level are automatically included in the table of contents, even if\n# they do not have an id attribute.\n# Note: This feature currently applies only to Markdown headings.\n# Minimum value: 0, maximum value: 99, default value: 0.\n# This tag requires that the tag MARKDOWN_SUPPORT is set to YES.\n\nTOC_INCLUDE_HEADINGS   = 0\n\n# When enabled doxygen tries to link words that correspond to documented\n# classes, or namespaces to their corresponding documentation. Such a link can\n# be prevented in individual cases by putting a % sign in front of the word or\n# globally by setting AUTOLINK_SUPPORT to NO.\n# The default value is: YES.\n\nAUTOLINK_SUPPORT       = YES\n\n# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want\n# to include (a tag file for) the STL sources as input, then you should set this\n# tag to YES in order to let doxygen match functions declarations and\n# definitions whose arguments contain STL classes (e.g. func(std::string);\n# versus func(std::string) {}). This also make the inheritance and collaboration\n# diagrams that involve STL classes more complete and accurate.\n# The default value is: NO.\n\nBUILTIN_STL_SUPPORT    = NO\n\n# If you use Microsoft's C++/CLI language, you should set this option to YES to\n# enable parsing support.\n# The default value is: NO.\n\nCPP_CLI_SUPPORT        = NO\n\n# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:\n# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen\n# will parse them like normal C++ but will assume all classes use public instead\n# of private inheritance when no explicit protection keyword is present.\n# The default value is: NO.\n\nSIP_SUPPORT            = NO\n\n# For Microsoft's IDL there are propget and propput attributes to indicate\n# getter and setter methods for a property. Setting this option to YES will make\n# doxygen to replace the get and set methods by a property in the documentation.\n# This will only work if the methods are indeed getting or setting a simple\n# type. If this is not the case, or you want to show the methods anyway, you\n# should set this option to NO.\n# The default value is: YES.\n\nIDL_PROPERTY_SUPPORT   = YES\n\n# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC\n# tag is set to YES then doxygen will reuse the documentation of the first\n# member in the group (if any) for the other members of the group. By default\n# all members of a group must be documented explicitly.\n# The default value is: NO.\n\nDISTRIBUTE_GROUP_DOC   = NO\n\n# If one adds a struct or class to a group and this option is enabled, then also\n# any nested class or struct is added to the same group. By default this option\n# is disabled and one has to add nested compounds explicitly via \\ingroup.\n# The default value is: NO.\n\nGROUP_NESTED_COMPOUNDS = NO\n\n# Set the SUBGROUPING tag to YES to allow class member groups of the same type\n# (for instance a group of public functions) to be put as a subgroup of that\n# type (e.g. under the Public Functions section). Set it to NO to prevent\n# subgrouping. Alternatively, this can be done per class using the\n# \\nosubgrouping command.\n# The default value is: YES.\n\nSUBGROUPING            = YES\n\n# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions\n# are shown inside the group in which they are included (e.g. using \\ingroup)\n# instead of on a separate page (for HTML and Man pages) or section (for LaTeX\n# and RTF).\n#\n# Note that this feature does not work in combination with\n# SEPARATE_MEMBER_PAGES.\n# The default value is: NO.\n\nINLINE_GROUPED_CLASSES = NO\n\n# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions\n# with only public data fields or simple typedef fields will be shown inline in\n# the documentation of the scope in which they are defined (i.e. file,\n# namespace, or group documentation), provided this scope is documented. If set\n# to NO, structs, classes, and unions are shown on a separate page (for HTML and\n# Man pages) or section (for LaTeX and RTF).\n# The default value is: NO.\n\nINLINE_SIMPLE_STRUCTS  = NO\n\n# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or\n# enum is documented as struct, union, or enum with the name of the typedef. So\n# typedef struct TypeS {} TypeT, will appear in the documentation as a struct\n# with name TypeT. When disabled the typedef will appear as a member of a file,\n# namespace, or class. And the struct will be named TypeS. This can typically be\n# useful for C code in case the coding convention dictates that all compound\n# types are typedef'ed and only the typedef is referenced, never the tag name.\n# The default value is: NO.\n\nTYPEDEF_HIDES_STRUCT   = NO\n\n# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This\n# cache is used to resolve symbols given their name and scope. Since this can be\n# an expensive process and often the same symbol appears multiple times in the\n# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small\n# doxygen will become slower. If the cache is too large, memory is wasted. The\n# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range\n# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536\n# symbols. At the end of a run doxygen will report the cache usage and suggest\n# the optimal cache size from a speed point of view.\n# Minimum value: 0, maximum value: 9, default value: 0.\n\nLOOKUP_CACHE_SIZE      = 0\n\n#---------------------------------------------------------------------------\n# Build related configuration options\n#---------------------------------------------------------------------------\n\n# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in\n# documentation are documented, even if no documentation was available. Private\n# class members and static file members will be hidden unless the\n# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.\n# Note: This will also disable the warnings about undocumented members that are\n# normally produced when WARNINGS is set to YES.\n# The default value is: NO.\n\nEXTRACT_ALL            = NO\n\n# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will\n# be included in the documentation.\n# The default value is: NO.\n\nEXTRACT_PRIVATE        = NO\n\n# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal\n# scope will be included in the documentation.\n# The default value is: NO.\n\nEXTRACT_PACKAGE        = NO\n\n# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be\n# included in the documentation.\n# The default value is: NO.\n\nEXTRACT_STATIC         = NO\n\n# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined\n# locally in source files will be included in the documentation. If set to NO,\n# only classes defined in header files are included. Does not have any effect\n# for Java sources.\n# The default value is: YES.\n\nEXTRACT_LOCAL_CLASSES  = YES\n\n# This flag is only useful for Objective-C code. If set to YES, local methods,\n# which are defined in the implementation section but not in the interface are\n# included in the documentation. If set to NO, only methods in the interface are\n# included.\n# The default value is: NO.\n\nEXTRACT_LOCAL_METHODS  = NO\n\n# If this flag is set to YES, the members of anonymous namespaces will be\n# extracted and appear in the documentation as a namespace called\n# 'anonymous_namespace{file}', where file will be replaced with the base name of\n# the file that contains the anonymous namespace. By default anonymous namespace\n# are hidden.\n# The default value is: NO.\n\nEXTRACT_ANON_NSPACES   = NO\n\n# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all\n# undocumented members inside documented classes or files. If set to NO these\n# members will be included in the various overviews, but no documentation\n# section is generated. This option has no effect if EXTRACT_ALL is enabled.\n# The default value is: NO.\n\nHIDE_UNDOC_MEMBERS     = NO\n\n# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all\n# undocumented classes that are normally visible in the class hierarchy. If set\n# to NO, these classes will be included in the various overviews. This option\n# has no effect if EXTRACT_ALL is enabled.\n# The default value is: NO.\n\nHIDE_UNDOC_CLASSES     = NO\n\n# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend\n# (class|struct|union) declarations. If set to NO, these declarations will be\n# included in the documentation.\n# The default value is: NO.\n\nHIDE_FRIEND_COMPOUNDS  = NO\n\n# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any\n# documentation blocks found inside the body of a function. If set to NO, these\n# blocks will be appended to the function's detailed documentation block.\n# The default value is: NO.\n\nHIDE_IN_BODY_DOCS      = NO\n\n# The INTERNAL_DOCS tag determines if documentation that is typed after a\n# \\internal command is included. If the tag is set to NO then the documentation\n# will be excluded. Set it to YES to include the internal documentation.\n# The default value is: NO.\n\nINTERNAL_DOCS          = NO\n\n# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file\n# names in lower-case letters. If set to YES, upper-case letters are also\n# allowed. This is useful if you have classes or files whose names only differ\n# in case and if your file system supports case sensitive file names. Windows\n# and Mac users are advised to set this option to NO.\n# The default value is: system dependent.\n\nCASE_SENSE_NAMES       = NO\n\n# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with\n# their full class and namespace scopes in the documentation. If set to YES, the\n# scope will be hidden.\n# The default value is: NO.\n\nHIDE_SCOPE_NAMES       = NO\n\n# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will\n# append additional text to a page's title, such as Class Reference. If set to\n# YES the compound reference will be hidden.\n# The default value is: NO.\n\nHIDE_COMPOUND_REFERENCE= NO\n\n# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of\n# the files that are included by a file in the documentation of that file.\n# The default value is: YES.\n\nSHOW_INCLUDE_FILES     = YES\n\n# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each\n# grouped member an include statement to the documentation, telling the reader\n# which file to include in order to use the member.\n# The default value is: NO.\n\nSHOW_GROUPED_MEMB_INC  = NO\n\n# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include\n# files with double quotes in the documentation rather than with sharp brackets.\n# The default value is: NO.\n\nFORCE_LOCAL_INCLUDES   = NO\n\n# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the\n# documentation for inline members.\n# The default value is: YES.\n\nINLINE_INFO            = YES\n\n# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the\n# (detailed) documentation of file and class members alphabetically by member\n# name. If set to NO, the members will appear in declaration order.\n# The default value is: YES.\n\nSORT_MEMBER_DOCS       = YES\n\n# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief\n# descriptions of file, namespace and class members alphabetically by member\n# name. If set to NO, the members will appear in declaration order. Note that\n# this will also influence the order of the classes in the class list.\n# The default value is: NO.\n\nSORT_BRIEF_DOCS        = NO\n\n# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the\n# (brief and detailed) documentation of class members so that constructors and\n# destructors are listed first. If set to NO the constructors will appear in the\n# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.\n# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief\n# member documentation.\n# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting\n# detailed member documentation.\n# The default value is: NO.\n\nSORT_MEMBERS_CTORS_1ST = NO\n\n# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy\n# of group names into alphabetical order. If set to NO the group names will\n# appear in their defined order.\n# The default value is: NO.\n\nSORT_GROUP_NAMES       = NO\n\n# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by\n# fully-qualified names, including namespaces. If set to NO, the class list will\n# be sorted only by class name, not including the namespace part.\n# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.\n# Note: This option applies only to the class list, not to the alphabetical\n# list.\n# The default value is: NO.\n\nSORT_BY_SCOPE_NAME     = NO\n\n# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper\n# type resolution of all parameters of a function it will reject a match between\n# the prototype and the implementation of a member function even if there is\n# only one candidate or it is obvious which candidate to choose by doing a\n# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still\n# accept a match between prototype and implementation in such cases.\n# The default value is: NO.\n\nSTRICT_PROTO_MATCHING  = NO\n\n# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo\n# list. This list is created by putting \\todo commands in the documentation.\n# The default value is: YES.\n\nGENERATE_TODOLIST      = YES\n\n# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test\n# list. This list is created by putting \\test commands in the documentation.\n# The default value is: YES.\n\nGENERATE_TESTLIST      = YES\n\n# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug\n# list. This list is created by putting \\bug commands in the documentation.\n# The default value is: YES.\n\nGENERATE_BUGLIST       = YES\n\n# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)\n# the deprecated list. This list is created by putting \\deprecated commands in\n# the documentation.\n# The default value is: YES.\n\nGENERATE_DEPRECATEDLIST= YES\n\n# The ENABLED_SECTIONS tag can be used to enable conditional documentation\n# sections, marked by \\if <section_label> ... \\endif and \\cond <section_label>\n# ... \\endcond blocks.\n\nENABLED_SECTIONS       =\n\n# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the\n# initial value of a variable or macro / define can have for it to appear in the\n# documentation. If the initializer consists of more lines than specified here\n# it will be hidden. Use a value of 0 to hide initializers completely. The\n# appearance of the value of individual variables and macros / defines can be\n# controlled using \\showinitializer or \\hideinitializer command in the\n# documentation regardless of this setting.\n# Minimum value: 0, maximum value: 10000, default value: 30.\n\nMAX_INITIALIZER_LINES  = 30\n\n# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at\n# the bottom of the documentation of classes and structs. If set to YES, the\n# list will mention the files that were used to generate the documentation.\n# The default value is: YES.\n\nSHOW_USED_FILES        = YES\n\n# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This\n# will remove the Files entry from the Quick Index and from the Folder Tree View\n# (if specified).\n# The default value is: YES.\n\nSHOW_FILES             = YES\n\n# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces\n# page. This will remove the Namespaces entry from the Quick Index and from the\n# Folder Tree View (if specified).\n# The default value is: YES.\n\nSHOW_NAMESPACES        = YES\n\n# The FILE_VERSION_FILTER tag can be used to specify a program or script that\n# doxygen should invoke to get the current version for each file (typically from\n# the version control system). Doxygen will invoke the program by executing (via\n# popen()) the command command input-file, where command is the value of the\n# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided\n# by doxygen. Whatever the program writes to standard output is used as the file\n# version. For an example see the documentation.\n\nFILE_VERSION_FILTER    =\n\n# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed\n# by doxygen. The layout file controls the global structure of the generated\n# output files in an output format independent way. To create the layout file\n# that represents doxygen's defaults, run doxygen with the -l option. You can\n# optionally specify a file name after the option, if omitted DoxygenLayout.xml\n# will be used as the name of the layout file.\n#\n# Note that if you run doxygen from a directory containing a file called\n# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE\n# tag is left empty.\n\nLAYOUT_FILE            =\n\n# The CITE_BIB_FILES tag can be used to specify one or more bib files containing\n# the reference definitions. This must be a list of .bib files. The .bib\n# extension is automatically appended if omitted. This requires the bibtex tool\n# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info.\n# For LaTeX the style of the bibliography can be controlled using\n# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the\n# search path. See also \\cite for info how to create references.\n\nCITE_BIB_FILES         =\n\n#---------------------------------------------------------------------------\n# Configuration options related to warning and progress messages\n#---------------------------------------------------------------------------\n\n# The QUIET tag can be used to turn on/off the messages that are generated to\n# standard output by doxygen. If QUIET is set to YES this implies that the\n# messages are off.\n# The default value is: NO.\n\nQUIET                  = NO\n\n# The WARNINGS tag can be used to turn on/off the warning messages that are\n# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES\n# this implies that the warnings are on.\n#\n# Tip: Turn warnings on while writing the documentation.\n# The default value is: YES.\n\nWARNINGS               = YES\n\n# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate\n# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag\n# will automatically be disabled.\n# The default value is: YES.\n\nWARN_IF_UNDOCUMENTED   = YES\n\n# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for\n# potential errors in the documentation, such as not documenting some parameters\n# in a documented function, or documenting parameters that don't exist or using\n# markup commands wrongly.\n# The default value is: YES.\n\nWARN_IF_DOC_ERROR      = YES\n\n# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that\n# are documented, but have no documentation for their parameters or return\n# value. If set to NO, doxygen will only warn about wrong or incomplete\n# parameter documentation, but not about the absence of documentation. If\n# EXTRACT_ALL is set to YES then this flag will automatically be disabled.\n# The default value is: NO.\n\nWARN_NO_PARAMDOC       = NO\n\n# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when\n# a warning is encountered.\n# The default value is: NO.\n\nWARN_AS_ERROR          = NO\n\n# The WARN_FORMAT tag determines the format of the warning messages that doxygen\n# can produce. The string should contain the $file, $line, and $text tags, which\n# will be replaced by the file and line number from which the warning originated\n# and the warning text. Optionally the format may contain $version, which will\n# be replaced by the version of the file (if it could be obtained via\n# FILE_VERSION_FILTER)\n# The default value is: $file:$line: $text.\n\nWARN_FORMAT            = \"$file:$line: $text\"\n\n# The WARN_LOGFILE tag can be used to specify a file to which warning and error\n# messages should be written. If left blank the output is written to standard\n# error (stderr).\n\nWARN_LOGFILE           =\n\n#---------------------------------------------------------------------------\n# Configuration options related to the input files\n#---------------------------------------------------------------------------\n\n# The INPUT tag is used to specify the files and/or directories that contain\n# documented source files. You may enter file names like myfile.cpp or\n# directories like /usr/src/myproject. Separate the files or directories with\n# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING\n# Note: If this tag is empty the current directory is searched.\n\nINPUT                  = ../src \\\n                         ../include\n\n# This tag can be used to specify the character encoding of the source files\n# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses\n# libiconv (or the iconv built into libc) for the transcoding. See the libiconv\n# documentation (see: https://www.gnu.org/software/libiconv/) for the list of\n# possible encodings.\n# The default value is: UTF-8.\n\nINPUT_ENCODING         = UTF-8\n\n# If the value of the INPUT tag contains directories, you can use the\n# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and\n# *.h) to filter out the source-files in the directories.\n#\n# Note that for custom extensions or not directly supported extensions you also\n# need to set EXTENSION_MAPPING for the extension otherwise the files are not\n# read by doxygen.\n#\n# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,\n# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,\n# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,\n# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f95, *.f03, *.f08,\n# *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf, *.qsf and *.ice.\n\nFILE_PATTERNS          = *.cpp \\\n                         *.hpp\n\n# The RECURSIVE tag can be used to specify whether or not subdirectories should\n# be searched for input files as well.\n# The default value is: NO.\n\nRECURSIVE              = NO\n\n# The EXCLUDE tag can be used to specify files and/or directories that should be\n# excluded from the INPUT source files. This way you can easily exclude a\n# subdirectory from a directory tree whose root is specified with the INPUT tag.\n#\n# Note that relative paths are relative to the directory from which doxygen is\n# run.\n\nEXCLUDE                =\n\n# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or\n# directories that are symbolic links (a Unix file system feature) are excluded\n# from the input.\n# The default value is: NO.\n\nEXCLUDE_SYMLINKS       = NO\n\n# If the value of the INPUT tag contains directories, you can use the\n# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude\n# certain files from those directories.\n#\n# Note that the wildcards are matched against the file with absolute path, so to\n# exclude all test directories for example use the pattern */test/*\n\nEXCLUDE_PATTERNS       =\n\n# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names\n# (namespaces, classes, functions, etc.) that should be excluded from the\n# output. The symbol name can be a fully qualified name, a word, or if the\n# wildcard * is used, a substring. Examples: ANamespace, AClass,\n# AClass::ANamespace, ANamespace::*Test\n#\n# Note that the wildcards are matched against the file with absolute path, so to\n# exclude all test directories use the pattern */test/*\n\nEXCLUDE_SYMBOLS        =\n\n# The EXAMPLE_PATH tag can be used to specify one or more files or directories\n# that contain example code fragments that are included (see the \\include\n# command).\n\nEXAMPLE_PATH           =\n\n# If the value of the EXAMPLE_PATH tag contains directories, you can use the\n# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and\n# *.h) to filter out the source-files in the directories. If left blank all\n# files are included.\n\nEXAMPLE_PATTERNS       = *\n\n# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be\n# searched for input files to be used with the \\include or \\dontinclude commands\n# irrespective of the value of the RECURSIVE tag.\n# The default value is: NO.\n\nEXAMPLE_RECURSIVE      = NO\n\n# The IMAGE_PATH tag can be used to specify one or more files or directories\n# that contain images that are to be included in the documentation (see the\n# \\image command).\n\nIMAGE_PATH             =\n\n# The INPUT_FILTER tag can be used to specify a program that doxygen should\n# invoke to filter for each input file. Doxygen will invoke the filter program\n# by executing (via popen()) the command:\n#\n# <filter> <input-file>\n#\n# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the\n# name of an input file. Doxygen will then use the output that the filter\n# program writes to standard output. If FILTER_PATTERNS is specified, this tag\n# will be ignored.\n#\n# Note that the filter must not add or remove lines; it is applied before the\n# code is scanned, but not when the output code is generated. If lines are added\n# or removed, the anchors will not be placed correctly.\n#\n# Note that for custom extensions or not directly supported extensions you also\n# need to set EXTENSION_MAPPING for the extension otherwise the files are not\n# properly processed by doxygen.\n\nINPUT_FILTER           =\n\n# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern\n# basis. Doxygen will compare the file name with each pattern and apply the\n# filter if there is a match. The filters are a list of the form: pattern=filter\n# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how\n# filters are used. If the FILTER_PATTERNS tag is empty or if none of the\n# patterns match the file name, INPUT_FILTER is applied.\n#\n# Note that for custom extensions or not directly supported extensions you also\n# need to set EXTENSION_MAPPING for the extension otherwise the files are not\n# properly processed by doxygen.\n\nFILTER_PATTERNS        =\n\n# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using\n# INPUT_FILTER) will also be used to filter the input files that are used for\n# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).\n# The default value is: NO.\n\nFILTER_SOURCE_FILES    = NO\n\n# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file\n# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and\n# it is also possible to disable source filtering for a specific pattern using\n# *.ext= (so without naming a filter).\n# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.\n\nFILTER_SOURCE_PATTERNS =\n\n# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that\n# is part of the input, its contents will be placed on the main page\n# (index.html). This can be useful if you have a project on for instance GitHub\n# and want to reuse the introduction page also for the doxygen output.\n\nUSE_MDFILE_AS_MAINPAGE =\n\n#---------------------------------------------------------------------------\n# Configuration options related to source browsing\n#---------------------------------------------------------------------------\n\n# If the SOURCE_BROWSER tag is set to YES then a list of source files will be\n# generated. Documented entities will be cross-referenced with these sources.\n#\n# Note: To get rid of all source code in the generated output, make sure that\n# also VERBATIM_HEADERS is set to NO.\n# The default value is: NO.\n\nSOURCE_BROWSER         = NO\n\n# Setting the INLINE_SOURCES tag to YES will include the body of functions,\n# classes and enums directly into the documentation.\n# The default value is: NO.\n\nINLINE_SOURCES         = NO\n\n# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any\n# special comment blocks from generated source code fragments. Normal C, C++ and\n# Fortran comments will always remain visible.\n# The default value is: YES.\n\nSTRIP_CODE_COMMENTS    = YES\n\n# If the REFERENCED_BY_RELATION tag is set to YES then for each documented\n# entity all documented functions referencing it will be listed.\n# The default value is: NO.\n\nREFERENCED_BY_RELATION = NO\n\n# If the REFERENCES_RELATION tag is set to YES then for each documented function\n# all documented entities called/used by that function will be listed.\n# The default value is: NO.\n\nREFERENCES_RELATION    = NO\n\n# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set\n# to YES then the hyperlinks from functions in REFERENCES_RELATION and\n# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will\n# link to the documentation.\n# The default value is: YES.\n\nREFERENCES_LINK_SOURCE = YES\n\n# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the\n# source code will show a tooltip with additional information such as prototype,\n# brief description and links to the definition and documentation. Since this\n# will make the HTML file larger and loading of large files a bit slower, you\n# can opt to disable this feature.\n# The default value is: YES.\n# This tag requires that the tag SOURCE_BROWSER is set to YES.\n\nSOURCE_TOOLTIPS        = YES\n\n# If the USE_HTAGS tag is set to YES then the references to source code will\n# point to the HTML generated by the htags(1) tool instead of doxygen built-in\n# source browser. The htags tool is part of GNU's global source tagging system\n# (see https://www.gnu.org/software/global/global.html). You will need version\n# 4.8.6 or higher.\n#\n# To use it do the following:\n# - Install the latest version of global\n# - Enable SOURCE_BROWSER and USE_HTAGS in the configuration file\n# - Make sure the INPUT points to the root of the source tree\n# - Run doxygen as normal\n#\n# Doxygen will invoke htags (and that will in turn invoke gtags), so these\n# tools must be available from the command line (i.e. in the search path).\n#\n# The result: instead of the source browser generated by doxygen, the links to\n# source code will now point to the output of htags.\n# The default value is: NO.\n# This tag requires that the tag SOURCE_BROWSER is set to YES.\n\nUSE_HTAGS              = NO\n\n# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a\n# verbatim copy of the header file for each class for which an include is\n# specified. Set to NO to disable this.\n# See also: Section \\class.\n# The default value is: YES.\n\nVERBATIM_HEADERS       = YES\n\n#---------------------------------------------------------------------------\n# Configuration options related to the alphabetical class index\n#---------------------------------------------------------------------------\n\n# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all\n# compounds will be generated. Enable this if the project contains a lot of\n# classes, structs, unions or interfaces.\n# The default value is: YES.\n\nALPHABETICAL_INDEX     = YES\n\n# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in\n# which the alphabetical index list will be split.\n# Minimum value: 1, maximum value: 20, default value: 5.\n# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.\n\nCOLS_IN_ALPHA_INDEX    = 5\n\n# In case all classes in a project start with a common prefix, all classes will\n# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag\n# can be used to specify a prefix (or a list of prefixes) that should be ignored\n# while generating the index headers.\n# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.\n\nIGNORE_PREFIX          =\n\n#---------------------------------------------------------------------------\n# Configuration options related to the HTML output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output\n# The default value is: YES.\n\nGENERATE_HTML          = NO\n\n# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it.\n# The default directory is: html.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_OUTPUT            = html\n\n# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each\n# generated HTML page (for example: .htm, .php, .asp).\n# The default value is: .html.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_FILE_EXTENSION    = .html\n\n# The HTML_HEADER tag can be used to specify a user-defined HTML header file for\n# each generated HTML page. If the tag is left blank doxygen will generate a\n# standard header.\n#\n# To get valid HTML the header file that includes any scripts and style sheets\n# that doxygen needs, which is dependent on the configuration options used (e.g.\n# the setting GENERATE_TREEVIEW). It is highly recommended to start with a\n# default header using\n# doxygen -w html new_header.html new_footer.html new_stylesheet.css\n# YourConfigFile\n# and then modify the file new_header.html. See also section \"Doxygen usage\"\n# for information on how to generate the default header that doxygen normally\n# uses.\n# Note: The header is subject to change so you typically have to regenerate the\n# default header when upgrading to a newer version of doxygen. For a description\n# of the possible markers and block names see the documentation.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_HEADER            =\n\n# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each\n# generated HTML page. If the tag is left blank doxygen will generate a standard\n# footer. See HTML_HEADER for more information on how to generate a default\n# footer and what special commands can be used inside the footer. See also\n# section \"Doxygen usage\" for information on how to generate the default footer\n# that doxygen normally uses.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_FOOTER            =\n\n# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style\n# sheet that is used by each HTML page. It can be used to fine-tune the look of\n# the HTML output. If left blank doxygen will generate a default style sheet.\n# See also section \"Doxygen usage\" for information on how to generate the style\n# sheet that doxygen normally uses.\n# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as\n# it is more robust and this tag (HTML_STYLESHEET) will in the future become\n# obsolete.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_STYLESHEET        =\n\n# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined\n# cascading style sheets that are included after the standard style sheets\n# created by doxygen. Using this option one can overrule certain style aspects.\n# This is preferred over using HTML_STYLESHEET since it does not replace the\n# standard style sheet and is therefore more robust against future updates.\n# Doxygen will copy the style sheet files to the output directory.\n# Note: The order of the extra style sheet files is of importance (e.g. the last\n# style sheet in the list overrules the setting of the previous ones in the\n# list). For an example see the documentation.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_EXTRA_STYLESHEET  =\n\n# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or\n# other source files which should be copied to the HTML output directory. Note\n# that these files will be copied to the base HTML output directory. Use the\n# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these\n# files. In the HTML_STYLESHEET file, use the file name only. Also note that the\n# files will be copied as-is; there are no commands or markers available.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_EXTRA_FILES       =\n\n# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen\n# will adjust the colors in the style sheet and background images according to\n# this color. Hue is specified as an angle on a colorwheel, see\n# https://en.wikipedia.org/wiki/Hue for more information. For instance the value\n# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300\n# purple, and 360 is red again.\n# Minimum value: 0, maximum value: 359, default value: 220.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_COLORSTYLE_HUE    = 220\n\n# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors\n# in the HTML output. For a value of 0 the output will use grayscales only. A\n# value of 255 will produce the most vivid colors.\n# Minimum value: 0, maximum value: 255, default value: 100.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_COLORSTYLE_SAT    = 100\n\n# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the\n# luminance component of the colors in the HTML output. Values below 100\n# gradually make the output lighter, whereas values above 100 make the output\n# darker. The value divided by 100 is the actual gamma applied, so 80 represents\n# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not\n# change the gamma.\n# Minimum value: 40, maximum value: 240, default value: 80.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_COLORSTYLE_GAMMA  = 80\n\n# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML\n# page will contain the date and time when the page was generated. Setting this\n# to YES can help to show when doxygen was last run and thus if the\n# documentation is up to date.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_TIMESTAMP         = NO\n\n# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML\n# documentation will contain a main index with vertical navigation menus that\n# are dynamically created via Javascript. If disabled, the navigation index will\n# consists of multiple levels of tabs that are statically embedded in every HTML\n# page. Disable this option to support browsers that do not have Javascript,\n# like the Qt help browser.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_DYNAMIC_MENUS     = YES\n\n# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML\n# documentation will contain sections that can be hidden and shown after the\n# page has loaded.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_DYNAMIC_SECTIONS  = NO\n\n# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries\n# shown in the various tree structured indices initially; the user can expand\n# and collapse entries dynamically later on. Doxygen will expand the tree to\n# such a level that at most the specified number of entries are visible (unless\n# a fully collapsed tree already exceeds this amount). So setting the number of\n# entries 1 will produce a full collapsed tree by default. 0 is a special value\n# representing an infinite number of entries and will result in a full expanded\n# tree by default.\n# Minimum value: 0, maximum value: 9999, default value: 100.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_INDEX_NUM_ENTRIES = 100\n\n# If the GENERATE_DOCSET tag is set to YES, additional index files will be\n# generated that can be used as input for Apple's Xcode 3 integrated development\n# environment (see: https://developer.apple.com/xcode/), introduced with OSX\n# 10.5 (Leopard). To create a documentation set, doxygen will generate a\n# Makefile in the HTML output directory. Running make will produce the docset in\n# that directory and running make install will install the docset in\n# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at\n# startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy\n# genXcode/_index.html for more information.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_DOCSET        = NO\n\n# This tag determines the name of the docset feed. A documentation feed provides\n# an umbrella under which multiple documentation sets from a single provider\n# (such as a company or product suite) can be grouped.\n# The default value is: Doxygen generated docs.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_FEEDNAME        = \"Doxygen generated docs\"\n\n# This tag specifies a string that should uniquely identify the documentation\n# set bundle. This should be a reverse domain-name style string, e.g.\n# com.mycompany.MyDocSet. Doxygen will append .docset to the name.\n# The default value is: org.doxygen.Project.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_BUNDLE_ID       = org.doxygen.Project\n\n# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify\n# the documentation publisher. This should be a reverse domain-name style\n# string, e.g. com.mycompany.MyDocSet.documentation.\n# The default value is: org.doxygen.Publisher.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_PUBLISHER_ID    = org.doxygen.Publisher\n\n# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.\n# The default value is: Publisher.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_PUBLISHER_NAME  = Publisher\n\n# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three\n# additional HTML index files: index.hhp, index.hhc, and index.hhk. The\n# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop\n# (see: https://www.microsoft.com/en-us/download/details.aspx?id=21138) on\n# Windows.\n#\n# The HTML Help Workshop contains a compiler that can convert all HTML output\n# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML\n# files are now used as the Windows 98 help format, and will replace the old\n# Windows help format (.hlp) on all Windows platforms in the future. Compressed\n# HTML files also contain an index, a table of contents, and you can search for\n# words in the documentation. The HTML workshop also contains a viewer for\n# compressed HTML files.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_HTMLHELP      = NO\n\n# The CHM_FILE tag can be used to specify the file name of the resulting .chm\n# file. You can add a path in front of the file if the result should not be\n# written to the html output directory.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nCHM_FILE               =\n\n# The HHC_LOCATION tag can be used to specify the location (absolute path\n# including file name) of the HTML help compiler (hhc.exe). If non-empty,\n# doxygen will try to run the HTML help compiler on the generated index.hhp.\n# The file has to be specified with full path.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nHHC_LOCATION           =\n\n# The GENERATE_CHI flag controls if a separate .chi index file is generated\n# (YES) or that it should be included in the master .chm file (NO).\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nGENERATE_CHI           = NO\n\n# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)\n# and project file content.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nCHM_INDEX_ENCODING     =\n\n# The BINARY_TOC flag controls whether a binary table of contents is generated\n# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it\n# enables the Previous and Next buttons.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nBINARY_TOC             = NO\n\n# The TOC_EXPAND flag can be set to YES to add extra items for group members to\n# the table of contents of the HTML help documentation and to the tree view.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nTOC_EXPAND             = NO\n\n# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and\n# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that\n# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help\n# (.qch) of the generated HTML documentation.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_QHP           = NO\n\n# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify\n# the file name of the resulting .qch file. The path specified is relative to\n# the HTML output folder.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQCH_FILE               =\n\n# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help\n# Project output. For more information please see Qt Help Project / Namespace\n# (see: http://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace).\n# The default value is: org.doxygen.Project.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_NAMESPACE          = org.doxygen.Project\n\n# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt\n# Help Project output. For more information please see Qt Help Project / Virtual\n# Folders (see: http://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-\n# folders).\n# The default value is: doc.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_VIRTUAL_FOLDER     = doc\n\n# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom\n# filter to add. For more information please see Qt Help Project / Custom\n# Filters (see: http://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-\n# filters).\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_CUST_FILTER_NAME   =\n\n# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the\n# custom filter to add. For more information please see Qt Help Project / Custom\n# Filters (see: http://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-\n# filters).\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_CUST_FILTER_ATTRS  =\n\n# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this\n# project's filter section matches. Qt Help Project / Filter Attributes (see:\n# http://doc.qt.io/archives/qt-4.8/qthelpproject.html#filter-attributes).\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_SECT_FILTER_ATTRS  =\n\n# The QHG_LOCATION tag can be used to specify the location of Qt's\n# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the\n# generated .qhp file.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHG_LOCATION           =\n\n# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be\n# generated, together with the HTML files, they form an Eclipse help plugin. To\n# install this plugin and make it available under the help contents menu in\n# Eclipse, the contents of the directory containing the HTML and XML files needs\n# to be copied into the plugins directory of eclipse. The name of the directory\n# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.\n# After copying Eclipse needs to be restarted before the help appears.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_ECLIPSEHELP   = NO\n\n# A unique identifier for the Eclipse help plugin. When installing the plugin\n# the directory name containing the HTML and XML files should also have this\n# name. Each documentation set should have its own identifier.\n# The default value is: org.doxygen.Project.\n# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.\n\nECLIPSE_DOC_ID         = org.doxygen.Project\n\n# If you want full control over the layout of the generated HTML pages it might\n# be necessary to disable the index and replace it with your own. The\n# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top\n# of each HTML page. A value of NO enables the index and the value YES disables\n# it. Since the tabs in the index contain the same information as the navigation\n# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nDISABLE_INDEX          = NO\n\n# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index\n# structure should be generated to display hierarchical information. If the tag\n# value is set to YES, a side panel will be generated containing a tree-like\n# index structure (just like the one that is generated for HTML Help). For this\n# to work a browser that supports JavaScript, DHTML, CSS and frames is required\n# (i.e. any modern browser). Windows users are probably better off using the\n# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can\n# further fine-tune the look of the index. As an example, the default style\n# sheet generated by doxygen has an example that shows how to put an image at\n# the root of the tree instead of the PROJECT_NAME. Since the tree basically has\n# the same information as the tab index, you could consider setting\n# DISABLE_INDEX to YES when enabling this option.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_TREEVIEW      = NO\n\n# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that\n# doxygen will group on one line in the generated HTML documentation.\n#\n# Note that a value of 0 will completely suppress the enum values from appearing\n# in the overview section.\n# Minimum value: 0, maximum value: 20, default value: 4.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nENUM_VALUES_PER_LINE   = 4\n\n# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used\n# to set the initial width (in pixels) of the frame in which the tree is shown.\n# Minimum value: 0, maximum value: 1500, default value: 250.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nTREEVIEW_WIDTH         = 250\n\n# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to\n# external symbols imported via tag files in a separate window.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nEXT_LINKS_IN_WINDOW    = NO\n\n# Use this tag to change the font size of LaTeX formulas included as images in\n# the HTML documentation. When you change the font size after a successful\n# doxygen run you need to manually remove any form_*.png images from the HTML\n# output directory to force them to be regenerated.\n# Minimum value: 8, maximum value: 50, default value: 10.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nFORMULA_FONTSIZE       = 10\n\n# Use the FORMULA_TRANSPARENT tag to determine whether or not the images\n# generated for formulas are transparent PNGs. Transparent PNGs are not\n# supported properly for IE 6.0, but are supported on all modern browsers.\n#\n# Note that when changing this option you need to delete any form_*.png files in\n# the HTML output directory before the changes have effect.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nFORMULA_TRANSPARENT    = YES\n\n# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see\n# https://www.mathjax.org) which uses client side Javascript for the rendering\n# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX\n# installed or if you want to formulas look prettier in the HTML output. When\n# enabled you may also need to install MathJax separately and configure the path\n# to it using the MATHJAX_RELPATH option.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nUSE_MATHJAX            = NO\n\n# When MathJax is enabled you can set the default output format to be used for\n# the MathJax output. See the MathJax site (see:\n# http://docs.mathjax.org/en/latest/output.html) for more details.\n# Possible values are: HTML-CSS (which is slower, but has the best\n# compatibility), NativeMML (i.e. MathML) and SVG.\n# The default value is: HTML-CSS.\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_FORMAT         = HTML-CSS\n\n# When MathJax is enabled you need to specify the location relative to the HTML\n# output directory using the MATHJAX_RELPATH option. The destination directory\n# should contain the MathJax.js script. For instance, if the mathjax directory\n# is located at the same level as the HTML output directory, then\n# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax\n# Content Delivery Network so you can quickly see the result without installing\n# MathJax. However, it is strongly recommended to install a local copy of\n# MathJax from https://www.mathjax.org before deployment.\n# The default value is: https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/.\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_RELPATH        = https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/\n\n# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax\n# extension names that should be enabled during MathJax rendering. For example\n# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_EXTENSIONS     =\n\n# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces\n# of code that will be used on startup of the MathJax code. See the MathJax site\n# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an\n# example see the documentation.\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_CODEFILE       =\n\n# When the SEARCHENGINE tag is enabled doxygen will generate a search box for\n# the HTML output. The underlying search engine uses javascript and DHTML and\n# should work on any modern browser. Note that when using HTML help\n# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)\n# there is already a search function so this one should typically be disabled.\n# For large projects the javascript based search engine can be slow, then\n# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to\n# search using the keyboard; to jump to the search box use <access key> + S\n# (what the <access key> is depends on the OS and browser, but it is typically\n# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down\n# key> to jump into the search results window, the results can be navigated\n# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel\n# the search. The filter options can be selected when the cursor is inside the\n# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>\n# to select a filter and <Enter> or <escape> to activate or cancel the filter\n# option.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nSEARCHENGINE           = YES\n\n# When the SERVER_BASED_SEARCH tag is enabled the search engine will be\n# implemented using a web server instead of a web client using Javascript. There\n# are two flavors of web server based searching depending on the EXTERNAL_SEARCH\n# setting. When disabled, doxygen will generate a PHP script for searching and\n# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing\n# and searching needs to be provided by external tools. See the section\n# \"External Indexing and Searching\" for details.\n# The default value is: NO.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nSERVER_BASED_SEARCH    = NO\n\n# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP\n# script for searching. Instead the search results are written to an XML file\n# which needs to be processed by an external indexer. Doxygen will invoke an\n# external search engine pointed to by the SEARCHENGINE_URL option to obtain the\n# search results.\n#\n# Doxygen ships with an example indexer (doxyindexer) and search engine\n# (doxysearch.cgi) which are based on the open source search engine library\n# Xapian (see: https://xapian.org/).\n#\n# See the section \"External Indexing and Searching\" for details.\n# The default value is: NO.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nEXTERNAL_SEARCH        = NO\n\n# The SEARCHENGINE_URL should point to a search engine hosted by a web server\n# which will return the search results when EXTERNAL_SEARCH is enabled.\n#\n# Doxygen ships with an example indexer (doxyindexer) and search engine\n# (doxysearch.cgi) which are based on the open source search engine library\n# Xapian (see: https://xapian.org/). See the section \"External Indexing and\n# Searching\" for details.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nSEARCHENGINE_URL       =\n\n# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed\n# search data is written to a file for indexing by an external tool. With the\n# SEARCHDATA_FILE tag the name of this file can be specified.\n# The default file is: searchdata.xml.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nSEARCHDATA_FILE        = searchdata.xml\n\n# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the\n# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is\n# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple\n# projects and redirect the results back to the right project.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nEXTERNAL_SEARCH_ID     =\n\n# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen\n# projects other than the one defined by this configuration file, but that are\n# all added to the same external search index. Each project needs to have a\n# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of\n# to a relative location where the documentation can be found. The format is:\n# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nEXTRA_SEARCH_MAPPINGS  =\n\n#---------------------------------------------------------------------------\n# Configuration options related to the LaTeX output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.\n# The default value is: YES.\n\nGENERATE_LATEX         = YES\n\n# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it.\n# The default directory is: latex.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_OUTPUT           = latex\n\n# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be\n# invoked.\n#\n# Note that when not enabling USE_PDFLATEX the default is latex when enabling\n# USE_PDFLATEX the default is pdflatex and when in the later case latex is\n# chosen this is overwritten by pdflatex. For specific output languages the\n# default can have been set differently, this depends on the implementation of\n# the output language.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_CMD_NAME         =\n\n# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate\n# index for LaTeX.\n# Note: This tag is used in the Makefile / make.bat.\n# See also: LATEX_MAKEINDEX_CMD for the part in the generated output file\n# (.tex).\n# The default file is: makeindex.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nMAKEINDEX_CMD_NAME     = makeindex\n\n# The LATEX_MAKEINDEX_CMD tag can be used to specify the command name to\n# generate index for LaTeX. In case there is no backslash (\\) as first character\n# it will be automatically added in the LaTeX code.\n# Note: This tag is used in the generated output file (.tex).\n# See also: MAKEINDEX_CMD_NAME for the part in the Makefile / make.bat.\n# The default value is: makeindex.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_MAKEINDEX_CMD    = makeindex\n\n# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX\n# documents. This may be useful for small projects and may help to save some\n# trees in general.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nCOMPACT_LATEX          = NO\n\n# The PAPER_TYPE tag can be used to set the paper type that is used by the\n# printer.\n# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x\n# 14 inches) and executive (7.25 x 10.5 inches).\n# The default value is: a4.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nPAPER_TYPE             = a4\n\n# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names\n# that should be included in the LaTeX output. The package can be specified just\n# by its name or with the correct syntax as to be used with the LaTeX\n# \\usepackage command. To get the times font for instance you can specify :\n# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}\n# To use the option intlimits with the amsmath package you can specify:\n# EXTRA_PACKAGES=[intlimits]{amsmath}\n# If left blank no extra packages will be included.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nEXTRA_PACKAGES         =\n\n# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the\n# generated LaTeX document. The header should contain everything until the first\n# chapter. If it is left blank doxygen will generate a standard header. See\n# section \"Doxygen usage\" for information on how to let doxygen write the\n# default header to a separate file.\n#\n# Note: Only use a user-defined header if you know what you are doing! The\n# following commands have a special meaning inside the header: $title,\n# $datetime, $date, $doxygenversion, $projectname, $projectnumber,\n# $projectbrief, $projectlogo. Doxygen will replace $title with the empty\n# string, for the replacement values of the other commands the user is referred\n# to HTML_HEADER.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_HEADER           =\n\n# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the\n# generated LaTeX document. The footer should contain everything after the last\n# chapter. If it is left blank doxygen will generate a standard footer. See\n# LATEX_HEADER for more information on how to generate a default footer and what\n# special commands can be used inside the footer.\n#\n# Note: Only use a user-defined footer if you know what you are doing!\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_FOOTER           =\n\n# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined\n# LaTeX style sheets that are included after the standard style sheets created\n# by doxygen. Using this option one can overrule certain style aspects. Doxygen\n# will copy the style sheet files to the output directory.\n# Note: The order of the extra style sheet files is of importance (e.g. the last\n# style sheet in the list overrules the setting of the previous ones in the\n# list).\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_EXTRA_STYLESHEET =\n\n# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or\n# other source files which should be copied to the LATEX_OUTPUT output\n# directory. Note that the files will be copied as-is; there are no commands or\n# markers available.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_EXTRA_FILES      =\n\n# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is\n# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will\n# contain links (just like the HTML output) instead of page references. This\n# makes the output suitable for online browsing using a PDF viewer.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nPDF_HYPERLINKS         = YES\n\n# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate\n# the PDF file directly from the LaTeX files. Set this option to YES, to get a\n# higher quality PDF documentation.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nUSE_PDFLATEX           = YES\n\n# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode\n# command to the generated LaTeX files. This will instruct LaTeX to keep running\n# if errors occur, instead of asking the user for help. This option is also used\n# when generating formulas in HTML.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_BATCHMODE        = NO\n\n# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the\n# index chapters (such as File Index, Compound Index, etc.) in the output.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_HIDE_INDICES     = NO\n\n# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source\n# code with syntax highlighting in the LaTeX output.\n#\n# Note that which sources are shown also depends on other settings such as\n# SOURCE_BROWSER.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_SOURCE_CODE      = NO\n\n# The LATEX_BIB_STYLE tag can be used to specify the style to use for the\n# bibliography, e.g. plainnat, or ieeetr. See\n# https://en.wikipedia.org/wiki/BibTeX and \\cite for more info.\n# The default value is: plain.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_BIB_STYLE        = plain\n\n# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated\n# page will contain the date and time when the page was generated. Setting this\n# to NO can help when comparing the output of multiple runs.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_TIMESTAMP        = NO\n\n# The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute)\n# path from which the emoji images will be read. If a relative path is entered,\n# it will be relative to the LATEX_OUTPUT directory. If left blank the\n# LATEX_OUTPUT directory will be used.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_EMOJI_DIRECTORY  =\n\n#---------------------------------------------------------------------------\n# Configuration options related to the RTF output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The\n# RTF output is optimized for Word 97 and may not look too pretty with other RTF\n# readers/editors.\n# The default value is: NO.\n\nGENERATE_RTF           = NO\n\n# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it.\n# The default directory is: rtf.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_OUTPUT             = rtf\n\n# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF\n# documents. This may be useful for small projects and may help to save some\n# trees in general.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nCOMPACT_RTF            = NO\n\n# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will\n# contain hyperlink fields. The RTF file will contain links (just like the HTML\n# output) instead of page references. This makes the output suitable for online\n# browsing using Word or some other Word compatible readers that support those\n# fields.\n#\n# Note: WordPad (write) and others do not support links.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_HYPERLINKS         = NO\n\n# Load stylesheet definitions from file. Syntax is similar to doxygen's\n# configuration file, i.e. a series of assignments. You only have to provide\n# replacements, missing definitions are set to their default value.\n#\n# See also section \"Doxygen usage\" for information on how to generate the\n# default style sheet that doxygen normally uses.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_STYLESHEET_FILE    =\n\n# Set optional variables used in the generation of an RTF document. Syntax is\n# similar to doxygen's configuration file. A template extensions file can be\n# generated using doxygen -e rtf extensionFile.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_EXTENSIONS_FILE    =\n\n# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code\n# with syntax highlighting in the RTF output.\n#\n# Note that which sources are shown also depends on other settings such as\n# SOURCE_BROWSER.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_SOURCE_CODE        = NO\n\n#---------------------------------------------------------------------------\n# Configuration options related to the man page output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for\n# classes and files.\n# The default value is: NO.\n\nGENERATE_MAN           = NO\n\n# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it. A directory man3 will be created inside the directory specified by\n# MAN_OUTPUT.\n# The default directory is: man.\n# This tag requires that the tag GENERATE_MAN is set to YES.\n\nMAN_OUTPUT             = man\n\n# The MAN_EXTENSION tag determines the extension that is added to the generated\n# man pages. In case the manual section does not start with a number, the number\n# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is\n# optional.\n# The default value is: .3.\n# This tag requires that the tag GENERATE_MAN is set to YES.\n\nMAN_EXTENSION          = .3\n\n# The MAN_SUBDIR tag determines the name of the directory created within\n# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by\n# MAN_EXTENSION with the initial . removed.\n# This tag requires that the tag GENERATE_MAN is set to YES.\n\nMAN_SUBDIR             =\n\n# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it\n# will generate one additional man file for each entity documented in the real\n# man page(s). These additional files only source the real man page, but without\n# them the man command would be unable to find the correct page.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_MAN is set to YES.\n\nMAN_LINKS              = NO\n\n#---------------------------------------------------------------------------\n# Configuration options related to the XML output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that\n# captures the structure of the code including all documentation.\n# The default value is: NO.\n\nGENERATE_XML           = NO\n\n# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it.\n# The default directory is: xml.\n# This tag requires that the tag GENERATE_XML is set to YES.\n\nXML_OUTPUT             = xml\n\n# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program\n# listings (including syntax highlighting and cross-referencing information) to\n# the XML output. Note that enabling this will significantly increase the size\n# of the XML output.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_XML is set to YES.\n\nXML_PROGRAMLISTING     = YES\n\n# If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, doxygen will include\n# namespace members in file scope as well, matching the HTML output.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_XML is set to YES.\n\nXML_NS_MEMB_FILE_SCOPE = NO\n\n#---------------------------------------------------------------------------\n# Configuration options related to the DOCBOOK output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files\n# that can be used to generate PDF.\n# The default value is: NO.\n\nGENERATE_DOCBOOK       = NO\n\n# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.\n# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in\n# front of it.\n# The default directory is: docbook.\n# This tag requires that the tag GENERATE_DOCBOOK is set to YES.\n\nDOCBOOK_OUTPUT         = docbook\n\n# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the\n# program listings (including syntax highlighting and cross-referencing\n# information) to the DOCBOOK output. Note that enabling this will significantly\n# increase the size of the DOCBOOK output.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_DOCBOOK is set to YES.\n\nDOCBOOK_PROGRAMLISTING = NO\n\n#---------------------------------------------------------------------------\n# Configuration options for the AutoGen Definitions output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an\n# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures\n# the structure of the code including all documentation. Note that this feature\n# is still experimental and incomplete at the moment.\n# The default value is: NO.\n\nGENERATE_AUTOGEN_DEF   = NO\n\n#---------------------------------------------------------------------------\n# Configuration options related to the Perl module output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module\n# file that captures the structure of the code including all documentation.\n#\n# Note that this feature is still experimental and incomplete at the moment.\n# The default value is: NO.\n\nGENERATE_PERLMOD       = NO\n\n# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary\n# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI\n# output from the Perl module output.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_PERLMOD is set to YES.\n\nPERLMOD_LATEX          = NO\n\n# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely\n# formatted so it can be parsed by a human reader. This is useful if you want to\n# understand what is going on. On the other hand, if this tag is set to NO, the\n# size of the Perl module output will be much smaller and Perl will parse it\n# just the same.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_PERLMOD is set to YES.\n\nPERLMOD_PRETTY         = YES\n\n# The names of the make variables in the generated doxyrules.make file are\n# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful\n# so different doxyrules.make files included by the same Makefile don't\n# overwrite each other's variables.\n# This tag requires that the tag GENERATE_PERLMOD is set to YES.\n\nPERLMOD_MAKEVAR_PREFIX =\n\n#---------------------------------------------------------------------------\n# Configuration options related to the preprocessor\n#---------------------------------------------------------------------------\n\n# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all\n# C-preprocessor directives found in the sources and include files.\n# The default value is: YES.\n\nENABLE_PREPROCESSING   = YES\n\n# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names\n# in the source code. If set to NO, only conditional compilation will be\n# performed. Macro expansion can be done in a controlled way by setting\n# EXPAND_ONLY_PREDEF to YES.\n# The default value is: NO.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nMACRO_EXPANSION        = NO\n\n# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then\n# the macro expansion is limited to the macros specified with the PREDEFINED and\n# EXPAND_AS_DEFINED tags.\n# The default value is: NO.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nEXPAND_ONLY_PREDEF     = NO\n\n# If the SEARCH_INCLUDES tag is set to YES, the include files in the\n# INCLUDE_PATH will be searched if a #include is found.\n# The default value is: YES.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nSEARCH_INCLUDES        = YES\n\n# The INCLUDE_PATH tag can be used to specify one or more directories that\n# contain include files that are not input files but should be processed by the\n# preprocessor.\n# This tag requires that the tag SEARCH_INCLUDES is set to YES.\n\nINCLUDE_PATH           =\n\n# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard\n# patterns (like *.h and *.hpp) to filter out the header-files in the\n# directories. If left blank, the patterns specified with FILE_PATTERNS will be\n# used.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nINCLUDE_FILE_PATTERNS  =\n\n# The PREDEFINED tag can be used to specify one or more macro names that are\n# defined before the preprocessor is started (similar to the -D option of e.g.\n# gcc). The argument of the tag is a list of macros of the form: name or\n# name=definition (no spaces). If the definition and the \"=\" are omitted, \"=1\"\n# is assumed. To prevent a macro definition from being undefined via #undef or\n# recursively expanded use the := operator instead of the = operator.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nPREDEFINED             =\n\n# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this\n# tag can be used to specify a list of macro names that should be expanded. The\n# macro definition that is found in the sources will be used. Use the PREDEFINED\n# tag if you want to use a different macro definition that overrules the\n# definition found in the source code.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nEXPAND_AS_DEFINED      =\n\n# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will\n# remove all references to function-like macros that are alone on a line, have\n# an all uppercase name, and do not end with a semicolon. Such function macros\n# are typically used for boiler-plate code, and will confuse the parser if not\n# removed.\n# The default value is: YES.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nSKIP_FUNCTION_MACROS   = YES\n\n#---------------------------------------------------------------------------\n# Configuration options related to external references\n#---------------------------------------------------------------------------\n\n# The TAGFILES tag can be used to specify one or more tag files. For each tag\n# file the location of the external documentation should be added. The format of\n# a tag file without this location is as follows:\n# TAGFILES = file1 file2 ...\n# Adding location for the tag files is done as follows:\n# TAGFILES = file1=loc1 \"file2 = loc2\" ...\n# where loc1 and loc2 can be relative or absolute paths or URLs. See the\n# section \"Linking to external documentation\" for more information about the use\n# of tag files.\n# Note: Each tag file must have a unique name (where the name does NOT include\n# the path). If a tag file is not located in the directory in which doxygen is\n# run, you must also specify the path to the tagfile here.\n\nTAGFILES               =\n\n# When a file name is specified after GENERATE_TAGFILE, doxygen will create a\n# tag file that is based on the input files it reads. See section \"Linking to\n# external documentation\" for more information about the usage of tag files.\n\nGENERATE_TAGFILE       =\n\n# If the ALLEXTERNALS tag is set to YES, all external class will be listed in\n# the class index. If set to NO, only the inherited external classes will be\n# listed.\n# The default value is: NO.\n\nALLEXTERNALS           = NO\n\n# If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed\n# in the modules index. If set to NO, only the current project's groups will be\n# listed.\n# The default value is: YES.\n\nEXTERNAL_GROUPS        = YES\n\n# If the EXTERNAL_PAGES tag is set to YES, all external pages will be listed in\n# the related pages index. If set to NO, only the current project's pages will\n# be listed.\n# The default value is: YES.\n\nEXTERNAL_PAGES         = YES\n\n# The PERL_PATH should be the absolute path and name of the perl script\n# interpreter (i.e. the result of 'which perl').\n# The default file (with absolute path) is: /usr/bin/perl.\n\nPERL_PATH              = /usr/bin/perl\n\n#---------------------------------------------------------------------------\n# Configuration options related to the dot tool\n#---------------------------------------------------------------------------\n\n# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram\n# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to\n# NO turns the diagrams off. Note that this option also works with HAVE_DOT\n# disabled, but it is recommended to install and use dot, since it yields more\n# powerful graphs.\n# The default value is: YES.\n\nCLASS_DIAGRAMS         = YES\n\n# You can define message sequence charts within doxygen comments using the \\msc\n# command. Doxygen will then run the mscgen tool (see:\n# http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the\n# documentation. The MSCGEN_PATH tag allows you to specify the directory where\n# the mscgen tool resides. If left empty the tool is assumed to be found in the\n# default search path.\n\nMSCGEN_PATH            =\n\n# You can include diagrams made with dia in doxygen documentation. Doxygen will\n# then run dia to produce the diagram and insert it in the documentation. The\n# DIA_PATH tag allows you to specify the directory where the dia binary resides.\n# If left empty dia is assumed to be found in the default search path.\n\nDIA_PATH               =\n\n# If set to YES the inheritance and collaboration graphs will hide inheritance\n# and usage relations if the target is undocumented or is not a class.\n# The default value is: YES.\n\nHIDE_UNDOC_RELATIONS   = YES\n\n# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is\n# available from the path. This tool is part of Graphviz (see:\n# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent\n# Bell Labs. The other options in this section have no effect if this option is\n# set to NO\n# The default value is: NO.\n\nHAVE_DOT               = NO\n\n# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed\n# to run in parallel. When set to 0 doxygen will base this on the number of\n# processors available in the system. You can set it explicitly to a value\n# larger than 0 to get control over the balance between CPU load and processing\n# speed.\n# Minimum value: 0, maximum value: 32, default value: 0.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_NUM_THREADS        = 0\n\n# When you want a differently looking font in the dot files that doxygen\n# generates you can specify the font name using DOT_FONTNAME. You need to make\n# sure dot is able to find the font, which can be done by putting it in a\n# standard location or by setting the DOTFONTPATH environment variable or by\n# setting DOT_FONTPATH to the directory containing the font.\n# The default value is: Helvetica.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_FONTNAME           = Helvetica\n\n# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of\n# dot graphs.\n# Minimum value: 4, maximum value: 24, default value: 10.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_FONTSIZE           = 10\n\n# By default doxygen will tell dot to use the default font as specified with\n# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set\n# the path where dot can find it using this tag.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_FONTPATH           =\n\n# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for\n# each documented class showing the direct and indirect inheritance relations.\n# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.\n# The default value is: YES.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nCLASS_GRAPH            = YES\n\n# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a\n# graph for each documented class showing the direct and indirect implementation\n# dependencies (inheritance, containment, and class references variables) of the\n# class with other documented classes.\n# The default value is: YES.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nCOLLABORATION_GRAPH    = YES\n\n# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for\n# groups, showing the direct groups dependencies.\n# The default value is: YES.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nGROUP_GRAPHS           = YES\n\n# If the UML_LOOK tag is set to YES, doxygen will generate inheritance and\n# collaboration diagrams in a style similar to the OMG's Unified Modeling\n# Language.\n# The default value is: NO.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nUML_LOOK               = NO\n\n# If the UML_LOOK tag is enabled, the fields and methods are shown inside the\n# class node. If there are many fields or methods and many nodes the graph may\n# become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the\n# number of items for each type to make the size more manageable. Set this to 0\n# for no limit. Note that the threshold may be exceeded by 50% before the limit\n# is enforced. So when you set the threshold to 10, up to 15 fields may appear,\n# but if the number exceeds 15, the total amount of fields shown is limited to\n# 10.\n# Minimum value: 0, maximum value: 100, default value: 10.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nUML_LIMIT_NUM_FIELDS   = 10\n\n# If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and\n# collaboration graphs will show the relations between templates and their\n# instances.\n# The default value is: NO.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nTEMPLATE_RELATIONS     = NO\n\n# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to\n# YES then doxygen will generate a graph for each documented file showing the\n# direct and indirect include dependencies of the file with other documented\n# files.\n# The default value is: YES.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nINCLUDE_GRAPH          = YES\n\n# If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are\n# set to YES then doxygen will generate a graph for each documented file showing\n# the direct and indirect include dependencies of the file with other documented\n# files.\n# The default value is: YES.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nINCLUDED_BY_GRAPH      = YES\n\n# If the CALL_GRAPH tag is set to YES then doxygen will generate a call\n# dependency graph for every global function or class method.\n#\n# Note that enabling this option will significantly increase the time of a run.\n# So in most cases it will be better to enable call graphs for selected\n# functions only using the \\callgraph command. Disabling a call graph can be\n# accomplished by means of the command \\hidecallgraph.\n# The default value is: NO.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nCALL_GRAPH             = NO\n\n# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller\n# dependency graph for every global function or class method.\n#\n# Note that enabling this option will significantly increase the time of a run.\n# So in most cases it will be better to enable caller graphs for selected\n# functions only using the \\callergraph command. Disabling a caller graph can be\n# accomplished by means of the command \\hidecallergraph.\n# The default value is: NO.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nCALLER_GRAPH           = NO\n\n# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical\n# hierarchy of all classes instead of a textual one.\n# The default value is: YES.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nGRAPHICAL_HIERARCHY    = YES\n\n# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the\n# dependencies a directory has on other directories in a graphical way. The\n# dependency relations are determined by the #include relations between the\n# files in the directories.\n# The default value is: YES.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDIRECTORY_GRAPH        = YES\n\n# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images\n# generated by dot. For an explanation of the image formats see the section\n# output formats in the documentation of the dot tool (Graphviz (see:\n# http://www.graphviz.org/)).\n# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order\n# to make the SVG files visible in IE 9+ (other browsers do not have this\n# requirement).\n# Possible values are: png, jpg, gif, svg, png:gd, png:gd:gd, png:cairo,\n# png:cairo:gd, png:cairo:cairo, png:cairo:gdiplus, png:gdiplus and\n# png:gdiplus:gdiplus.\n# The default value is: png.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_IMAGE_FORMAT       = png\n\n# If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to\n# enable generation of interactive SVG images that allow zooming and panning.\n#\n# Note that this requires a modern browser other than Internet Explorer. Tested\n# and working are Firefox, Chrome, Safari, and Opera.\n# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make\n# the SVG files visible. Older versions of IE do not have SVG support.\n# The default value is: NO.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nINTERACTIVE_SVG        = NO\n\n# The DOT_PATH tag can be used to specify the path where the dot tool can be\n# found. If left blank, it is assumed the dot tool can be found in the path.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_PATH               =\n\n# The DOTFILE_DIRS tag can be used to specify one or more directories that\n# contain dot files that are included in the documentation (see the \\dotfile\n# command).\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOTFILE_DIRS           =\n\n# The MSCFILE_DIRS tag can be used to specify one or more directories that\n# contain msc files that are included in the documentation (see the \\mscfile\n# command).\n\nMSCFILE_DIRS           =\n\n# The DIAFILE_DIRS tag can be used to specify one or more directories that\n# contain dia files that are included in the documentation (see the \\diafile\n# command).\n\nDIAFILE_DIRS           =\n\n# When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the\n# path where java can find the plantuml.jar file. If left blank, it is assumed\n# PlantUML is not used or called during a preprocessing step. Doxygen will\n# generate a warning when it encounters a \\startuml command in this case and\n# will not generate output for the diagram.\n\nPLANTUML_JAR_PATH      =\n\n# When using plantuml, the PLANTUML_CFG_FILE tag can be used to specify a\n# configuration file for plantuml.\n\nPLANTUML_CFG_FILE      =\n\n# When using plantuml, the specified paths are searched for files specified by\n# the !include statement in a plantuml block.\n\nPLANTUML_INCLUDE_PATH  =\n\n# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes\n# that will be shown in the graph. If the number of nodes in a graph becomes\n# larger than this value, doxygen will truncate the graph, which is visualized\n# by representing a node as a red box. Note that doxygen if the number of direct\n# children of the root node in a graph is already larger than\n# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note that\n# the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.\n# Minimum value: 0, maximum value: 10000, default value: 50.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_GRAPH_MAX_NODES    = 50\n\n# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the graphs\n# generated by dot. A depth value of 3 means that only nodes reachable from the\n# root by following a path via at most 3 edges will be shown. Nodes that lay\n# further from the root node will be omitted. Note that setting this option to 1\n# or 2 may greatly reduce the computation time needed for large code bases. Also\n# note that the size of a graph can be further restricted by\n# DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction.\n# Minimum value: 0, maximum value: 1000, default value: 0.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nMAX_DOT_GRAPH_DEPTH    = 0\n\n# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent\n# background. This is disabled by default, because dot on Windows does not seem\n# to support this out of the box.\n#\n# Warning: Depending on the platform used, enabling this option may lead to\n# badly anti-aliased labels on the edges of a graph (i.e. they become hard to\n# read).\n# The default value is: NO.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_TRANSPARENT        = NO\n\n# Set the DOT_MULTI_TARGETS tag to YES to allow dot to generate multiple output\n# files in one run (i.e. multiple -o and -T options on the command line). This\n# makes dot run faster, but since only newer versions of dot (>1.8.10) support\n# this, this feature is disabled by default.\n# The default value is: NO.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_MULTI_TARGETS      = NO\n\n# If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page\n# explaining the meaning of the various boxes and arrows in the dot generated\n# graphs.\n# The default value is: YES.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nGENERATE_LEGEND        = YES\n\n# If the DOT_CLEANUP tag is set to YES, doxygen will remove the intermediate dot\n# files that are used to generate the various graphs.\n# The default value is: YES.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_CLEANUP            = YES\n"
  },
  {
    "path": "examples/.ipynb_checkpoints/AutoregressionOfBuySell-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import environment\\n\",\n    \"import utility\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import stats\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Autocorrelation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def autocorr(x,lags):\\n\",\n    \"    '''function to compute correlation of x for lags'''\\n\",\n    \"\\n\",\n    \"    corr=[1. if l==0 else np.corrcoef(x[l:],x[:-l])[0][1] for l in lags]\\n\",\n    \"    return np.array(corr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tmin=9.5*1e9*60*60\\n\",\n    \"tmax=16.5*1e9*60*60\\n\",\n    \"lags=range(20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Data Selection: some big cap/liquid stocks and some medium cap.\\n\",\n    \"Algorithmic execution slipts large clients order in small ones, hence we expect large autocorr\\n\",\n    \"The splitting is less common in med-cap stocks, hence we expect weakest autocorr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#symbols = ([\\\"AAPL\\\",\\\"ARQL\\\",\\\"CSCO\\\",\\\"QQQ\\\",\\\"FB\\\",\\\"INTC\\\",\\\"QCOM\\\",\\\"NVDA\\\",\\\"CMCSA\\\",\\\"AMD\\\",\\\"EEM\\\",\\\"SPY\\\",\\\"GDX\\\",\\\"MSFT\\\",\\\"AMZN\\\"])\\n\",\n    \"#symbols.sort()\\n\",\n    \"#dates = ['01302019','12282018','05302019','05302018','03272019']\\n\",\n    \"symbols = [\\\"AAPL\\\",\\\"RARE\\\",\\\"MSFT\\\",\\\"INTC\\\",\\\"CSCO\\\"]\\n\",\n    \"dates = ['01302019','12282018','05302019','05302018','03272019']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_acf(date,symbol,lags=range(50)):\\n\",\n    \"    a = environment.ts(date,'NASDAQ',symbol,PATH=\\\"/Volumes/LaCie/\\\")\\n\",\n    \"    executions = a.messages[(a.messages.type==\\\"E\\\") & (a.messages.time < tmax) & (a.messages.time > tmin)]\\n\",\n    \"    sign = executions.loc[:,['time','side']]\\n\",\n    \"    sign.loc[:,'side'] = sign.side.apply(lambda x: x if x else -1)\\n\",\n    \"    acf = autocorr(sign.side.values, lags=lags)\\n\",\n    \"    return acf\\n\",\n    \"\\n\",\n    \"def plot_acf(lags,acf):\\n\",\n    \"    plt.loglog(lags,acf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"AAPL\\t01302019\\n\",\n      \"AAPL\\t12282018\\n\",\n      \"AAPL\\t05302019\\n\",\n      \"AAPL\\t05302018\\n\",\n      \"AAPL\\t03272019\\n\",\n      \"RARE\\t01302019\\n\",\n      \"RARE\\t12282018\\n\",\n      \"RARE\\t05302019\\n\",\n      \"RARE\\t05302018\\n\",\n      \"RARE\\t03272019\\n\",\n      \"MSFT\\t01302019\\n\",\n      \"MSFT\\t12282018\\n\",\n      \"MSFT\\t05302019\\n\",\n      \"MSFT\\t05302018\\n\",\n      \"MSFT\\t03272019\\n\",\n      \"INTC\\t01302019\\n\",\n      \"INTC\\t12282018\\n\",\n      \"INTC\\t05302019\\n\",\n      \"INTC\\t05302018\\n\",\n      \"INTC\\t03272019\\n\",\n      \"CSCO\\t01302019\\n\",\n      \"CSCO\\t12282018\\n\",\n      \"CSCO\\t05302019\\n\",\n      \"CSCO\\t05302018\\n\",\n      \"CSCO\\t03272019\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"acfs={}\\n\",\n    \"tmp = {}\\n\",\n    \"for s in symbols:\\n\",\n    \"    for d in dates:\\n\",\n    \"        print(s,d,sep=\\\"\\\\t\\\")\\n\",\n    \"        tmp[d] = get_acf(d,s,lags)\\n\",\n    \"    acfs[s] = np.mean(np.array(list(tmp.values())),axis=0)\\n\",\n    \"    tmp = {}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Log log Plot of the autocorrelation for different stocks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure()\\n\",\n    \"for s in symbols:\\n\",\n    \"    plot_acf(range(20),acfs[s])\\n\",\n    \"plt.grid(1)\\n\",\n    \"plt.legend(symbols)\\n\",\n    \"plt.show()    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/.ipynb_checkpoints/ChronoClock-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Partial Gaussianity recovery trought volume clock\\n\",\n    \"\\n\",\n    \"Reproducing Results of ch1 in High-Frequency trading EaslEy, de Prado and O’Hara.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import environment\\n\",\n    \"import utility\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import stats\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"import scipy.signal\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Trading hours:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tmin=9.5*1e9*60*60\\n\",\n    \"tmax=16.5*1e9*60*60\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Selecting data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"symbols = ([\\\"QQQ\\\"])\\n\",\n    \"symbols.sort()\\n\",\n    \"#dates = ['01302019','12282018','05302019','05302018','03272019']\\n\",\n    \"dates = ['01302019']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Extract returns in chrono time and in volume time\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_returns_chrono(symbol,date):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Resample execution price with linear interpolation in minute intervals\\n\",\n    \"    Returns pertental price change normalized by the sqrt of the time interval\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    a = environment.ts(date,'NASDAQ',symbol,PATH='/Volumes/LaCie/data')\\n\",\n    \"    mask = (a.messages.type=='E') & (a.messages.time<tmax) & (a.messages.time>tmin)\\n\",\n    \"    executions = a.messages.loc[mask,['price','time']].copy()\\n\",\n    \"    minutes_t = np.arange(tmin,tmax,60*1e9)\\n\",\n    \"    price = np.interp(minutes_t,executions.time,executions.price)\\n\",\n    \"    t = minutes_t*1e-9\\n\",\n    \"    change = np.diff(price) / price[1:]\\n\",\n    \"    res = change/np.sqrt(np.diff(t))\\n\",\n    \"    return res[~np.isnan(res) & ~np.isinf(res)]\\n\",\n    \"\\n\",\n    \"def get_returns_volume(symbol,date):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Get 50 data points in non constant time-intervals sampled every V/50 executed lots.\\n\",\n    \"    Where V is the total volume traded in that day.\\n\",\n    \"    Returns pertental price change normalized by the sqrt of the time interval\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    a = environment.ts(date,'NASDAQ',symbol,PATH='/Volumes/LaCie/data')\\n\",\n    \"    mask = (a.messages.type=='E') & (a.messages.time<tmax) & (a.messages.time>tmin)\\n\",\n    \"    executions = a.messages.loc[mask].copy()\\n\",\n    \"    total_vol = np.sum(executions['execSize'])\\n\",\n    \"    vol_bin_size = total_vol/50\\n\",\n    \"    x,_ = scipy.signal.find_peaks(np.cumsum(executions['execSize']) % int(vol_bin_size))\\n\",\n    \"    t = executions.iloc[x].time*1e-9\\n\",\n    \"    price = executions.iloc[x]['price']\\n\",\n    \"    change = np.diff(price) / price[1:]\\n\",\n    \"    res = change/np.sqrt(t.diff())\\n\",\n    \"    return res[~np.isnan(res) & ~np.isinf(res)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Building data of returns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"date:\\t01302019\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"chrono = np.array([])\\n\",\n    \"volume = np.array([])\\n\",\n    \"for d in dates:\\n\",\n    \"    print(\\\"date:\\\",d,sep='\\\\t')\\n\",\n    \"    chrono_time_ret = get_returns_chrono(symbols[0],d)\\n\",\n    \"    vol_time_ret = get_returns_volume(symbols[0],d)\\n\",\n    \"    chrono = np.append(chrono,chrono_time_ret)\\n\",\n    \"    volume = np.append(volume,vol_time_ret.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Outiler selection and normalization (if needed):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"chrono_n=chrono#[(chrono < 0.005) & (chrono > -0.005)]\\n\",\n    \"volume_n=volume#[(volume < 0.005) & (volume > -0.005)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### QQPlots of the returns in the two different clocks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure()\\n\",\n    \"plt.title('QQPlot')\\n\",\n    \"plt.subplot(1,2,1)\\n\",\n    \"scipy.stats.probplot(chrono, dist=\\\"norm\\\",plot=plt)\\n\",\n    \"plt.title('chrono clock')\\n\",\n    \"plt.grid(1)\\n\",\n    \"plt.subplot(1,2,2)\\n\",\n    \"scipy.stats.probplot(volume, dist=\\\"norm\\\",plot=plt)\\n\",\n    \"plt.title('volume clock')\\n\",\n    \"plt.grid(1)\\n\",\n    \"plt.ylabel(' ')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/.ipynb_checkpoints/LFTimePatters-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Evidence of chrono time by LF taders\\n\",\n    \"\\n\",\n    \"Reproducing Figure 1.4 in High-Frequency trading EaslEy, de Prado and O’Hara.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#%%javascript\\n\",\n    \"#IPython.OutputArea.auto_scroll_threshold = 9999;\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import environment\\n\",\n    \"import utility\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import stats\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"from mpl_toolkits.mplot3d import Axes3D\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#plt.rcParams['figure.figsize'] = [18,12]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### List of principal securities\\n\",\n    \"\\n\",\n    \"Choose semi-random liquid-securities in NASDAQ.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"symbols = ([\\\"AAPL\\\",\\\"ARQL\\\",\\\"CSCO\\\",\\\"QQQ\\\",\\\"FB\\\",\\\"INTC\\\",\\\"QCOM\\\",\\\"NVDA\\\",\\\"CMCSA\\\",\\\"AMD\\\",\\\"EEM\\\",\\\"SPY\\\",\\\"GDX\\\",\\\"MSFT\\\",\\\"AMZN\\\"])\\n\",\n    \"symbols.sort()\\n\",\n    \"dates = ['01302019','12282018','05302019','05302018','03272019']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Function to obtain the series of total Volumes for each hour,second pair\\n\",\n    \"avg over minutes (only execution volume)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_total_size(symbol,date):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    read message data, extract executions, group by hour and second bukets by mean.\\n\",\n    \"    So for each pair security/data return a ps.Series of mean volume happend in that Hour/Second combination.\\n\",\n    \"    \\n\",\n    \"    e.g. (15,38) then is the avg volume executed in all times were the hour was 15 and the seconds were 38.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    a = environment.ts(date,'NASDAQ',symbol,PATH='/Volumes/LaCie/data')\\n\",\n    \"    executions = a.messages.loc[a.messages.type=='E'].copy()\\n\",\n    \"    new_time = executions.time.apply(lambda x: utility.parseNanosecondsToDateTime(x))\\n\",\n    \"    executions.loc[:,'time'] = new_time\\n\",\n    \"    executions.loc[:,'hs'] = executions.time.apply(lambda x: (x.hour,x.second))\\n\",\n    \"    ts_total_size_by_sec = executions.fillna(0).groupby(['hs']).mean()\\n\",\n    \"    volumes = ts_total_size_by_sec.loc[ts_total_size_by_sec.index.map(lambda x: x[0]>=9 and x[0]<=16),'size']\\n\",\n    \"    return volumes/volumes.sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Joining all the series in a dtaframe\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"AAPL\\t01302019\\n\",\n      \"AAPL\\t12282018\\n\",\n      \"AAPL\\t05302019\\n\",\n      \"AAPL\\t05302018\\n\",\n      \"AAPL\\t03272019\\n\",\n      \"AMD\\t01302019\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.7/site-packages/numpy/core/_methods.py:32: RuntimeWarning: invalid value encountered in reduce\\n\",\n      \"  return umr_minimum(a, axis, None, out, keepdims, initial)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"AMD\\t12282018\\n\",\n      \"AMD\\t05302019\\n\",\n      \"AMD\\t05302018\\n\",\n      \"AMD\\t03272019\\n\",\n      \"AMZN\\t01302019\\n\",\n      \"AMZN\\t12282018\\n\",\n      \"AMZN\\t05302019\\n\",\n      \"AMZN\\t05302018\\n\",\n      \"AMZN\\t03272019\\n\",\n      \"ARQL\\t01302019\\n\",\n      \"ARQL\\t12282018\\n\",\n      \"ARQL\\t05302019\\n\",\n      \"ARQL\\t05302018\\n\",\n      \"ARQL\\t03272019\\n\",\n      \"CMCSA\\t01302019\\n\",\n      \"CMCSA\\t12282018\\n\",\n      \"CMCSA\\t05302019\\n\",\n      \"CMCSA\\t05302018\\n\",\n      \"CMCSA\\t03272019\\n\",\n      \"CSCO\\t01302019\\n\",\n      \"CSCO\\t12282018\\n\",\n      \"CSCO\\t05302019\\n\",\n      \"CSCO\\t05302018\\n\",\n      \"CSCO\\t03272019\\n\",\n      \"EEM\\t01302019\\n\",\n      \"EEM\\t12282018\\n\",\n      \"EEM\\t05302019\\n\",\n      \"EEM\\t05302018\\n\",\n      \"EEM\\t03272019\\n\",\n      \"FB\\t01302019\\n\",\n      \"FB\\t12282018\\n\",\n      \"FB\\t05302019\\n\",\n      \"FB\\t05302018\\n\",\n      \"FB\\t03272019\\n\",\n      \"GDX\\t01302019\\n\",\n      \"GDX\\t12282018\\n\",\n      \"GDX\\t05302019\\n\",\n      \"GDX\\t05302018\\n\",\n      \"GDX\\t03272019\\n\",\n      \"INTC\\t01302019\\n\",\n      \"INTC\\t12282018\\n\",\n      \"INTC\\t05302019\\n\",\n      \"INTC\\t05302018\\n\",\n      \"INTC\\t03272019\\n\",\n      \"MSFT\\t01302019\\n\",\n      \"MSFT\\t12282018\\n\",\n      \"MSFT\\t05302019\\n\",\n      \"MSFT\\t05302018\\n\",\n      \"MSFT\\t03272019\\n\",\n      \"NVDA\\t01302019\\n\",\n      \"NVDA\\t12282018\\n\",\n      \"NVDA\\t05302019\\n\",\n      \"NVDA\\t05302018\\n\",\n      \"NVDA\\t03272019\\n\",\n      \"QCOM\\t01302019\\n\",\n      \"QCOM\\t12282018\\n\",\n      \"QCOM\\t05302019\\n\",\n      \"QCOM\\t05302018\\n\",\n      \"QCOM\\t03272019\\n\",\n      \"QQQ\\t01302019\\n\",\n      \"QQQ\\t12282018\\n\",\n      \"QQQ\\t05302019\\n\",\n      \"QQQ\\t05302018\\n\",\n      \"QQQ\\t03272019\\n\",\n      \"SPY\\t01302019\\n\",\n      \"SPY\\t12282018\\n\",\n      \"SPY\\t05302019\\n\",\n      \"SPY\\t05302018\\n\",\n      \"SPY\\t03272019\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame()\\n\",\n    \"for s in symbols:\\n\",\n    \"    for d in dates:\\n\",\n    \"        print(s,d,sep='\\\\t')\\n\",\n    \"        try:\\n\",\n    \"            serie = get_total_size(s,d)\\n\",\n    \"            serie.name = s+'_'+d\\n\",\n    \"            df=df.join(serie, how='outer')\\n\",\n    \"        except:\\n\",\n    \"            pass\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Normalizing the data since we want to give same weight to a stock very traded (large number size) and one that is not very traded\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"normalized_df = (df-df.min())/(df.max()-df.min())\\n\",\n    \"orig_cols = normalized_df.columns\\n\",\n    \"normalized_df.loc[:,'hour'] = normalized_df.index.map(lambda x: x[0])\\n\",\n    \"normalized_df.loc[:,'sec'] = normalized_df.index.map(lambda x: x[1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Building the matrix with n=0,...,59 (seconds) rows and m=9,...,16 columns (hours). \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n=len(set(normalized_df.sec.values))\\n\",\n    \"m=len(set(normalized_df.hour.values))\\n\",\n    \"B = np.zeros([n,m])\\n\",\n    \"for i in range(n):\\n\",\n    \"    for j in range(m):\\n\",\n    \"        ans = normalized_df.loc[(normalized_df.hour==j+9) & (normalized_df.sec==i)].loc[:,orig_cols].fillna(0).mean(axis=1).values\\n\",\n    \"        if ans.size>0:    \\n\",\n    \"            B[i,j] = ans\\n\",\n    \"\\n\",\n    \"A = B/np.sum(B,axis=0)/m\\n\",\n    \"            \\n\",\n    \"vol_df = pd.DataFrame(data=A*100,index = range(n), columns = range(9,9+m))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAQABJREFUeAHs3Qm8neOdOPDn5mZfCEkYlUiREUsRI4khtjBoYy9mqkWK0hatrajW39oqbXWY6UxTy0R0pZS2qLHFUkuFWksQbUiKSiSRiMh27z/POz239yb33pxz7lne857v+/kc5z3v+6zf57jc332e92loXnUEBwECBAgQIECAAAECBAgQIECAAAECmRTolsle6RQBAgQIECBAgAABAgQIECBAgAABAomAAKAvAgECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQIBAdwTZFFi5cmWYOnVqeOSRR8JLL70UZs2aFd5///2wZMmS0KdPn9C/f/8wbNiwsNVWW4Vx48aF8ePHh8bGxmxi6BUBAgQIECBAgAABAgQIECBAoI4FGppXHXXc/8x1ffHixeGKK64IV111VViwYMEa/YvD3dDQsMb1gQMHhtNOOy2cccYZoV+/fmvcL9eFGzf6TLmKVm6BAj/vsbDAHJKXS2CTbpX7d7BcfchKuc+teDcrXan5fry74v2a70OWOnBEr82y1J2a7stzwb8baRnAKadukJamaMcqgb5nXFPzDsvn/il1fegx2M//1A2KBhHIU8AMwDyhaiHZa6+9Fg444IDwyiuvhNXjujGoF1+9evUKS5cuDTFQGF+5Y/78+eHCCy8MP/vZz8Idd9wRNt1009wt7wQIECBAgAABAgQIECBAgAABAjUsIABYw4PXuulxae/+++8fXn311ST4F5f2HnPMMWGPPfYIW265ZYgz/FY/4gzB6dOnhwcffDDccMMNyVLh+DmW84c//CH07t179Sw+EyBAgAABAgQIECBAgAABAgQI1JiATUBqbMA6au5//dd/JTP/4v3vfOc74YUXXgjnnHNO+Od//ud2g38xXQwKxvsxXUx/+eWXx8vh5ZdfDrE8BwECBAgQIECAAAECBAgQIECAQO0LmAFY+2OY9OCmm25Knu13wgknhDPPPLPgXsXnAp511llhxowZ4ZprrgmxvGLKyVU8e/bs3Kl3AgQIECBAgAABAgQIEChUoGlloTmkJ0CAQIcCAoAd0tTWjfj8v3h86lOf6lLDjzzyyCQAGAOBXTniDsP5HD//h0/nk0waAgQIECBAgAABAgQIECBAgACBIgUEAIuES1u2uLFHPPr06dOlpuXyL1u2rEvlyEyAAAECBAgQIECAAAECXRBobupCZlkJECDQVkAAsK1HzX6KM+7i7r8PPPBA2GmnnYrux9SpU5O8Q4cOLbqMmHHWrFl55X9kzDl5pZOIAAECBAgQIECAAAECBAgQIECgOAEBwOLcUpdrwoQJyeYdl156aRg/fnwYO3ZswW38/e9/H2L++DzAuBNwV46uBhC7Ure8BAgQIECAAAECBAgQIECAAAECfxewC/DfLWr67NRTTw3rrLNOeP/998Nuu+0WTj755DBt2rTQ1NT5tPF4P6Y76aSTwu67757kj+XE8hwECBAgQIAAAQIECBAgUCWB+Ltc2l5VolAtAQJdFzADsOuGqShhk002CT/60Y+STUCWLFkSJk2alLz69esXRowYEeKMvP79+4eePXuG+Hy/GCiMO/XGzT4WL16c9KG5uTl5huCPf/zjkO8mHqnovEYQIECAAAECBAgQIECAAAECBAh0KCAA2CFN7d048MADw+9+97tk9l58j0cM9D377LPJq70exaBf7ogzB6+66qowatSo3CXvBAgQIECAAAECBAgQIECAAAECNS7QsCoA9PcIUI13RvP/LhCX9d52221JQPCll14Kc+fO/fvNv50NGjQobL311mHXXXcNhxxySBgzZswaacp94YaNjyp3FcrPU+C+HkvyTClZuQUWNS8vdxXKz1NgeEPfPFNKVm6BWc0flLsK5Rcg8JGGPgWklrScAs+teLecxSu7AIER3QcWkFrScgtcO/PmcldR9vKXvfnHstdRaAU9P7JNoVmkJ0AgJQJmAKZkIErdjBjMax3QW7p0aVi0aFH48MMPQ+/evcOAAQNCr169Sl2t8ggQIECAAAECBAgQIECAAAECBFImIACYsgEpV3NisE/Ar1y6yiVAgAABAgQIECBAgAABAgQIpFdAADC9Y6NlBAgQIECAAAECBAgQIFCvAnEHYAcBAgRKJNCtROUohgABAgQIECBAgAABAgQIECBAgACBFAoIAKZwUDSJAAECBAgQIECAAAECBAgQIECAQKkELAEulaRyCBAgQIAAAQIECBAgQIBAqQSaLQEuFaVyCBAIwQxA3wICBAgQIECAAAECBAgQIECAAAECGRYQAMzw4OoaAQIECBAgQIAAAQIECBAgQIAAAUuAfQcIECBAgAABAgQIECBAgEDaBJpWpq1F2kOAQA0LmAFYw4On6QQIECBAgAABAgQIECBAgAABAgTWJiAAuDYh9wkQIECAAAECBAgQIECAAAECBAjUsIAlwDU8eFlo+uvdm7PQjUz0Yf3QIxP9yEInlgc7vqVlHOeF5WlpSt2347Wl79a9QZoAPtbro2lqTl23pU+D/36n5Qtw3sD30tIU7ciKgF2AszKS+kEgFQJmAKZiGDSCAAECBAgQIECAAAECBAgQIECAQHkEBADL46pUAgQIECBAgAABAgQIECBAgAABAqkQsAQ4FcOgEQQIECBAgAABAgQIECBAoJVAk8fCtNJwSoBAFwXMAOwioOwECBAgQIAAAQIECBAgQIAAAQIE0iwgAJjm0dE2AgQIECBAgAABAgQIECBAgAABAl0UsAS4i4CyEyBAgAABAgQIECBAgACBUgs02wW41KTKI1DXAmYA1vXw6zwBAgQIECBAgAABAgQIECBAgEDWBcwAzPoI6x8BAgQIECBAgAABAgQI1J6ATUBqb8y0mECKBcwATPHgaBoBAgQIECBAgAABAgQIECBAgACBrgoIAHZVUH4CBAgQIECAAAECBAgQIECAAAECKRawBDjFg6NpBAgQIECAAAECBAgQIFCnAjYBqdOB120C5REwA7A8rkolQIAAAQIECBAgQIAAAQIECBAgkAoBMwBTMQzVacSCBQvCXXfdFV5//fXQv3//sO2224bddtstNDQ0VKxBvULl6qpYp2q0otebP6jRlmev2bs1r5O9TtVojx4Ni2q05dlr9vrd+2WvUzXco7caltVw67PV9MHd+mSrQzXcm5/N71vDrc9e08/NXpf0iAABAl0SEADsEl86Mz///PPhoosuCg8//HD48MMPw5ZbbhlOP/308KlPfaqlwTfccEM45ZRTwuLFi1uuxZPNNtssXH311WH8+PFtrvtAgAABAgQIECBAgAABAhUUaFpZwcpURYBA1gUsAc7YCN99991h7Nix4dZbbw1z5swJixYtCk8++WT4zGc+E84777ykt3fccUc4/vjjw/vvvx+am5vbvF577bUwYcKEcP/992dMRncIECBAgAABAgQIECBAgAABAvUpIACYoXGfN29eOOqoo8LSpUuToN7IkSPD6NGjQ2NjY/L5W9/6VnjqqafCaaedFpqamsK//uu/hp/85CchBg0nT56cBP4iR8x/3HHHJbMHM8SjKwQIECBAgAABAgQIECBAgACBuhSwBDhDw37dddeFuXPnhu7du4df/OIX4eCDD0569/LLL4c999wzvPPOO+HEE08Mf/rTn0JcAhxnBbY+Jk6cGP793/89nHnmmWHWrFnhpptuCsccc0zrJHmfz549O++0EhIgQIAAAQIECBAgQIDAagJ2AV4NxEcCBLoiYAZgV/RSlvfOO+9MNvD45Cc/2RL8i02MMwHPOuusZBbgM888Ew499NA1gn+5rsRnBY4bNy75ePvtt+cuF/w+bNiwkM+r4IJlIECAAAECBAgQIECAAAECBAgQKEhAALAgrnQnfvHFF5MGHn744Ws09KCDDmq5Fpf+dnbEmYHx2YAxWOggQIAAAQIECBAgQIAAAQIECBCobQFLgGt7/Nq0fsGCBcnnoUOHtrkeP2y88cYt1zbffPOW8/ZOtt122+RyXDJc7BGXEOdz/HTcV/NJJg0BAgQIECBAgAABAgTqS2DVc9sdBAgQKJWAAGCpJFNQTp8+fZJdf7t1W3NiZ7yXO1qf5661fh8wYEDyccmSJa0vF3TeXhCyoAIkJkCAAAECBAgQIECAAAECBAgQKInAmpGikhSrkGoIDBkyJKn2rbfe6lL177//fpJ/0KBBXSpHZgIECBAgQIAAAQIECBAgQIAAgeoLmAFY/TEoWQviMt+4w29Hy28nT56c1LW22XmvvfZakm7DDTcsWdsURIAAAQIECBAgQIAAAQIFCNgFuAAsSQkQWJuAAODahGro/ujRo8PDDz8c/vCHP7Tb6okTJ7Z7ffWLv/vd75JLI0aMWP2WzwQIECBAgAABAgQIECBAgAABAjUmIABYYwPWWXPHjBmT7N57//33d5as03tLly4Nt9xyS2hoaAjjxo3rNG0pbq67shSlKKMUAr0aPRGgFI6lKGN5QylKUUYpBLoHg1EKx1KUsUFj31IUo4wSCRy6pLFEJSmmqwJnN8zuahHyl0hgy16blagkxRAgQIAAgdILCACW3rRqJU6YMCHcc889Sf3Nzc1JEK/Qxvz85z8PcROQ+Np///0LzS49AQIECBAgQIAAAQIECJRCwC7ApVBUBgECfxMQAMzQVyEG7fbee+8u9SguE853qXCXKpKZAAECBAgQIECAAAECBAgQIECgIgLW/FWEWSUECBAgQIAAAQIECBAgQIAAAQIEqiNgBmB13NVKgAABAgQIECBAgAABAgQ6FGhu9sD0DnHcIECgYAEzAAsmk4EAAQIECBAgQIAAAQIECBAgQIBA7QgIANbOWGkpAQIECBAgQIAAAQIECBAgQIAAgYIFLAEumEwGAgQIECBAgAABAgQIECBQZoHmpjJXoHgCBOpJwAzAehptfSVAgAABAgQIECBAgAABAgQIEKg7ATMA627IdZgAAQIECBAgQIAAAQIEUi/QZAZg6sdIAwnUkIAZgDU0WJpKgAABAgQIECBAgAABAgQIECBAoFABAcBCxaQnQIAAAQIECBAgQIAAAQIECBAgUEMClgDX0GBlsan9zWpPzbD27N6YmrbUe0Oean6/3glS0/8Pm1ekpi313pANuvWpd4JU9f/GPstT1Z56bsyA5f7dSMv4X/jWA2lpinasEvh6FhRsApKFUdQHAqkRMAMwNUOhIQQIECBAgAABAgQIECBAgAABAgRKLyAAWHpTJRIgQIAAAQIECBAgQIAAAQIECBBIjYAlwKkZCg0hQIAAAQIECBAgQIAAAQJ/E2haiYIAAQIlEzADsGSUCiJAgAABAgQIECBAgAABAgQIECCQPgEBwPSNiRYRIECAAAECBAgQIECAAAECBAgQKJmAJcAlo1QQAQIECBAgQIAAAQIECBAokYBdgEsEqRgCBKKAGYC+BwQIECBAgAABAgQIECBAgAABAgQyLCAAmOHB1TUCBAgQIECAAAECBAgQIECAAAEClgD7DhAgQIAAAQIECBAgQIAAgbQJNDWlrUXaQ4BADQuYAVjDg6fpBAgQIECAAAECBAgQIECAAAECBNYmIAC4NiH3CRAgQIAAAQIECBAgQIAAAQIECNSwgCXANTx4xTR92rRp4cYbbwzxfc6cOaF79+5h4403Drvttls49thjw0YbbVRMsfIQIECAAAECBAgQIECAQCkF7AJcSk1lEah7gYbmVUfdK2QA4I9//GO47rrrQrdu3cJll12WBPZad2vhwoXhuOOOC7feemvry23Oe/bsGb7xjW+EM888s831cn64cPhnylm8sgsQeC4sKiC1pOUUMDW7nLqFle3JO4V5lTP1m8sXlrN4ZRcoMKLH+gXmkLxcAv/Q0KtcRSuXQE0LfHfmz2q6/bHxHz6Wvj703vnImnfVAQL1KmAGYEZG/qabbgpXXnllGDdu3BrBv2XLloUJEyaExx57LOTivXHm3+DBg8PKlSvD3Llzk+tLly4NZ599dli0aFG48MILMyKjGwQIECBAgAABAgQIECBAgACB+hYw0SQj4z916tTQ0NAQDj300DV69J//+Z/h0UcfTa5vv/324fbbb0+CfG+++Wb461//GhYsWBAmT54chg4dmgQC4yzAZ599do1yXCBAgAABAgQIECBAgACBCgnEXYDT9qpQ11VDgEDpBQQAS29alRJnzpyZ1LvDDjusUf8111yTBAf/+Z//OZkFGGcD9ur19+UiAwYMCBMnTgxPPPFESxDw+9///hrluECAAAECBAgQIECAAAECBAgQIFB7ApYA196YtdviuKFHPAYNGtTm/vz588Mrr7ySBAAvv/zy0Lt37zb3W3/4h3/4h/DVr341nHLKKSHOKOzKMXv27K5kl5cAAQIECBAgQIAAAQIECBAgQKBEAgKAJYKsdjFxFt+7774b3nvvvTZNiddyx5gxY3KnHb7vtNNOyb233nqrwzT53Bg2bFg+ycIFm3w6r3QSESBAgAABAgQIECBAoK4E4vJfBwECBEokYAlwiSCrXczw4cOTJkybNq1NU1rPCPzwww/b3Gvvw5IlS5LLcUdgBwECBAgQIECAAAECBAgQIECAQO0LCADW/hgmPYjP9Ys7/Mbn/cWdfXPHeuutF7bZZpvk429/+9vc5Q7f77zzzuTepptu2mGafG7MmjUr5PPKpyxpCBAgQIAAAQIECBAgQIAAAQIEihcQACzeLlU5P/e5z4V+/folz/s74YQTVm0W9ffp4ueee24SHPzKV74SZsyY0WG743P/rrzyyuR5gYccckiH6fK5EXcUzueVT1nSECBAgAABAgQIECBAoN4EmptXrvo9Ll2vehsD/SWQJQEBwIyMZnzm3qWXXpoE+qZMmRJGjx4dbrrpprB48eLw6U9/Onz5y18Ob775Zoi7BJ9xxhkhzgZ84YUXwrPPPhtuvfXWcMwxx4R99903xGXCG264YfjSl76UERndIECAAAECBAgQIECAAAECBAjUt4BNQDI0/jFoFzcBOf/885PA3pFHHpnM5vvoRz8aBg8enOwAHAOCV111VfJavetxCfHAgQPDzTffHOLSYQcBAgQIECBAgAABAgQIVEmg1aquKrVAtQQIZEjADMAMDWbsynnnnRfuueeesPnmmyezAeNS4D/96U8hbg6ydOnSJCCY63IM+LV+feITnwi///3vwy677JJL4p0AAQIECBAgQIAAAQIECBAgQKDGBcwArPEBbK/5e++9d3j55ZfD3XffHX7xi1+Exx9/PEyfPr3NcwFjviFDhiQbhOy+++7hsMMOC9tuu217xZX12kthcVnLV3j+AoMaeuWfWMqyCnQPDWUtX+H5C7ze9H7+iaUsq8D/9OlX1vIVXpjApJU9CssgddkEnl7xbtnKVnBhAmcsH1RYBqkJECBAgEAFBQQAK4hdyaoaGhrCfvvtl7xivXGmX1weHJcA9+7dOwwYMCD07Nmzkk1SFwECBAgQIECAAAECBAjkK9D8940d880iHQECBDoSEADsSCZj12NAMD7fL74cBAgQIECAAAECBAgQIECAAAEC9SPgGYD1M9Z6SoAAAQIECBAgQIAAAQIECBAgUIcCZgDW4aDrMgECBAgQIECAAAECBAikXMAuwCkfIM0jUFsCZgDW1nhpLQECBAgQIECAAAECBAgQIECAAIGCBAQAC+KSmAABAgQIECBAgAABAgQIECBAgEBtCVgCXFvjpbUECBAgQIAAAQIECBAgUA8CdgGuh1HWRwIVEzADsGLUKiJAgAABAgQIECBAgAABAgQIECBQeQEBwMqbq5EAAQIECBAgQIAAAQIECBAgQIBAxQQsAa4YtYoIECBAgAABAgQIECBAgECeAnYBzhNKMgIE8hEwAzAfJWkIECBAgAABAgQIECBAgAABAgQI1KiAGYA1OnBZafaABl/BtIxlc2hOS1Pqvh0fhqa6N0gLwIhuA9LSlLpvx/dWrqx7gzQBbL+yR5qaU9dtmdWtV133P02d36D7h2lqjrYQIECAAIE2AqIvbTh8IECAAAECBAgQIECAAAECKRCwC3AKBkETCGRHwBLg7IylnhAgQIAAAQIECBAgQIAAAQIECBBYQ0AAcA0SFwgQIECAAAECBAgQIECAAAECBAhkR8AS4OyMpZ4QIECAAAECBAgQIECAQFYE7AKclZHUDwKpEDADMBXDoBEECBAgQIAAAQIECBAgQIAAAQIEyiMgAFgeV6USIECAAAECBAgQIECAAAECBAgQSIWAJcCpGAaNIECAAAECBAgQIECAAAECrQQsAW6F4ZQAga4KmAHYVUH5CRAgQIAAAQIECBAgQIAAAQIECKRYQAAwxYOjaQQIECBAgAABAgQIECBAgAABAgS6KmAJcFcF5SdAgAABAgQIECBAgAABAqUWaG4qdYnKI0CgjgXMAKzjwdd1AgQIECBAgAABAgQIECBAgACB7AuYAZjhMX799dfDww8/HF588cXwxhtvhEWLFoUlS5aEAQMGhPXWWy9ss802YezYsWGXXXYJDQ0NGZbQNQIECBAgQIAAAQIECNSYgE1AamzANJdAugUEANM9PkW17pe//GW47LLLwlNPPZVX/mHDhoUvfOEL4Ywzzgg9e/bMK0+pEv15xcJSFaWcLgr071bZse9iczOdfVzDwEz3r5Y690TwMyot4/X68gVpaYp2rBJY3nMIh5QILFyxNCUt0YwLGldCSJHAnSlqi6YUJhAnsvzHf/xHuOOOO8KsWbNCr169wuabbx7+9V//NZx88smhb9++hRXYSep77703/PjHPw6/+93vwltvvRW6d+8eNtxww7DddtuFvffeOxx99NGhf//+HZbwwQcfhO9///vhF7/4RXjttdfC0qVLQ/z9ev/99w9f/vKXw/DhwzvM6waBSgs0NK86Kl2p+sojEH/4fOpTn0p+UMYaVh/aOMtv9Wu5lsR7I0eODDfddFP42Mc+lrtc9ve9h+5b9jpUkJ+AAGB+TpVIJQBYCeX86hAAzM+pEqkEACuhnH8dWwsA5o9V5pSzVywqcw2Kz1egVzdzK/K1qkS6O9+o/RDgkl9/txJUBdXR56CvFJS+0MS/+c1vwlFHHRUWLmz/j7BbbLFF8vvuiBEjCi26Tfr58+eHY489NvzqV79qc331D08//XQYNWrU6peTzzNmzAgTJkwIr776arv311lnnfCTn/wkHHDAAe3ed5FApQX8V6rS4mWs75Of/GS45557kiDfJptsEvbZZ5/krxfvvfdeeOihh8Lzzz+f/PXke9/7Xthxxx2TH1RxlmCcMRiXCE+fPj2MHz8+TJ06taJBwDKSKJoAAQIECBAgQIAAAQK1KVBnm4DEYNu//du/JY+tirPuzj333OT30/gYq5///OfhmmuuCa+88koyu+7JJ59MHm1VzMDG34/j78q5FXOHHnpoOPzww5NZho2NjcmswwcffDDccsstHRYfH68VZ/nlgn8nnHBCMhmnT58+ye/T3/rWt5IgZuzPI4880mEQscMK3CBQBgEzAMuAWo0ib7zxxnDkkUcmz/I7//zzw//7f/8vdOvWdo+Xn/3sZ+Gzn/1sssw3/rCLfz2JR9OqZ0tce+214fTTT09+2G677bbhD3/4Q4g//Mp9mAFYbuH8yzcDMH+rcqc0A7DcwvmXbwZg/lblTmkGYLmFCyvfDMDCvMqZ2gzAcuoWVrYZgIV5lTt1JmYA/urb5WYquPw+B59dcJ58M+y+++7JM+zjMtw4gWXnnXduk/U73/lOOPvs/6v/ggsuCBdeeGGb+/l+OOaYY8KPfvSjZHJMXAF30EEHtZs1rp5buXJlsix49QTxd+5LLrkkufztb387nHXWWW2SPProo2GPPfYIK1asSN4feOCBNvd9IFANgbYRomq0QJ0lEbjhhhuSco444ogQfxiuHvyLN2OAMAYGFy9eHL761a+21BvTnnjiiSFOt47nL7zwQjJVuSVBESezZ88O+byKKFoWAgQIECBAgAABAgQIEMiQwBNPPJEE/2KXjj/++DWCf/H6mWeeGbbaaqt4Gq666qqwfPny5LyQf8Rn/cXgXzy+8Y1vdBj8i/fjY7JiMHL1I9Ybn1EYj9ie2K7Vj7jRZuxHPOJswmnTpq2exGcCFRcQAKw4eXkqfOaZZ5IfUBMnTuy0gtz9+EDVZcuWtUm71157hU9/+tPJEuL4l5CuHPHBp/m8ulKHvAQIECBAgAABAgQIEMisQNwFOG2vMmHfdtttLSXHZ/O1d8TJKnH2XjwWLFiQLLVtL11n1+KGHfFYd911wymnnNJZ0g7vxUdmxWXE8Yi/X7c3+Sbei6vvcsett96aO/VOoGoCAoBVoy9txXPnzk0K/MhHPtJpwRtttFFyP05F/stf/rJG2vgcwXg899xza9xzgQABAgQIECBAgAABAgQIlFogzsyLR79+/ZLn1XdUflxWmzvis/UKOeIEmNymH/EZgL17906yx2W+cbfhmTNnhg8//HCtRebaGhO2bs/qGUePHt2yY3GhbV29LJ8JlEJAALAUiikoI/4FIx5vvvlmp62JW5vnjp49e+ZOW97j5iHxmDNnTsu1Yk7iD9B8XsWULQ8BAgQIECBAgAABAgQIZEfgpZdeSjoTd/dtb9ltrqdbbrll7jTk8rRcWMvJs88+2xLgi8+9jzsNn3baaWHw4MEh/h686aabJjMDY3Cws2f2vfjiiy01tW5Py8W/ncR+5HYrLrStq5flM4FSCKy5oL0UpSqj4gLbbbddMgU6PgvwE5/4RIf1554VGP+ykpsN2Drx0qVLk49x16WuHEOHDu1KdnkJECBAgAABAgQIECBQ3wIp3AU4Puc9n6OQ3wfjrLvcira15VtvvfWSWYLxufZxwkkhR+vAXdwIM87Qy+3imysnzhK89957w3333RfiTr7nnHNO7lbLe84g/k49cODAluvtncTHYsXVdXGCTfxdu1evXu0lc41ARQTMAKwIc/kriUt34y5F8dl9cTeieL76EXcKvvjii5NnBe67777tPqvgj3/8Y5KtveDg6uX5TIAAAQIECBAgQIAAAQL1I5DPc95jmkKORYsWtSTPZyJKDLzF4/3332/Jl8/JvHnzWpJdfvnlSfDv4x//eIgbkMQg5DvvvBN+8IMfJLMA4+/TcePM3JLhloyrTnLtLaStMX+h7W1dp3MCpRAwA7AUiikoI+4wFHcieuWVV5Lt0CdPnhzi1OUNN9wweUBp3EY9/uUh/iDr0aNHOO+889ptddwcJO52NHbs2Hbvu0iAAAECBAgQIECAAAECBEol0Pq5e+09pmr1enKz6JYsWbL6rU4/x1mDuSPWGX9fvv3220NjY2NyeciQIeELX/hC+NjHPpY82y/OEjz33HOTnYLj78i5I9feQtoa8xba3lx93gmUSkAAsFSSVS4n/hCMf52IP8TilOTXX389XHvttW1aFYN/8TkEkyZNCqNGjWpzL36Izw+Mzzro27dviDMEHQQIECBAgAABAgQIECBQJYG4A3DKjkKX3ebT/NxmHDFtXIK7tiP32Ko+ffqsLWmb+63riTfiLMBc8K91wl133TXEFXY333xz8pzB559/PsRHbuWOXDmFtDXmLbS9ufq8EyiVgABgqSRTUM7IkSPDU089FS666KLwk5/8pGVr8ti0OOtvr732SmYH7rTTTu22Nu4gPH/+/Hbvlevi+o2F/dAuVzuUG8K6DT0wpETg7pVd24QnJd3IRDN2ahyUiX5koRMf7fl/y32y0Jcs9OGIZf/3zOAs9KXW+/ClYCzSMoZnLV8/LU3RDgJlE1jbM/qKqXjAgAEt2fJZJpubyZfPEtyWgledtK4nzvbbYYcdWt9uc77ffvslAcB4cdq0aW0CgLlyCmlrLKfQ9sY8DgKlFPAMwFJqpqCs+IPs+9//fnj33XeTv1bELcqfeeaZJLD329/+NnQU/EtB0zWBAAECBAgQIECAAAECBOpMIM6oGzTo//7omttgoyOCOGElFwAs9FmDrdOvLZDZOm3cwKP1kcsb27FgwYLWt9Y4z82YjL+n55Yur5HIBQIVEhAArBB0pavp1q1biDMCd9lll+SvFXFZr4MAAQIECBAgQIAAAQIEakQgLgFO26tMdFtvvXVS8owZM8KKFSs6rGX69Okt97baaquW83xOttlmm5ZkK1eubDlv76T1/fgYrdZHrq3xWuv2tE4Tz2M/XnvtteRyoW1dvSyfCZRCQACwFIrKIECAAAECBAgQIECAAAECBIoSiM/di0ecVRcfa9XR8eCDD7bcGjduXMt5PifDhw8Pm2yySZJ05syZyQaZHeXLBe7i/Y033rhNslxb48XW7WmTaNWHJ598smW2YqFtXb0snwmUQkAAsBSKyiBAgAABAgQIECBAgAABAgSKEjjkkENa8k2ePLnlvPVJ3JX3hhtuSC4NHDgwjB8/vvXtvM4PO+ywJN3ChQvDfffd12GeX/7yly33Wgf84sU999wzrLvuusn9KVOmdBhIvP7665M08R+HHnpoy7kTAtUSEACslrx6CRAgQIAAAQIECBAgQIBARwLNzWFVdCldr47a2sXrY8eODbvttltSynXXXRcee+yxNUq84oorkufcxxunnnpqstFl60QPPPBAaGhoSF6f/exnW99qOT/ttNNCbhffM844I8RA4OrHj3/84xDLisf+++8fWj8PMF7r2bNn+PKXvxxPk/Z897vfTc5b/yO2P/YjHnvssUcYM2ZM69vOCVRFQACwKuwqJUCAAAECBAgQIECAAAECBHICV111VejTp0/y7Lx99903fOtb3wqPP/54mDp1avj85z8fzj777CTpFltsEc4888xctoLe4xLgiy++OMnz/PPPhxh4jDMO47LjWM+XvvSlkAserrPOOuHf//3f2y3/rLPOCrEd8Yjtiu2L+WN7Y7tj++MzAGN/rrzyynbLcJFApQXaPs2y0rWrjwABAgQIECBAgAABAgQIEKh7gR122CHceOON4aijjkpm5n3ta19bwyQG3e64444wYMCANe7leyEG7+bNmxcuv/zy8PLLL4fjjjtujawbbLBBuO2228I//uM/rnEvXoj1x3ZMmDAhvPrqq+Hqq69OXq0TxwDiT37ykzBq1KjWl50TqJqAGYBVo1cxAQIECBAgQIAAAQIECBDoQCBtOwDH9pT5OPDAA8Nzzz0XTj/99GSGXd++fUN83t/o0aOTgN3TTz8dRowY0eVWxFl6jzzySDj66KPDRz/60dCrV6/kuX5xqe4ll1wSXnnllbDzzjt3Wk9sR2xPDCTG9sV2xvaOHDkyaX/sxwEHHNBpGW4SqKRAQ/Oqo5IVqotAa4Ejhh/c+qPzKgqs29CjirWrurXAzJWLWn90XkWBnRoHVbF2VbcWWBr870prj2qfH7FsabWboP6/CXwpvMsiJQIXr9woJS3RjCjw8b/+vOYhlvzsgtT1oc+RF6WuTRpEgEB+AmYA5uckFQECBAgQIECAAAECBAgQIECAAIGaFPAMwJocNo0mQIAAAQIECBAgQIAAgUwLVGDJbab9dI4AgTYCAoBtOHyotMDAhp6VrlJ9HQi8btlpBzKVv9wjmJxdefX2a/xz85L2b7hacYFeDf69qDh6JxVe0t0S4E54KnprQFOvitanso4FftWz/M9H67h2d1YX+PjqF3wmQIBAnQsIANb5F0D3CRAgQIAAAQIECBAgQCCFAs2CyikcFU0iULMC/pxes0On4QQIECBAgAABAgQIECBAgAABAgTWLiAAuHYjKQgQIECAAAECBAgQIECAAAECBAjUrIAlwDU7dBpOgAABAgQIECBAgAABApkVsAlIZodWxwhUQ8AMwGqoq5MAAQIECBAgQIAAAQIECBAgQIBAhQQEACsErRoCBAgQIECAAAECBAgQIECAAAEC1RCwBLga6uokQIAAAQIECBAgQIAAAQKdCTQ3d3bXPQIECBQkYAZgQVwSEyBAgAABAgQIECBAgAABAgQIEKgtAQHA2hovrSVAgAABAgQIECBAgAABAgQIECBQkIAlwAVxSUyAAAECBAgQIECAAAECBCogYBfgCiCrgkD9CJgBWD9jracECBAgQIAAAQIECBAgQIAAAQJ1KCAAWIeDrssECBAgQIAAAQIECBAgQIAAAQL1I2AJcP2MdSp7+sbK91PZrnps1Abd+tZjt1PZ5yENPVPZrnpsVI/QUI/dTmWfn1k5P5XtqtdGjWpcr167nrp+v96wJHVtqtcG3bloer12Xb/LJWAJcLlklUugLgUEADM+7M8880x47bXXQmNjY9hqq63CyJEj8+rxnDlzwg9+8IMk7fnnn59XHokIECBAgAABAgQIECBAgAABAgTSJyAAmL4xKUmL/vd//zd86UtfSoJ/rQvcbrvtwje/+c0wYcKE1pfXOH/nnXfChRdeGBoaGoIA4Bo8LhAgQIAAAQIECBAgQIAAAQIEakbAMwBrZqjyb+hNN90UDjzwwCT419zcHFq/nnvuueTe8ccfH5YssWQkf1UpCRAgQIAAAQIECBAgUEGB5qYQ0vaqYPdVRYBAaQXMACytZ9VL++tf/xpOPPHEsGLFimT23mGHHRbGjx8fli5dGh588MFwxx13hJUrV4brr78+vPjii8nn9ddfv+Ttnj17dsnLVCABAgQIECBAgAABAgQIECBAgEDhAgKAhZulOsekSZPCwoULk2f+3XLLLeGggw5qae/pp58enn322fC5z30uPPXUU+GJJ54Iu+++e7jnnnvCRhtt1JKuFCfDhg3Lq5h9h348r3QSESBAgAABAgQIECBAgAABAgQIFCdgCXBxbqnNFZ/9F5/bd8IJJ7QJ/uUavP3224dHHnkkxCXAcWnwSy+9FHbdddcwc+bMXBLvBAgQIECAAAECBAgQIFBlgeamVY9zStmryiSqJ0CgCwJmAHYBL41ZX3nllaRZn/zkJztsXs+ePcM111wTNttss/D1r389Cf7ttttuyUzALbfcssN8hdyYNWtWXsmP3/mEvNJJRIAAAQIECBAgQIAAAQIECBAgUJyAAGBxbqnNFZf/xmPIkCFrbeO5554b4vP/Tj755PDmm28my4HvvvvuMGrUqLXmXVuCoUOHri2J+wQIECBAgAABAgQIECBAgAABAhUQsAS4AsiVrKJ///5Jde+++25e1X7+858PP/rRj5JnBs6dOzfstdde4fHHH88rr0QECBAgQIAAAQIECBAgUCaBplW7AKftVaauKpYAgfILCACW37iiNYwYMSKp78knn8y73iOPPDLcfPPNoVevXmHBggVh3333Dffdd1/e+SUkQIAAAQIECBAgQIAAAQIECBBIr4AAYHrHpqiWjR49Otnc4/bbby8of9wtOObp169fWLx4cYg7BjsIECBAgAABAgQIECBAgAABAgRqX8AzAGt/DNv0YJ999gmTJk1Kdvp9+eWXw8iRI9vc7+zD3nvvHeIuwgcccEB47733OktasnvbNg4sWVkK6prArOYlXStA7pIJvN38YcnKUlDXBIY09OpaAXKXTOB7PfwvS8kwS1DQhSsXl6AURZRCYGBDz1IUo4wSCJzXZ7sSlKIIAq0EmlctAXYQIECgRAJmAJYIMi3FfPzjH09m8TU3N4cLL7yw4Gbtsssu4f77789rE5GCC5eBAAECBAgQIECAAAECBAgQIECg4gL+nF5x8vJW2KdPn/Doo4+GuBtwt27FxXfjLsCxjIcffri8jVU6AQIECBAgQIAAAQIECLQv0NTc/nVXCRAgUISAAGARaGnPsu2223a5iZtttlmILwcBAgQIECBAgAABAgQIECBAgEBtCxQ3Ray2+6z1BAgQIECAAAECBAgQIECAAAECBOpGwAzAuhlqHSVAgAABAgQIECBAgACBmhFosglIzYyVhhKoAQEzAGtgkDSRAAECBAgQIECAAAECBAgQIECAQLECAoDFymU835QpU0JjY2Po3t0k0YwPte4RIECAAAECBAgQIECAAAECGRcQ3cn4AHele83Ndp3qip+8BAgQIECAAAECBAgQKFrAEuCi6WQkQGBNATMA1zRxhQABAgQIECBAgAABAgQIECBAgEBmBAQAMzOUOkKAAAECBAgQIECAAAECBAgQIEBgTQFLgNc0qekrxx13XEnaP2PGjJKUoxACBAgQIECAAAECBAgQKELAI5mKQJOFAIGOBAQAO5Kp0evXX399aGhoqJnWb9zUWDNtzXpD3+1mLNIyxvOalqalKXXfjj83r6h7g7QA/Hfom5amaMcqgY0bLCJJyxfh98veTktT6r4dj6/8sO4N0gRwQpoaoy0ECBBIgYAAYAoGoRxNsIFHOVSVSYAAAQIECBAgQIAAAQIECBCoPQEBwNobs05bPGjQoDBv3ryw3377hUmTJnWatrObN998czjrrLM6S+IeAQIECBAgQIAAAQIECJRLwC7A5ZJVLoG6FBAAzNiwjxkzJtx1111h+vTpYfjw4UX3bvDgwUXnlZEAAQIECBAgQIAAAQIECBAgQCA9Ah7gkp6xKElLYgAwHm+88UaYM2dOScpUCAECBAgQIECAAAECBAgQIECAQO0KCADW7ti12/KxY8e2XJ82bVrLuRMCBAgQIECAAAECBAgQqCGBpuYQ0vaqIT5NJUCgrYAlwG09av5TLgAYNwGJAcAJEyYU1acRI0aEiRMnFpVXJgIECBAgQIAAAQIECBAgQIAAgfQICACmZyxK0pIhQ4aEphI8LHbcuHEhvhwECBAgQIAAAQIECBAgQIAAAQK1LSAAWNvjp/UECBAgQIAAAQIECBAgkEWB5qYs9kqfCBCokoBnAFYJXrUECBAgQIAAAQIECBAgQIAAAQIEKiEgAFgJZXUQIECAAAECBAgQIECAAAECBAgQqJKAJcBVglctAQIECBAgQIAAAQIECBDoUCDuAOwgQIBAiQTMACwRpGIIECBAgAABAgQIECBAgAABAgQIpFHADMA0jkodtenVbsvqqLfp7mrv0JjuBtZR6zbo1qeOepvurm4YeqS7gXXUui2W+5tlmob78R7++52W8RjcvV9amlL37XhvxQd1bwCgtALNTTYBKa2o0gjUt4AAYJ2O/zvvvBOuu+668OCDD4a//OUvicJHPvKRsMcee4Rjjz02bLTRRnUqo9sECBAgQIAAAQIECBAgQIAAgWwJCABmazzDDTfckPTokEMOCeuss067vbv22mvD6aefHj74oO1fKV988cVw7733hksvvTRcccUV4fOf/3y7+V0kQIAAAQIECBAgQIAAAQIECBCoHQEBwNoZq7xa+tnPfjY0NDSE0aNHh6233nqNPFdffXX44he/mFxvbm5O0g4aNCh5nzt3bojXYmDwpJNOCt27dw/HH3/8GmW4QIAAAQIECBAgQIAAAQJlFrAJSJmBFU+gvgQEAOtovN98881w2mmnJUG+Hj16hPPPPz+ceOKJYciQIYlCDAD+8Ic/DJdccklYtmxZkvbggw8OgwcPLlhp9uzZBeeRgQABAgQIECBAgAABAgQIECBAoPQCAoClN01tiT/4wQ/Chx9+GLp16xZuvfXWMGHChDZtjYG+r3/962H77bcPMfAXZwLG5cJf/epX26TL58OwYcPySRa+OPyIvNJJRIAAAQIECBAgQIAAAQIECBAgUJyALfWKc6vJXPH5fnF58OGHH75G8K91hw444IBw2GGHJTMF77vvvta3nBMgQIAAAQIECBAgQIBAJQSaV+0CnLZXJfqtDgIEyiJgBmBZWNNZ6IwZM5KGxeDe2o4jjjgi3HzzzeGPf/zj2pK2e3/WrFntXl/94qW7nrH6JZ8JECBAgAABAgQIECBAgAABAgRKKCAAWELMtBe1cOHCpImbbbbZWpuaSzN//vy1pm0vwdChQ9u77BoBAgQIECBAgAABAgQIECBAgECFBQQAKwxezeriM/7efvvtgpoQdwJ2ECBAgAABAgQIECBAgECFBewCXGFw1RHItoDoTkbH96233gr9+/dv07uRI0cmAcC4PHfHHXdsc2/1D++8805yqZgdgFcvy2cCBAgQIECAAAECBAgQIECAAIHqCQgAVs++rDXvu+++HZb/+OOPh0MOOaTD+/HGs88+m9zfaKONOk3nJgECBAgQIFAAyp8AAEAASURBVECAAAECBAgQIECAQLoFBADTPT5Fta65ubnTfLfccku47LLLOk3zm9/8JtkxePTo0Z2m6+rNZWHVzlaOVAhs3tw7Fe3QiBAWNvj3Ii3fg4FN3dLSlLpvx02NxT2Ttu7hygTwmWUDy1SyYgsVuKzJvxuFmpUr/Y59PAO7XLZ1W26T/yes27HXcQJlEBAALANqNYucPHlyXtUvWrQoDBgwoN20zzzzTHjssceSAODOO+/cbhoXCRAgQIAAAQIECBAgQIAAAQIEakNAALA2xinvVk6cODHvtB0lXG+99cLUqVOT22t7VmBHZbhOgAABAgQIECBAgAABAgQIECCQDgEBwHSMQ6paMXz48BBfDgIECBAgQIAAAQIECBCokoBdgKsEr1oC2RTwcKNsjqteESBAgAABAgQIECBAgAABAgQIEEgEBAB9EQgQIECAAAECBAgQIECAAAECBAhkWMAS4AwPrq4RIECAAAECBAgQIECAQI0KNNsFuEZHTrMJpFLADMBUDkv1GzVlypTQ2NgYuncXI67+aGgBAQIECBAgQIAAAQIECBAgQKB4AdGd4u0yn7O5uTnzfdRBAgQIECBAgAABAgQIECBAgEDWBQQAsz7C+keAAAECBAgQIECAAAECtSdgF+DaGzMtJpBiAUuAUzw4mkaAAAECBAgQIECAAAECBAgQIECgqwJmAHZVMGX5jzvuuJK0aMaMGSUpRyEECBAgQIAAAQIECBAgQIAAAQLVFRAArK5/yWu//vrrQ0NDQ8nLLVeB2zb1LlfRyi1Q4MVuSwvMIXm5BAYGP5rLZVtoufeHeYVmkb5MAt1C7fy3rUwEqSr2+kb/bqRlQAZ365+WptR9O6Yvm1P3BgBKK9DcZBfg0ooqjUB9C/gtM6PjbwOPjA6sbhEgQIAAAQIECBAgQIAAAQIEChQQACwQLO3JBw0aFObNmxf222+/MGnSpKKbe/PNN4ezzjqr6PwyEiBAgAABAgQIECBAgEAXBGwC0gU8WQkQWF1AAHB1kRr/PGbMmHDXXXeF6dOnh+HDhxfdm8GDBxedV0YCBAgQIECAAAECBAgQIECAAIH0CNgFOD1jUZKWxABgPN54440wZ47nkJQEVSEECBAgQIAAAQIECBAgQIAAgRoWEACs4cFrr+ljx45tuTxt2rSWcycECBAgQIAAAQIECBAgUEMCcQlw2l41xKepBAi0FbAEuK1HzX/KBQDjJiAxADhhwoSi+jRixIgwceLEovLKRIAAAQIECBAgQIAAAQIECBAgkB4BAcD0jEVJWjJkyJDQVILt4seNGxfiy0GAAAECBAgQIECAAAECBAgQIFDbAgKAtT1+Wk+AAAECBAgQIECAAAECWRRobspir/SJAIEqCXgGYJXgVUuAAAECBAgQIECAAAECBAgQIECgEgICgJVQVgcBAgQIECBAgAABAgQIECBAgACBKglYAlwleNUSIECAAAECBAgQIECAAIEOBeIOwA4CBAiUSEAAsESQiilO4K7md4vLKFfJBfZfOajkZSqwOIH53fzPXnFypc/VKzSWvlAlEsiAwPaN62agF9nowi8/mJGNjmSgFwN79MtAL3SBAAECBLIqIACY1ZEtoF/PP/98mD59eujevXvYdNNNw6hRowrILSkBAgQIECBAgAABAgQIECBAgECaBQQA0zw6XWjbzJkzw/e+973w2GOPhWXLloWRI0eGL37xi2H8+PEtpb700kth4sSJ4amnnmq5Fk823njjcPnll4cjjzyyzXUfCBAgQIAAAQIECBAgQKAyAs2WAFcGWi0E6kRAADCDA33vvfeGQw45JCxZsqSldy+88EK45ZZbwnnnnRcuuuii8Oabb4Z99tknvPXWW6G5ue1yw9mzZ4ejjjoqvP322+H0009vKcMJAQIECBAgQIAAAQIECBAgQIBA7QkIANbemHXa4jlz5oRPfepT4YMPPmhJN3jw4DB37tzk8ze+8Y2wxx57hGuvvTYJAvbu3TsJ9u24445h+fLl4cEHHwy33npraGpqCueee2448MADw4gRI1rKckKAAAECBAgQIECAAAECBAgQIFBbAgKAtTVea23tpEmTwrx580JDQ0Mye+/8888P66yzTli0aFG4+OKLwxVXXBEuvfTS8Oijj4ZBgwYlAb+tt966pdxTTjkl/Pa3vw0HH3xwEhD84Q9/GL7zne+03M/3JM4idBAgQIAAAQIECBAgQIBAkQKWABcJJxsBAu0JCAC2p1LD1+66664k+Bdn7n33u99t6cmAAQOSQN5rr70WbrvttiRNfEZg6+BfLvEnPvGJ8LnPfS7EYGKcEVjMMWzYsLyyfXzox/NKJxEBAgQIECBAgAABAgQIECBAgEBxAt2KyyZXWgXibr7xiJt7tHccffTRLZcPOuiglvPVT+IzBOMxY8aM1W/5TIAAAQIECBAgQIAAAQIECBAgUEMCZgDW0GDl09S41Dcem2yySbvJW1/fcMMN200TL+buvf/++x2m6ezGrFmzOrvdcu+EnU9oOXdCgAABAgQIECBAgAABAn8TWPVcdgcBAgRKJSAAWCrJlJTTr1+/sHDhwmSDj7ixx+pH3PU3d8RnBQ4ZMiT3sc37/Pnzk899+/Ztcz3fD0OHDs03qXQECBAgQIAAAQIECBAgQIAAAQJlFLAEuIy41Sh68803T6r99a9/3W71ra/fe++97aaJF++5557k3vDhwztM4wYBAgQIECBAgAABAgQIECBAgED6BQQA0z9GBbXwX/7lX0Jzc3O4/vrrQ9zBt/URP0+ePDnssMMOobGxMXz9618Pc+bMaZ0kOX/hhRfCf/7nfyYbhey6665r3HeBAAECBAgQIECAAAECBMosEHcBTturzF1WPAEC5RMQACyfbVVK/uIXvxj69OkTmlY9L+Kkk04KG2ywQdhpp52S9/g5Xv/a174W4k6/M2fODKNGjQqXXXZZuPvuu8Odd94Zzj333BCDfvHZfw0NDcluwFXpiEoJECBAgAABAgQIECBAgAABAgRKIuAZgCVhTE8hccnupEmTwrHHHpsE++bOnRvefffdZFZgbGXcHfiwww4LW2+9dXjooYfC22+/ncwEbN2DOIMwHqeeemoyW7D1vVKf9+nWo9RFKq9IgQfCwiJzylZqgfUaepW6SOUVKdD0t5+HRWaXrYQCBzQNLGFpiuqqwK/C/z0ruKvlyN91gWG91u96IUooicCYRmNREkiFECBAgEBZBMwALAtrdQs9+uijw4MPPhj233//sN5664W4kcc//dM/hauvvjpcd911SeO22mqrEJ8HuNFGGyXBwRj0y71i+osvvjhcccUV1e2I2gkQIECAAAECBAgQIFCvAmlb/hvb4yBAoGYFzACs2aHrvOHjxo0Lv/nNbzpNtNtuu4U//elP4a677govvfRS6N69e9h0003D3nvvHdZdd91O87pJgAABAgQIECBAgAABAgQIECBQGwICgLUxTmVrZc+ePcNBBx2UvMpWiYIJECBAgAABAgQIECBAoCCB3KOZCsokMQECBDoQsAS4AxiXCRAgQIAAAQIECBAgQIAAAQIECGRBQAAwC6OoDwQIECBAgAABAgQIECBAgAABAgQ6ELAEuAMYlwkQIECAAAECBAgQIECAQNUEbLpRNXoVE8iigBmAWRzVEvRpypQpobGxMdkYpATFKYIAAQIECBAgQIAAAQIECBAgQKBKAmYAVgm+Fqr10NlaGCVtJECAAAECBAgQIECAAAECBAh0LiAA2LmPuwQIECBAgAABAgQIECBAoPIClgBX3lyNBDIsYAlwhgdX1wgQIECAAAECBAgQIECAAAECBAiYAZix78Bxxx1Xkh7NmDGjJOUohAABAgQIECBAgAABAgQIECBAoLoCAoDV9S957ddff31oaGgoebnlKnBlc1O5ilZugQKDuvUpMIfk5RLoGxrLVbRyCxQY3q1fgTkkL5fA693896JctsWU26+5RzHZ5CmDwBlL/ZwqA2tRRZ7U/GpR+WQqj8A3y1NsRUtttgS4ot4qI5B1AQHAjI6wDTwyOrC6RYAAAQIECBAgQIAAAQIECBAoUEAAsECwtCcfNGhQmDdvXthvv/3CpEmTim7uzTffHM4666yi88tIgAABAgQIECBAgAABAgQIECCQDgEBwHSMQ8laMWbMmHDXXXeF6dOnh+HDhxdd7uDBg4vOKyMBAgQIECBAgAABAgQIdFHAEuAuAspOgEBrAbsAt9bIwHkMAMbjjTfeCHPmzMlAj3SBAAECBAgQIECAAAECBAgQIECgKwICgF3RS2HesWPHtrRq2rRpLedOCBAgQIAAAQIECBAgQIAAAQIE6lPAEuCMjXsuABg3AYkBwAkTJhTVwxEjRoSJEycWlVcmAgQIECBAgAABAgQIEOiiQFMX88tOgACBVgICgK0wsnA6ZMiQ0NTU9f9SjBs3LsSXgwABAgQIECBAgAABAgQIECBAoLYFLAGu7fHTegIECBAgQIAAAQIECBAgQIAAAQKdCpgB2CmPmwQIECBAgAABAgQIECBAoPICzXYBrjy6GglkWMAMwAwPrq4RIECAAAECBAgQIECAAAECBAgQEAD0HSBAgAABAgQIECBAgAABAgQIECCQYQFLgDM8uLXQtZ4NjbXQzLpo459WvFcX/ayFTm7Xff1aaGZdtPG5FfProp+10MmJzRvUQjPrpo2LevSum76mvaOX9VyQ9ibWTft2bRxeN33V0QoJWAJcIWjVEKgPATMA62OcC+rlI488Eo477rhw/PHHF5RPYgIECBAgQIAAAQIECBAgQIAAgfQJCACmb0yq3qIZM2aE66+/PnlVvTEaQIAAAQIECBAgQIAAAQIECBAg0CUBS4C7xCczAQIECBAgQIAAAQIECBAog0BTGcpUJAECdStgBmDdDr2OEyBAgAABAgQIECBAgAABAgQI1IOAGYAZGuWHHnqoJL2ZPn16l8uZPXt2l8tQAAECBAgQIECAAAECBOpVoNkmIPU69PpNoCwCAoBlYa1OoXvuuWdoaGioTuWr1Tps2LDVrrT/8fBNDmr/hqsECBAgQIAAAQIECBAgQIAAAQIlERAALAljugppbm5OV4O0hgABAgQIECBAgAABAgQIECBAoGoCAoBVoy99xT179gzLly8P2223XTj00EOLruCZZ54Jv/rVr4rOHzPOmjUrr/ynjzs5r3QSESBAgAABAgQIECBAoK4EbAJSV8OtswTKLSAAWG7hCpYfA39PPvlk6NGjR7jggguKrnnKlCldDgAOHTq06PplJECAAAECBAgQIECAAAECBAgQKJ2AXYBLZ1n1ksaMGZO04fnnnw/Lli2rens0gAABAgQIECBAgAABAgQIECBAoPoCAoDVH4OStWDs2LFJWXEZcFzG6yBAgAABAgQIECBAgACB2hSIuwCn7VWbklpNgEAUEADM0PcgFwCMXZo2bVqGeqYrBAgQIECAAAECBAgQIECAAAECxQp4BmCxcinMt+WWW4brr78+xF2At95666JbOHHixBBflTiGN/StRDXqyEOgV2NjHqkkqYTAus3GohLO+dRxQLcN8kkmTQUEXurmSegVYM67ijnNS/NOK2F5BXbtNqi8FSg9b4FZwSN48saSkAABAgQqLmAGYMXJy1dhQ0NDOOaYY5LgXe55gOWrTckECBAgQIAAAQIECBAgUDaB+LevtL3K1tm/F/z666+HM888M8QJLv369Qvrr79+iL/ffuc73wkffPDB3xOW8CyWu9lmm4X4O3V8ffSjH+209D333LMlbS5PR++dFuQmgQoKmAFYQWxVESBAgAABAgQIECBAgAABAu0L/OY3vwlHHXVUWLhwYUuCGJx78sknk9e1114b7rjjjjBixIiW+6U4Of/888Of//znUhSlDAKpFRAATO3QaBgBAgQIECBAgAABAgQIEKgPgaeffjr827/9W1iyZEno379/OPfcc8P48eOTzz//+c/DNddcE1555ZWw//77J8HAAQMGlAQm1nvllVeG3r17hx49eoRFixblXe7o0aPD5MmT804vIYFqCggAVlNf3QQIECBAgAABAgQIECBAoB2B5jp7/O2pp56aBPu6d+8e7r777rDzzju3qOy1117hH//xH8PZZ5+dBAGvuOKKcOGFF7bcL/Zk5cqV4YQTTgjx/YILLgjXXXddQQHAuET5Yx/7WLHVy0egogKeAVhRbpURIECAAAECBAgQIECAAAECrQWeeOKJ8PDDDyeXjj/++DbBv1y6+FzArbbaKvl41VVXheXLl+duFf0ey3nqqafCyJEjwznnnFN0OTISqAUBAcBaGKUi2vi73/0unHbaaeHAAw8MhxxySDJ9Ok5tzud49dVXkwegbr755vkkl4YAAQIECBAgQIAAAQIECBQtcNttt7XkPfbYY1vOW59069Yt2fQyXluwYEGYOnVq69sFn8fNRuKz/+IxadKk0LNnz4LLkIFALQlYAlxLo5VHW+NfQeIPzJ/97GdtUseHqX77298Ohx12WPjv//7vMHjw4Db3W39YtmxZmDlzZrKrUevrzgkQIECAAAECBAgQIECgQgJ1tAQ4TmCJR1xSu+OOO3YIvMcee7Tce+SRR8K+++7b8rnQk5NOOiksXrw4HH300SHu6usgkHUBMwAzNsLx+QU//elPQ3Nzc7uvW265JWy33XYh/rB0ECBAgAABAgQIECBAgACBagu89NJLSRPi7r7xGYAdHVtuuWXLrVyelgsFnMRNRe68886w3nrrhfg8wWKP6dOnh5122ikMHDgw2URk6NCh4eCDDw433HBDSZYoF9su+Qi0JyAA2J5KjV6LQb34g6ahoSFsscUWIc76e//998O7774bYuBv7NixSVDw7bffTv5Scvvtt9doTzWbAAECBAgQIECAAAECBCotMHv27JDPq5B2ffjhh2Hu3LlJlhhA6+yIAbs4SzAes2bN6ixph/fmz5+fPC4rJrjsssvCkCFDOky7tht//etfQ3x+4XvvvReWLl0a/vKXv4Rf//rXYeLEiWHUqFGhK0HKtdXtPoFCBToOrRdakvRVF4g7FsVj4403Do8++mhYf/31k899+/YNhx56aPIswO9973vJ8wDj1uqf/OQnky3LP/OZzyTp/IMAAQIECBAgQIAAAQIE0iGQxl2Ahw0blhdOXJGW77Fo0aKWpP3792857+gkBgDj0t042aWY46yzzgoxcBd3GY4r6Io54vMI99577zBhwoSw/fbbh0GDBiW7B//hD38IP/zhD5PA34svvhjGjx+fBAg32WSTYqqRh0BJBQQAS8pZ3cLiDMA4+y/ujpQL/rVuUe7e6NGjk+Bf/MtH/MtE/IH7hS98oXVS5wQIECBAgAABAgQIECBAoOwCcQZg7shnI45evXolyeOklkKPhx56KPzP//xPssw4bvwRf0cu5vjlL3+ZLPtdPe9uu+0W4rMFY2BxypQpSaAxbs4Z0zsIVFtAALDaI1DC+t98882ktPiXjM6O+ODU+INvv/32CzHPySefnPz15Ctf+Upn2cpyb71mq9DLAltEoR9t6ltELlnKIfDHxmXlKFaZRQj8uWFlEblkKYdA39BYjmKVWaTAPiv+b/lVkdllK6HAIz3+/otzCYtVVBECuy/vXUQuWQjUlkCxy24762Xv3n//dyduSLm2Iy61jUefPn3WlrTN/ZjvxBNPTB6LdeqppybPxm+ToIAP8Zl/HR09evQI1157bXj88cfDyy+/HG699dZkaXBcqecgUE0B0Zdq6pe47rgDcDwaG9f+S9I222wTHn744bDpppsmPwDPOeeccMEFF5S4RYojQIAAAQIECBAgQIAAgaIE4i7AKXvFZ/Tl8yqkvwMGDGhJns+y3rj8Nx75LBduKXjVyTe/+c0kIBeXMV900UWtb5X8PG5kcvzxx7eU++CDD7acOyFQLQEzAKslX4Z648NL44y+N954o9Ot03NVx+BfDALus88+yTMKvvGNbyQzAY877rhcEu8ECBAgQIAAAQIECBAgQKBsAnEGYHyGXty8Mm4w0tkRH2OVCwDm+zzCXHmXX355cvov//IvyYaZueut33Nlx/e4U3A8Nthgg7DXXnu1TpbX+dZbb92SLm4O4iBQbQEBwGqPQAnrjz9gYgAwBvXiph/5HB/5yEeS5cD77rtvePrpp8OVV14ZnnvuuXyySkOAAAECBAgQIECAAAECBLosEH+Xjb/HzpgxI6xYsSJ5Rl97hU6fPr3l8lZbbdVyns9Jbnnx5MmTk80wO8sTdyU+8sgjkyTxEVrFBACLfb5gZ+1yj0BXBCwB7opeyvLuuuuuyXLeX/ziF8l7vs2Lf22ZOnVq2GWXXZJ8999/f75ZpSNAgAABAgQIECBAgACBMgjEXYDT9ipDN5Mi4++y8Ygz75566qnkvL1/tF5KO27cuPaSpOZa3AU4d8SJNw4C1RYQAKz2CJSw/rgFeTziLMBCdxlaZ511wj333JMsBy5ky/YSNl9RBAgQIECAAAECBAgQIFCHAoccckhLr+MMvfaOpqamcMMNNyS34iYc48ePby9Zh9fi77lrew0fPjzJH99zaR944IEOy+zoRpzFGHcbzh2777577tQ7gaoJCABWjb70Fe+4444h/uVks802S7YcL7SGuIvS7bffnvfy4ULLl54AAQIECBAgQIAAAQIE8hNI2+y/2J5yHWPHjg277bZbUvx1110XHnvssTWquuKKK5Jn18cbcRffuNtu6yMG6uKy2/j67Gc/2/pWSc/j6rkFCxZ0WGbcnPNzn/tcS1sPPPDAUOjzCjss3A0CXRDwDMAu4KUx60MPPdSlZsUforfcckuXypCZAAECBAgQIECAAAECBAgUInDVVVeFuKx3yZIlIT6j/mtf+1oyyy9+jhtyXH311UlxW2yxRTjzzDMLKbqkaadMmRIOOuig5LXnnnuGkSNHhriiLu5gHJcvx3bmlv/GDURivxwE0iAgAJiGUdAGAgQIECBAgAABAgQIECBQxwI77LBDuPHGG8NRRx0VFi5cmAQAV+eIwb877rgjDBgwYPVbFf0cg30//elPk1dHFW+77bZJ4HLTTTftKInrBCoqIABYUW6VESBAgAABAgQIECBAgACBtQuUc8nt2muvToq4XPa5555LZs3FQN/s2bNDz549w4gRI8IRRxwRTjnllNC3b9/qNO5vtZ5zzjlh1KhRyTLlONNvzpw5Yd68eaFXr15hww03DKNHjw6HH3548mitxsbGqrZV5QRaCzSserBlc+sLzglUUuCbwz9TyerU1YnAuk0Nndx1q5ICf2xcVsnq1NWJwAdhZSd33aqkQN/gf6Ar6b22usYu77m2JO5XSOCRHh9WqCbVrE1g9+W915bE/QoKHPOXH1ewtvJU9dfxe5Sn4C6UuuHUB7uQW1YCBKopYBOQauqrmwABAgQIECBAgAABAgQIECBAgECZBSwBLjNw2ouPOxhdcsklyU5J9913X8WbO7VpbsXrVGH7Ahs39m//hqsVF9h5ZZ+K16nC9gUOHTGr/RuuVlzg0j9vVPE6VdixwJSGtzu+6U5FBT6z4h8qWp/KOhaYb6JyxzjuFCfQbIVOcXByESDQnoAAYHsqdXTtnXfeCbnt0uuo27pKgAABAgQIECBAgAABAgQIEKgbAUuA62aodZQAAQIECBAgQIAAAQIECBAgQKAeBcwAzNCob7bZZgX3ZvHixS15Vs/f0NAQXnvttZb7TggQIECAAAECBAgQIECgMgL1uAtwZWTVQqA+BQQAMzTuM2fOTJ7lV8zGzjFPzN/6iAFABwECBAgQIECAAAECBAgQIECAQG0LCADW9vi12/p11lknrLfeeu3eW/1inAE4d+7cJHC4ySabrH676M+zZ88uOq+MBAgQIECAAAECBAgQIECAAIH/z96dwEdV3Qsc/08WIBBQZIlKohKQRYuCBBQhAiLUBgVBpVDRyF7QVhBxpQrWVlEpYtVSFEFaNIIRCwQRFIWwVARFdpcg+w4GEgiBZOblHDvzJslkMjOZ5d6Z332feblz7lm/Byn8OeceBPwnQADQf5Yhr+mKK67Qq/jOnz8vQ4cOlccee0xiYtxP8fvvvy8DBgzQff/pp5/8NoakpCSP6uqW2MOjfGRCAAEEEEAAAQQQQAABBCJJwGZlR1YkzTdjRSDQAhwCEmjhINa/detWefjhh+XcuXPy9NNPy7XXXivZ2dlB7AFNIYAAAggggAACCCCAAAIIIIAAAggYTYAAoNFmpAr9iYuLk5dfflnWrVsnbdq0ke3bt0uXLl1k0KBBcvz48SrU7H3RvXv3iicf72umBAIIIIAAAggggAACCCCAAAIIIICANwLu94d6UxN5DSOggn8qCPjKK6/IM888I7Nnz5aFCxfKpEmTZMiQIUHpZ2JiYlDaoREEEEAAAQQQQAABBBBAIBwFOAU4HGeVMSEQOgFWAIbOPqAtR0VF6e3AalvwrbfeKidOnJDhw4dLamqqqDQuBBBAAAEEEEAAAQQQQAABBBBAAIHIECAAGObzrE72zcrKknfffVcaNmwoq1evluuuu04fEFJQUBDmo2d4CCCAAAIIIIAAAggggAACCCCAAAIEACPk10D//v31OwEHDx4sRUVF+l2BLVu2lGXLlkWIAMNEAAEEEEAAAQQQQAABBMwjYLNZxGgf8+jRUwQQKCtgsZVcZRP5Ht4CK1eu1NuBv//+e7FY1P+o2PTP4uLioA/810m/CXqbNOha4KKo6q4fkBp0AWvQW6TBigRSrfEVPSI9yAKLLSeC3CLNuRMo5o+P7niC+iy65M9yXMYQSI6qbYyO0Ast8Nqu900vsb/DzYYbQ6O1yw3XJzqEAAKeCbAC0DOnsMp10003yaZNm2T8+PESGxsbVmNjMAgggAACCCCAAAIIIIAAAggggAACpQUIAJb2iJhv1apVk2effVbOnj0rVqtVQrH6L2KwGSgCCCCAAAIIIIAAAggg4KWAOgXYaB8vh0B2BBAwkAABQANNBl1BAAEEEEAAAQQQQAABBBBAAAEEEEDA3wIEAP0tSn0IIIAAAggggAACCCCAAAIIIIAAAggYSCDGQH2hKwgggAACCCCAAAIIIIAAAgggUCJgs3LID78QEEDAfwIEAP1naaiaVq1aJR988IHk5ORIdHS0tGzZUvr16ydt2rSptJ8//PCD/PrXv9YnA6vyXAgggAACCCCAAAIIIIAAAggggAAC5hUgAGjeuXPZ8/Pnz8ugQYPkvffeK/V84cKF8uKLL8qdd94pb7zxhtSvX7/Uc+cv586dk127dukAoHM69wgggAACCCCAAAIIIIAAAsERsNmC0w6tIIBAZAjwDsAwm+dhw4bJu+++K7aS/7Vw9cnMzJRrrrlGVq9eHWYjZzgIIIAAAggggAACCCCAAAIIIIAAAq4ECAC6UjFpmgrqzZ49W6/ca9asmahVf/n5+XL8+HFRgb/27dvroOChQ4ekR48esmjRIpOOlG4jgAACCCCAAAIIIIAAAggggAACCHgqQADQUykT5JsxY4buZaNGjWTNmjXSs2dPqVmzptStW1f69Okja9eulZdeekliYmKkoKBA+vbtK3PmzDHByOgiAggggAACCCCAAAIIIBBZAuoQEKN9ImsGGC0C4SVAADCM5lOtALRYLDJ27Fi56KKLyo3M/mzZsmU6KFhUVCTp6ekybdq0cnlJQAABBBBAAAEEEEAAAQQQQAABBBAIDwEOAQmPedSjOHDggP7ZoUMHt6Pq3LmzrFy5Up/0q8o88MADeqvwI4884rZcIB5eG31hIKqlTh8E8qTYh1IUCYTAruK8QFRLnT4I/BBdw4dSFAmEwJVSJxDVUqePAjut+T6WpJi/BRKi+H3K36a+1jf/5BZfi1IuAAKvBaBOqkQAAQTMLMAKQDPPXpm+qxOA1RUdHV3mSfmvV199tWRnZ0vjxo31ewEfe+wxeeaZZ8pnJAUBBBBAAAEEEEAAAQQQQCDoAkbb/qv6w4UAAuYVIABo3rkr1/MGDRrotD179pR75ipBBf9UELBly5Y6CPjcc8/p7cOu8pKGAAIIIIAAAggggAACCCCAAAIIIGBOAQKA5pw3l72+6qqrdLoK6nl6XXrppXo7cJs2bXQQ8JVXXpHRo0d7Wpx8CCCAAAIIIIAAAggggAACCCCAAAIGFyAAaPAJ8qZ7nTp10kG8efPm6Z+elq1Xr558/vnncuONN+pyy5cv97Qo+RBAAAEEEEAAAQQQQAABBAIgYLNJyd/PjPUJwDCpEgEEgiRAADBI0MFoJi0tTTejDvb48MMPvWqyTp06ok4H7t69u1fBQ68aITMCCCCAAAIIIIAAAggggAACCCCAQNAFCAAGnTxwDbZt21bUKsDk5GR55513vG4oLi5OFi1aJH369PG6LAUQQAABBBBAAAEEEEAAAQQQQAABBIwpEGPMbtErXwVWrlzpa1FdLjY2VjIzM6tUB4URQAABBBBAAAEEEEAAAQSqJsCpu1XzozQCCJQWYAVgaQ++IYAAAggggAACCCCAAAIIIIAAAgggEFYCBADDajoZDAIIIIAAAggggAACCCCAAAIIIIAAAqUF2AJc2oNvCCCAAAIIIIAAAggggAACCIRcwGazhLwPdAABBMJHgBWA4TOXjAQBBBBAAAEEEEAAAQQQQAABBBBAAIFyAqwALEdCQjAF9tnOBrM52nIjcLGlupunPEIgMgW6nuXfyYwy80djmAujzIXqxzrbaSN1J6L7UmA7H9HjN9LgH49vY6Tu0BcEEEAAAQRKCfCn6VIc5v6SnJwsaWlp8v7770thYaG5B0PvEUAAAQQQQAABBBBAAIEIFrBZRYz2ieDpYOgImF6AAKDpp/D/B7Br1y755JNP5He/+50kJCTIsGHDJDs7+/8zcIcAAggggAACCCCAAAIIIIAAAgggEHECBADDcMptNpucOnVK3n77benSpYs0adJEJk6cKDt37gzD0TIkBBBAAAEEEEAAAQQQQAABBBBAAAF3AgQA3emY9JlaAXjppZeKCgSqj1oZ+Oyzz8qVV14pqamp8tZbb+kAoUmHR7cRQAABBBBAAAEEEEAAgbAXsJacAmy0T9ijM0AEwliAAGAYTu6TTz4pe/bskSVLlujtwHFxcY5g4Jo1a2TEiBFy8cUXS//+/WXx4sVitZa8XMLP1759+8STj5+bpToEEEAAAQQQQAABBBBAAAEEEEAAgTICnAJcBiRcvkZFRUmPHj30Jz8/Xz744AOZPXu2rFixQgcDz549K/PmzdOfhg0byj333CP33XefXHPNNX4hSEpK8qieAZfd4VE+MiGAAAIIIIAAAggggAACCCCAAAII+CbACkDf3ExVKj4+Xu6//35Zvny53g783HPPSYsWLRyrAg8fPixTpkyRNm3aSOvWreWVV16RI0eOmGqMdBYBBBBAAAEEEEAAAQQQCCcBW8kWYKN9wsmXsSAQaQIEACNsxtXKPLVFeNu2bfLll1/KqFGjpF69eo5g4ObNm2Xs2LGSmJgot912m886e/fuFU8+PjdAQQQQQAABBBBAAAEEEEAAAQQQQAABjwTYAuwRU3hmateunaiPWvGXlZWltwirn+fOnZOioiL5+OOPfR64CiByIYAAAggggAACCCCAAAII+CZgs1p8K0gpBBBAwIUAKwBdoERaUkxMjPTu3VsyMzPl4MGD8vrrr0v79u0jjYHxIoAAAggggAACCCCAAAIIIIAAAmEpQAAwLKfV90HVrVtXRo4cKf/973/lu+++870iSiKAAAIIIIAAAggggAACCCCAAAIIGEKALcCGmAZjdqJp06bG7Bi9QgABBBBAAAEEEEAAAQTCXMBmC/MBMjwEEAiqAAHAoHLTWFmB2hZ+CZY1CdX3GOEdI6GyL9vu1dEXlk3ie4gE1kbzJ+8Q0ZdrNqm4XBIJIRSoZakWwtZp2lngRNFp56/ch1Agt1r9ELZO0wgggAACCLgXIPri3sdUT3/66Sfd30aNGpmq33QWAQQQQAABBBBAAAEEEEAAAQQQQCBwAgQAA2cb9Jovv/zyoLdJgwgggAACCCCAAAIIIIAAAv4X4BRg/5tSIwKRLMAhIJE8+4wdAQQQQAABBBBAAAEEEEAAAQQQQCDsBQgAhv0UM0AEEEAAAQQQQAABBBBAAAEEEEAAgUgWYAtwJM8+Y0cAAQQQQAABBBBAAAEEEDCkgNXGIX2GnBg6hYBJBQgAmnTiKuv2qlWr5IMPPpCcnByJjo6Wli1bSr9+/aRNmzaVFZUffvhBfv3rX4vFYtHlKy1ABgQQQAABBBBAAAEEEEAAAQQQQAABwwoQADTs1PjWsfPnz8ugQYPkvffeK1XBwoUL5cUXX5Q777xT3njjDalfv36p585fzp07J7t27dIBQOd07hFAAAEEEEAAAQQQQAABBBBAAAEEzCfAOwDNN2duezxs2DB59913xWazufxkZmbKNddcI6tXr3ZbDw8RQAABBBBAAAEEEEAAAQRCJ2Ar2QJstE/oNGgZAQSqKkAAsKqCBiqvgnqzZ8/WK/eaNWsmatVffn6+HD9+XFTgr3379jooeOjQIenRo4csWrTIQL2nKwgggAACCCCAAAIIIIAAAggggAACgRAgABgI1RDVOWPGDN1yo0aNZM2aNdKzZ0+pWbOm1K1bV/r06SNr166Vl156SWJiYqSgoED69u0rc+bMCVFvaRYBBBBAAAEEEEAAAQQQQAABBBBAIBgCBACDoRykNtQKQHVwx9ixY+Wiiy4q16r92bJly3RQsKioSNLT02XatGnl8pKAAAIIIIAAAggggAACCCAQOoGStzqV7OAy1id0GrSMAAJVFeAQkKoKGqj8gQMHdG86dOjgtledO3eWlStX6pN+VZkHHnhAbxV+5JFH3JYLxMPTtuJAVEudPggkWKr5UIoigRDYZssLRLXU6YPALdY6PpSiSCAEtkWfD0S11OmjQJLE+1iSYv4WuDCqur+rpD4fBZYXH/GxJMUCIfCnQFRKnQgggICJBVgBaOLJK9t1dQKwuqKjo8s+Kvf96quvluzsbGncuLF+L+Bjjz0mzzzzTLl8JCCAAAIIIIAAAggggAACCCCAAAIImFuAAKC5569U7xs0aKC/79mzp1R6RV9U8E8FAVu2bKmDgM8995zePlxRftIRQAABBBBAAAEEEEAAAQSCI2AtOQXYaJ/gjJxWEEAgEAIEAAOhGqI6r7rqKt2yCup5el166aV6O3CbNm10EPCVV16R0aNHe1qcfAgggAACCCCAAAIIIIAAAggggAACBhcgAGjwCfKme506ddJBvHnz5umfnpatV6+efP7553LjjTfqcsuXL/e0KPkQQAABBBBAAAEEEEAAAQQQQAABBAwuQADQ4BPkTffS0tJ0dnWwx4cffuhNUalTp46o04G7d+/uVfDQq0bIjAACCCCAAAIIIIAAAggg4JGArWQLsNE+HnWcTAggYEgBAoCGnBbfOtW2bVtRqwCTk5PlnXfe8bqSuLg4WbRokfTp08frshRAAAEEEEAAAQQQQAABBBBAAAEEEDCmQIwxu0WvfBVYuXKlr0V1udjYWMnMzKxSHRRGAAEEEEAAAQQQQAABBBBAAAEEEDCOAAFA48wFPUEAAQQQQAABBBBAAAEEEEBAC9hsQCCAAAL+E2ALsP8sqQkBBBBAAAEEEEAAAQQQQAABBBBAAAHDCbAC0HBTQocQQAABBBBAAAEEEEAAAQQiXcBacggIFwIIIOAvAVYA+kuSehBAAAEEEEAAAQQQQAABBBBAAAEEEDCgACsADTgpkdSl82KNpOEaeqznhZeMGGWC6liqGaUrEd+PebYjEW9gFIDfWBsapSv0o0SgrjUWB4MIrI5hhZBBpkKqRUcbpSv0AwEEEEAAgXICBADLkYRXwunTp2X9+vWyfft22bt3r+Tn50tBQYHExcVJfHy8JCUlScuWLSUlJUVq1aoVXoNnNAgggAACCCCAAAIIIICASQVsbAE26czRbQSMKUAA0JjzUuVerVy5Ul566SX57LPPpLCwsNL6qlevLrfccouMGzdOUlNTK81PBgQQQAABBBBAAAEEEEAAAQQQQAABcwjwDkBzzJPHvTx37pwMHDhQunbtKosXL5azZ8+KreT8+Mo+Kl9WVpZ06dJF7r33XlH1cCGAAAIIIIAAAggggAACCCCAAAIImF+AFYDmn8NSI7jzzjt14E8F/KJL3kOiVvV17txZWrRoobf7qm2+arWfWhWotgerbcE7duyQFStWyKeffirFxcXy7rvvysmTJ2XBggWl6uYLAggggAACCCCAAAIIIIBAcAQ4BTg4zrSCQKQIEAAMo5nOyMjQq/gsFovcfvvt8sYbb0ijRo3cjrBt27b6+eOPPy779u2TUaNGyaJFi3Q9c+fOlX79+rktX9FDVRcXAggggAACCCCAAAIIIIAAAggggEDoBQgAhn4O/NaDWbNm6brU9t+PPvpIVCDQmysxMVGXU6sGv/jiC3n77bd9DgCqw0U8ufpd1tuTbORBAAEEEEAAAQQQQAABBBBAAAEEEPBRgHcA+ghnxGLffvutDvo99NBDXgf/7OOJioqSMWPG6K+qPi4EEEAAAQQQQAABBBBAAIHgC9hKmjTaJ/gKtIgAAv4SYAWgvyQNUE9ubq7uxaWXXlql3tjL2+vzpTL1bkFPrrEdH/QkG3kQQAABBBBAAAEEEEAAAQQQQAABBHwUIADoI5wRiyUkJOhDPbZs2SL2d/v50s/NmzfrYqo+Xy+1nZgLAQQQQAABBBBAAAEEEEAAAQQQQCD0AmwBDv0c+K0Hqampok7//ctf/iI///yzT/Wqcqq8en+gqo8LAQQQQAABBBBAAAEEEEAg+ALqFGCjfYKvQIsIIOAvAQKA/pI0QD3qBF/1Dr+cnBy57rrrZN68eVJUVORRz1Q+deqvWjmoyqsA4AMPPOBRWTIhgAACCCCAAAIIIIAAAggggAACCBhXgC3Axp0br3vWoUMHGT9+vDz77LOyZ88e6d+/v9SpU0dUeosWLURty42Pj5dq1arJuXPnJD8/X/bt2yc7duyQtWvXyqlTp/QKQtXwn/70J7nhhhu87gMFEEAAAQQQQAABBBBAAAEEEEAAAQSMJUAA0FjzUeXeTJgwQS655BJ5/PHH5eTJk/rzySefiPq4u9TWYXVdcMEFMmnSJBk+fLi77H571tRS0291UVHVBH6yFVStAkr7TeBw8Wm/1UVFVROoE1W9ahVQ2m8CZyy//O+U3yqkoioJfB19pkrlKew/gdrCH+f9p1m1mq4vrlW1CiiNQBkBW8kWYC4EEEDAXwJsAfaXpIHqGTFihOzcuVMH8tTqP7UtWAX4Kvqo5yqfCvypcsEK/hmIjK4ggAACCCCAAAIIIIAAAggggAACYSvAPxmG6dTWrVtXxo0bpz/nz5+X77//Xm/3zcvLk7Nnz0qNGjWkdu3aeltws2bNJDY2NkwlGBYCCCCAAAIIIIAAAggggAACCCAQ2QIEACNg/lVw7+qrr9afCBguQ0QAAQQQQAABBBBAAAEETC9gNf0IGAACCBhJgC3ARpoN+oIAAggggAACCCCAAAIIIIAAAggggICfBVgB6GdQM1WXm5srS5Yskd27d+vTgVu1aiWpqalisfCyWTPNI31FAAEEEEAAAQQQQAABBBBAAAEE3AkQAHSnY9JnmzdvlokTJ0p2drZ+31+LFi1kzJgx0r9/f8eIZs+eLQ8++KCcPl36tNHk5GSZPn26dO3a1ZGXGwQQQAABBBBAAAEEEEAAgeAK2ISFGcEVpzUEwluALcBhNr9Lly6V9u3by/z58+Xo0aOiDv1Yv3693HPPPTJ+/Hg92qysLBkyZIjk5+eXOxk4JydH0tLSZPny5WEmw3AQQAABBBBAAAEEEEAAAQQQQACByBQgABhG837ixAkZOHCgFBYW6sBe8+bNJSUlRaKjo/X3559/XjZs2CCjR48Wq9Uq/fr1kzlz5ogKGs6cOVMH/hSHKj948GC9ejCMeBgKAggggAACCCCAAAIIIIAAAgggEJECbAEOo2mfMWOGHDt2TGJiYmTevHnSu3dvPbrvvvtOunTpIkeOHJHhw4fLzp07RW0BVqsCna/09HSZMmWKjB07Vvbu3Stz586V++67zzkL9wgggAACCCCAAAIIIIAAAkEQsNqC0AhNIIBAxAiwAjCMpnrx4sX6AI++ffs6gn9qeGol4Lhx4/QqwI0bN0qfPn3KBf/sDOpdgR07dtRfFy1aZE/mJwIIIIAAAggggAACCCCAAAIIIICASQUIAJp04lx1e9u2bTr5rrvuKve4V69ejjS19dfdpVYG2mw2UcFCLgQQQAABBBBAAAEEEEAAgeALWEsOATHaJ/gKtIgAAv4SYAuwvyQNUE9ubq7uRWJiYrneNGrUyJHWpEkTx72rm1atWulktWU40FfrQk62CrSxp/WfqV7d06zkC7CALZr9HgEm9rj6M7Yij/OSMbACuyyFgW2A2r0T4Lcp77wCmPun4rwA1k7V3ggMLOI/DG+8yIsAAgggEFwBVgAG1zugrcXFxen6o6LKT6v9mcrgfO+qQ7Vr19bJBQUFrh6ThgACCCCAAAIIIIAAAggggAACCCBgIgFWAJposirraoMGDSQvL08OHjxYWVa3z/Pz8/XzevXquc3HQwQQQAABBBBAAAEEEEAAgcAI2Eq2AHMhgAAC/hIgAOgvSQPUo7b5qhN+1Qm+rq6ZM2fqZFdbhJ3z5+Tk6K8JCQnOydwjgAACCCCAAAIIIIAAAggggAACCJhQgACgCSetoi6npKRIdna2fP311y6zpKenu0wvm7hq1Sqd1LRp07KP+I4AAggggAACCCCAAAIIIIAAAgggYDIBAoAmmzB33W3Xrp0+vXf58uXusrl9VlhYKJmZmWKxWKRjx45u8/IQAQQQQAABBBBAAAEEEEAgMALWwFRLrQggEKECBADDaOLT0tJk2bJlekQ2W8kbI0qCeN5eGRkZog4BUZ+ePXt6W5z8CCCAAAIIIIAAAggggAACCCCAAAIGEyAAaLAJqUp3VNCuW7duValC1DZhT7cKV6khCiOAAAIIIIAAAggggAACCCCAAAIIBEWAAGBQmGkEAQQQQAABBBBAAAEEEEAAAc8FOAXYcytyIoBA5QJRlWchBwIIIIAAAggggAACCCCAAAIIIIAAAgiYVYAAoFlnjn4jgAACCCCAAAIIIIAAAggggAACCCDggQBbgD1AIgsCCCCAAAIIIIAAAggggAACwRTgFOBgatMWAuEvQAAw/OfY0CNcVP2sofsXSZ0rLjk5mssYAm1t8cboCL2QRkX8d2GUXwZZsQVG6Qr9KBGoaYnGwSACcRb+OG+QqZDp1QnXGGUuVD9SjdQZ+oIAAggYQIA/MRhgEkLZhYMHD8qePXskPj5emjZtKtWrVw9ld2gbAQQQQAABBBBAAAEEEEAAAQQQQMDPAgQA/QxqhOoKCgpkxowZkp2dLWfPnpUWLVrIyJEj5YorrnB0b/PmzTpt7dq1jrS4uDi5++675YUXXpCEhARHOjcIIIAAAggggAACCCCAAALBFWBNaXC9aQ2BcBcgABhmM7xr1y7p3r277Ny50zGyRYsWyauvvioZGRnSu3dv+fHHH6Vr167y888/i81p2+eZM2dk9uzZsnz5clmxYkWpgKGjMm4QQAABBBBAAAEEEEAAAQQQQAABBEwlQADQVNPlvrNFRUU6wJeTk1MuY2Fhodx7772yfft2GTx4sJw4cUKSkpLk1ltvlQYNGsj+/fvl448/liNHjsjevXt1XrWCkAsBBBBAAAEEEEAAAQQQQAABBBBAwNwCBADNPX+leq9W+KmtvRaLRf74xz/K448/LrVr15b33ntPRo0aJadPn5ZHH31UVq1aJb///e9l6tSpEhsb66hDrQAcNGiQzJs3T9asWSNLly6VHj16OJ57c7Nv3z5vspMXAQQQQAABBBBAAAEEEEDAScAmFqdv3CKAAAJVEyAAWDU/Q5VWgTt13XjjjTJlyhRH34YMGSJbt26VV155RW8Dbtu2rbz++us6UOjIVHJTs2ZNvQV4w4YN8tNPP+lAoK8BQLW60JPrvsv7epKNPAgggAACCCCAAAIIIIAAAggggAACPgpE+ViOYgYU+Oabb3RQT63iK3vdd999jqRhw4aVC/7ZH6pTgFXAUL0bcP369fZkfiKAAAIIIIAAAggggAACCCCAAAIImFSAFYAmnThX3T527JhObtKkSbnHzicAX3PNNeWeOye0b99ef92zZ49zslf36j2CnlxPdXrIk2zkQQABBBBAAAEEEEAAAQQiSsDKDuCImm8Gi0CgBQgABlo4iPWrd/+pS733r+x14YUXOpLq1KnjuHd1U79+fZ2cn5/v6rFHaYmJiR7lIxMCCCCAAAIIIIAAAggggAACCCCAQGAFCAAG1jeotavTfNXKu8OHD1epXXVisLpcBRKrVDGFEUAAAQQQQAABBBBAAAEEPBKwcgiIR05kQgABzwQIAHrmZIpcCQkJOgB48OBBl/1V7wFUqwTr1q3r8rk9cf/+/fpW1ceFAAIIIIAAAggggAACCCCAAAIIIGBuAQKA5p6/Ur2/9tpr9cEdGzduLJVu/zJr1iz7rduf69at088vv/xyt/l4iAACCCCAAAIIIIAAAggggAACCCBgfAECgMafI497qA7veOutt2TVqlUel3GVMTMzU68UtB8G4iqPv9IOFZ/xV1XUU0WBa6P//z2RVayK4lUU+Ep8f/9mFZumeBmBH6rFlknha6gEotkGFSp6l+1WkyiX6SQGX+AYf5YKPnoFLU6M4b+LCmhI9lHA5mM5iiGAAAKuBAgAulIxaVqPHj3kqaeekqioKLFarfqnt0P58MMP5ccff9QBQFUfFwIIIIAAAggggAACCCCAAAIIIICAuQUIAJp7/kr1/rLLLpM///nPpdK8/dK5c2f56aefdDFVHxcCCCCAAAIIIIAAAggggAACCCCAgLkFCACae/783vt69eqJ+nAhgAACCCCAAAIIIIAAAgiETsAauqZpGQEEwlCAF1WE4aQyJAQQQAABBBBAAAEEEEAAAQQQQAABBOwCrAC0S0Tgz9zcXFmyZIns3r1b4uPjpVWrVpKamqrf/xeBHAwZAQQQQAABBBBAAAEEEEAAAQQQCEsBAoBhOK2bN2+WiRMnSnZ2tpw9e1ZatGghY8aMkf79+ztGO3v2bHnwwQfl9OnTjjR1k5ycLNOnT5euXbuWSucLAggggAACCCCAAAIIIIBA8ASsFkvwGqMlBBAIewG2AIfZFC9dulTat28v8+fPl6NHj0peXp6sX79e7rnnHhk/frwebVZWlgwZMkTy8/PFZrOV+uTk5EhaWposX748zGQYDgIIIIAAAggggAACCCCAAAIIIBCZAgQAw2jeT5w4IQMHDpTCwkId1GvevLmkpKRIdHS0/v7888/Lhg0bZPTo0WK1WqVfv34yZ84cUUHDmTNn6sCf4lDlBw8erFcPhhEPQ0EAAQQQQAABBBBAAAEEEEAAAQQiUoAAYBhN+4wZM+TYsWMSExOjVwBu375d1q1bJ2pLcEJCgh7p8OHDZefOnaK2AGdkZMiAAQPklltukfT0dFm0aJFMnjxZ59u7d6/MnTs3jHQYCgIIIIAAAggggAACCCBgHgFbSVeN9jGPHj1FAIGyAgQAy4qY+PvixYv1AR59+/aV3r17O0aiVgKOGzdOrwLcuHGj9OnTR28JdmRwulHvCuzYsaNOUQFBLgQQQAABBBBAAAEEEEAAAQQQQAABcwsQADT3/JXq/bZt2/T3u+66q1S6+tKrVy9Hmtr66+5S7wtU7wZUwUIuBBBAAAEEEEAAAQRlWkhKAABAAElEQVQQQAABBBBAAAFzC3AKsLnnr1Tvc3Nz9ffExMRS6epLo0aNHGlNmjRx3Lu6adWqlU4+cuSIq8d+TWsUXcuv9VGZ7wI/2kqfCO17TZSsqkCxzVrVKijvJ4FGwu9RfqKscjVFHIRYZUN/VpAnxf6sjrqqINCQP0tVQc+/RYed/+XP4v6tldp8FfjK14IGKsefCA00GXQFgTAQYAVgGEyifQhxcXH6Niqq/LTan6kMzvf2ss4/a9eurb8WFBQ4J3OPAAIIIIAAAggggAACCCCAAAIIIGBCgfKRIhMOgi7/ItCgQQN9c/DgwSqR5Ofn6/L16tWrUj0URgABBBBAAAEEEEAAAQQQQAABBBAIvQABwNDPgd96YN/mq07wdXXNnDlT3n77bXG1Rdg5f05Ojv5qPznY+Rn3CCCAAAIIIIAAAggggAACgRewlrz+wmifwI9aZPfu3TJ27Fhp0aKF1KpVSy666CJp166dvPTSS3LmzJkqdWH79u3y2muvSXp6ulx33XX678Y1atTQ7SQnJ8tvf/tb+c9//qPfie9JQ0VFRTJt2jRJTU0VtSBH7bZTr9waMWKEbN261ZMqyINA0AR4B2DQqAPfUEpKimRnZ8vXX3/tsjH1m5wn16pVq3S2pk2bepKdPAgggAACCCCAAAIIIIAAAghUWWDhwoUycOBAOXXqlKMuFfRbv369/rz11luSlZUlvv5d9S9/+YvMmTPHUbfzzU8//STqM3fuXOncubNkZmaKu11xx44dk7S0NPnqq9JvnNy5c6dMnz5d3nnnHR1sHDp0qHMz3CMQMgFWAIaM3v8Nq38VUaf3Ll++3OfKCwsL9W90FotFOnbs6HM9FEQAAQQQQAABBBBAAAEEEEDAU4FvvvlGr8BTwb/4+HhRwbo1a9bIZ599JsOGDdPVfP/999KzZ0/Jy8vztNpS+WJiYuT666+Xhx9+WNQOuY8//lgHFpctWyZ///vf5Ve/+pXOv2LFCrn99tvFanV9FEtxcbH06dPHEfzr27evruvLL7+UV199VRo2bCjq79ZqJaBqgwsBIwhYSgJGNiN0hD5UXUD9Jrhu3Tpd0c033ywqiOftpf6VYsKECbrY0qVL5corr/S2Cq/yD77iLq/ykzlwArm2c4GrnJq9EuAUYK+4Apq5reWCgNZP5Z4LcAqw51bByMkpwMFQ9qyNXdbTnmUkV8AF9hWdDHgbNOC5wFcHVnqe2aA551w60HA9u+fAvwPWp5tuuknvaFNBupUrV0qHDh1KtaW2AD/66KM67ZlnnnH8vbVUpkq+qC27qv6KLhXY69evn3z44Yc6i9oO3KtXr3LZ1au1hgwZotNHjRolr7/+eqk8P/74o7Rt21avZFSrFdXWY3ftlirMFwQCJMAKwADBhqJadXpvt27d9MeX4J/qs9ombF/6HOjgXyiMaBMBBBBAAAEEEEAAAQQQQMBYAmohi3qdlbpUYK1s8E+lq/cCtmzZUt3K1KlT5fz58/rem/9XWRAuOjpaxo0b56jS3idHwv9uXn75ZX2n3k+oApNlLxX0e+KJJ3SyCgbOnz+/bBa+IxB0AQKAQSenQQQQQAABBBBAAAEEEEAAAQQQsAt89NFH9lsZNGiQ4975JioqSu677z6dlJubK59//rnzY7/dq4U19uvs2bP2W8dPtQ1ZrehTl1otWLNmTccz55v777/f8ZUAoIOCmxAKEAAMIT5NI4AAAggggAACCCCAAAIIIOBKQL2ry2gfV/30R5r9IEp16q/aOlvRpQ7nsF+rV6+23/r1Z0ZGhqM+dRJx2cveV5Xu3J+y+S6++GJp1qyZTg5UX8u2yXcE3AkQAHSnwzMEEEAAAQQQQAABBBBAAAEEEAiogH1Fndo6626brnNAzl7GHx1TJ/quXbtWbz9Wh4+oq379+nLPPfeUq37btm2ONOf+OBKdbuzP9+7dK6dP885WJxpuQyBQ8dsvQ9AZmkQAAQQQQAABBBBAAAEEEEAAARGr92c6Bpxt3759HrWRmJjoUT6VSW2zVQE4dVVWrm7duqJWCapgmgqqVeXq0qWLqNN+XV0q+Ke27V544YXlHjsbVNbfpKQkXV6dvarKNW/evFx9JCAQLAECgMGSph2XAkesBS7TSQy+wIWW6sFvlBZdCnxX9MsfgFw+JDGoAv2FU4CDCu6msd2xBvxbkJv+hvuj1cUnwn2IphlfQnQt0/Q13DuaWu3ScB8i40NA7AGtyihUwMvTKy8vz5E1Pj7ecV/RjT0AmJ+fX1GWKqX/8Y9/lD/96U96BaCrirzpr+qr/QpUf+318xOBygQIAFYmFIbP1b+wfPrpp/LVV1/J0aNH9RLrRo0aSWpqqtx4441hOGKGhAACCCCAAAIIIIAAAgggYEQB54M2qlWrVmkXq1f/ZeFCQUHVFpPMnDlTryRUwUp1qMj69evlH//4h7z22muyc+dOeeuttyQhIaFcf7zpr72vqpKq9rdcR0hAwEsBAoBeghk5+/Hjx8X+ctGePXuKOsK87KV+M5s4caKcOOH6X+5btWol06ZNkxtuuKFsUb4jgAACCCCAAAIIIIAAAggEScAapHa8aaaq225dtVWjRg1H8rlz5xz3Fd0UFhbqR3FxcRVl8Si9cePGpfKpBTEjR46Uu+++WxYtWiTt2rWTNWvWlNuWXLa/zt9LVVjyxd5XlV7V/patm+8IeCtAANBbMQPnnz17tjzyyCNy5ZVXSq9evcr19MEHH9T/oqEeVLQke9OmTfoko8zMTLntttvK1UECAggggAACCCCAAAIIIIBAZApU9s47X1Rq167tKObJNln7YRqebBd2VOzhjQrmqZWBl19+uX7H4KOPPirvvvtuqdJl++suAGjvq6ogEP0t1TG+IFCJAKcAVwJkpsefffaZ7u5dd91VrtvqBaZvvPGGDvzVrFlTBwqXLVsmO3bskK1bt8qCBQskPT1drxo8f/68Pu3o8OHD5eohAQEEEEAAAQQQQAABBBBAAAF/CagAWr169XR1zgdsuKr/559/dpym6+n7CF3V4y5NHQDSsWNHneU///mPqL8fO1/OQdDK+mtfMWmxWMqtJHSuk3sEgiHACsBgKAepDRXMU5er9/i9/PLL+pl619/nn38u6nh156tly5Z6xd/9998vaWlpov7lZerUqfLXv/7VOZvH95X9RuhxRWREAAEEEEAAAQQQQAABBCJQwPNjNMyPc9VVV0l2drb8+OOPUlRUpN9T72pU9r/zqmfq77CBuho0aKCrPnPmjD6h+JJLLnE0pfpqv1R/Wrdubf9a7qe9vypY6XwgSLmMJCAQBAFWAAYBOVhN2FfsXXpp6RPI1G9aX375pah/dVCBwLLBP+f+de7cWcaMGaNXCn788cfOj7y6V7/BefLxqlIyI4AAAggggAACCCCAAAIIhJ1Ap06d9JjUltkNGzZUOL4VK1Y4ntlX6TkS/Hizf/9+R21lt+7a+6oyOPfHUeB/N4cOHZLvv/9efwtkX8u2y3cEKhIgAFiRjAnTo6J+mU7nU4nUMFRg0Gr95RWyv/nNbyodmT2POvmICwEEEEAAAQQQQAABBBBAAIFACtxxxx2O6tU7+Fxd6u+06r336rrwwgula9eurrJVOU3tZlu7dq2uR70L0PmdfyqxWbNmjtWHc+fOFbXgxtU1a9YsR3KfPn0c99wgECoBAoChkg9Au2p7r7rsy4ztTTifNuR8DLn9edmf9jzFxcVlH3n8Xb3rwJOPxxWSEQEEEEAAAQQQQAABBBCIIAGrRcRon0Dxt2/fXtQpvOqaMWOGIwDn3N7kyZNl+/btOumhhx6S2NhY58fyxRdf6F1vaueberVV2Uutxlu+fHnZ5FLfT548Kb/73e/EfhrxfffdV+q5/Ys6fFNdJ06cEHVQSNkrJydHnn/+eZ2sduARACwrxPdQCPAOwFCoB6hN9S8gKvinTily/g3v4osvFvVRKwHVVuCbbrrJbQ9UHnXZA4puM1fw0PnFqBVkIRkBBBBAAAEEEEAAAQQQQAABLaDeQa+2yhYUFEiPHj3kySef1Kv81PeMjAyZPn26zqdW4I0dO9ZrtQMHDki3bt3k2muvFbXisG3btvrvyTExMaK2665evVoHH9W9un71q1/J448/7rIddYDm22+/rcu8/vrruvywYcOkbt26sm7dOvnzn/8sp06dErVL79VXX63wnYYuKycRgQAJEAAMEGwoqlVBv2nTpok6DfjNN98U9RuQ/RoxYoRMnDhR/wam/tWjoqPKDx48qP+lQv2rSffu3e3F+YkAAggggAACCCCAAAIIIIBAwATatGkj77//vgwcOFAHz1QAsOylgn9ZWVnltuWWzefu+7fffivq4+7q2bOnqK3INWvWdJktOjpaPvroI32A5ldffSWZmZn645xZ7ax77bXXxP6KLedn3CMQCgG2AIdCPUBttmvXTgYPHqwP8Bg5cqSoZdEqoKcu9ZvnDTfcoFcAqp8LFixwHJ+unquly+pfMNTSa/UvI9WqVZMHHnhAPeJCAAEEEEAAAQQQQAABBBAIsoC1pD2jfQJNcPvtt8umTZv0wZQq2KcCcOp9fykpKTJp0iT55ptv3B5q6a5/anXhJ598IuPGjdMrC6+88kqpU6eOXp130UUX6RWB6u/Aq1atkkWLFon9JOCK6qxfv76sWbNG3njjDVEHg9SrV08vtElOTtaLcdRhJkOHDq2oOOkIBF3AYiu5gt4qDQZMQL2rQC1nXrJkiX7/gVpyfN1110mrVq1EvQtQvU+hsLBQt69W+anfTNW7/tTyZHWpXw4qXf1LhQoiBvq67bKegW6C+j0UuNBS3cOcZAu0wHfnjwW6Cer3UOBhSfIwJ9kCLbA7tuRFSFyGEVhWfMQwfYn0jiRE14p0AsOM/2L+LGWYuVAd+duuDEP1x5fOvJk40JdiAS0zbN+/A1o/lSOAQOAE2AIcONuQ1KxW7v3nP//R7xxQ/0Jy/vx5Wb9+vf44d0gF+tRHrfxzvho2bKjfUdCvXz/n5IDdXxEVH7C6qdg7gZO2894VIHfABFrE1g9Y3VTsncAcW653BcgdMIFoGwHAgOH6UHGdKP7RyAe2gBRpKa63pwWkMSp1K3BMfD9Az23FPEQAAQQQQMAPAmwB9gOi0apQpyE9++yz+kAQdSJR48aNdbDPHvRTP50vlV8dDKJeuqpOKwpW8M+5D9wjgAACCCCAAAIIIIAAAgj8v4DRtv+q/nAhgIB5BVgBaN65q7TnKvD3wgsv6M+xY8dk27ZtesXf6dOn9bsJateuLUlJSaLefaBOPuJCAAEEEEAAAQQQQAABBBBAAAEEEAg/AaI+4TenLkekXlCqVvlxIYAAAggggAACCCCAAAIIIIAAAghElgABwMiab0aLAAIIIIAAAggggAACCCBgAgFef2uCSaKLCJhIgACgiSbL313Nzc3VpwXv3r1b4uPj9UnBqamp+hRgf7dFfQgggAACCCCAAAIIIIAAAggggAACoREgABga94C2unnzZpk4caJkZ2fL2bNnpUWLFjJmzBjp37+/o93Zs2fLgw8+KOp9gM5XcnKyTJ8+Xbp27eqczD0CCCCAAAIIIIAAAggggAACCCCAgEkFCACadOIq6vbSpUuld+/ecu7cOX3yr8q3fv16ueeee2TLli3y3HPPSVZWlgwZMkSKi4vLVaNOAU5LS9N5br755nLPSUAAAQQQQAABBBBAAAEEEAi8AKfuBt6YFhCIJIGoSBpsuI/1xIkTMnDgQCksLNTBv+bNm0tKSopER0fr788//7xs2LBBRo8eLVarVfr16ydz5swRFTScOXOmDvwpI1V+8ODBevVguJsxPgQQQAABBBBAAAEEEEAAAQQQQCDcBQgAhtEMz5gxQ44dOyYxMTEyf/582b59u6xbt07UluCEhAQ90uHDh8vOnTtFbQHOyMiQAQMGyC233CLp6emyaNEimTx5ss63d+9emTt3bhjpMBQEEEAAAQQQQAABBBBAAAEEEEAgMgUIAIbRvC9evFgf4NG3b1+9Ddg+NLUScNy4cXoV4MaNG6VPnz56S7D9ufNP9a7Ajh076iQVEORCAAEEEEAAAQQQQAABBBAIvoDaAmy0T/AVaBEBBPwlQADQX5IGqGfbtm26F3fddVe53vTq1cuRprb+urvU+wJtNpuoYCEXAggggAACCCCAAAIIIIAAAggggIC5BTgExNzzV6r3ubm5+ntiYmKpdPWlUaNGjrQmTZo47l3dtGrVSicfOXLE1WO/prUqivVrfVTmu0C8lbnwXc+/JddWO+/fCqnNZ4GzUv6wJJ8ro2CVBIpsvAq9SoB+Lnyb9QI/10h1vgq8Zznqa1HK+VmgmN+n/CxKdTYIEEAAAT8KsALQj5ihriouLk53ISqq/LTan6kMzveu+ly7dm2dXFBQ4OoxaQgggAACCCCAAAIIIIAAAggggAACJhIoHykyUefpammBBg0a6ISDBw+WfuDlt/z8fF2iXr16XpYkOwIIIIAAAggggAACCCCAAAIIIICA0QTYAmy0GalCf9Q2X3XCrzrB19U1c+ZMnexqi7Bz/pycHP3VfnKw8zPuEUAAAQQQQAABBBBAAAEEAi9gtQS+DVpAAIHIESAAGEZznZKSItnZ2fL111+7HFV6errL9LKJq1at0klNmzYt+4jvCCCAAAIIIIAAAggggAACCCCAAAImE2ALsMkmzF1327Vrp0/vXb58ubtsbp8VFhZKZmamWCwW6dixo9u8PEQAAQQQQAABBBBAAAEEEEAAAQQQML4AKwCNP0ce9zAtLU2WLVum89tsNh3E87jw/zJmZGSIOgREfXr27OltcfIjgAACCCCAAAIIIIAAAgj4QcDqhzqoAgEEELALEAC0S4TBTxW069atW5VGorYJe7pVuEoNURgBBBBAAAEEEEAAAQQQQAABBBBAICgCbAEOCjONIIAAAggggAACCCCAAAIIIIAAAgggEBoBVgCGxp1WEUAAAQQQQAABBBBAAAEEEKhQgC3AFdLwAAEEfBBgBaAPaBRBAAEEEEAAAQQQQAABBBBAAAEEEEDALAIEAM0yU/QTAQQQQAABBBBAAAEEEEAAAQQQQAABHwTYAuwDGkX8J7As+pT/KqOmKgm0ia5dpfIU9p9Aq6JY/1VGTVUSSC3mfyarBOjHwrElp9tzGUfg/er5xulMhPekt7VBhAsYZ/hfxxQYpzP0JCwE+F++sJhGBoGAYQT4m41hpiJwHdm3b59s27ZN9uzZI3l5eVJQUCDqxOC6devK1VdfLddcc41ER0cHrgPUjAACCCCAAAIIIIAAAggggAACCCAQMgECgCGjD2zD+/fvl6lTp0pmZqbs2rXLbWM1atSQvn37yqhRo6RDhw5u8/IQAQQQQAABBBBAAAEEEEAAAQQQQMBcArwD0Fzz5VFvJ0+eLM2aNRP186effhJbybYp9VGX/d75p1oR+O6770qnTp1k8ODBepWgRw2RCQEEEEAAAQQQQAABBBBAICACVouI0T4BGSiVIoBAUARYARgU5uA18uSTT8qkSZMcAb8mTZpIQkKCnDx5Unbs2CHFxcVSvXp1efTRR6VWrVryww8/yIYNG2Tjxo26k++8845s3bpVli5dKhdccEHwOk5LCCCAAAIIIIAAAggggAACCCCAAAIBESAAGBDW0FT63//+Vwf/VOvdu3eX119/XZo2berozNGjR2X8+PHy5ptv6hV/KvBXp04d/TwnJ0f+9Kc/SUZGhqxfv17uv/9+mT9/vqOstzfqvYNcCCCAAAIIIIAAAggggAACCCCAAAKhFyAAGPo58FsPpk2bplf+paSkSFZWlsTElJ7eBg0ayD//+U85ffq0vPfeezrgp94TqC61UlBtA7722mvliSeekAULFsinn34qt9xyi0/9S0pK8qjcnZfd7lE+MiGAAAIIIIAAAggggAACkSRgjaTBMlYEEAi4AO8ADDhx8BpYuXKlWCwWGTNmTLngn3MvHnvsMR0o/Ne//uXYKmx/rp6lpqbqr//+97/tyfxEAAEEEEAAAQQQQAABBBBAAAEEEDCpQOklYiYdBN3+ReDQoUP6pkWLFm5J7M/VewHVacGJiYml8quDQLKzs2X16tWl0r35snfvXo+yj+44yqN8ZEIAAQQQQAABBBBAAAEEEEAAAQQQ8E2AAKBvboYspQ73KCwsrPQU37y8PEf/i4qKHPf2G3uA0B5QtKd787NsUNGbsuRFAAEEEEAAAQQQQAABBCJdwBbpAIwfAQT8KsAWYL9yhrYy9R4/dS1evNhtR+zPo6Oj5eKLLy6XV6Wry/6zXAYSEEAAAQQQQAABBBBAAAEEEEAAAQRMI0AA0DRTVXlHb731Vv1Ov1dffVW++OILlwXUab/qPX/qXYHXX3+91KhRo1y+nTt36rSEhIRyz0hAAAEEEEAAAQQQQAABBBBAAAEEEDCXAFuAzTVfbnv7xz/+UV577TW9BbhHjx4yYMAAUUFBFchT7/tbsWKFzJw5Uz+3HxbiqkJ1+q963qpVK1ePSUMAAQQQQAABBBBAAAEEEAiwgFXYBBxgYqpHIKIECACG0XQ3bNhQZs+eLXfffbecP39e1Cm+ZU/ytdl++R+RoUOHSt++fcuN/vTp0/LBBx/olYTdunUr99zfCTWEX4L+NvW1vv2W874WpZyfBY7FsDjbz6Q+V3cihv8ufMbzc8EztvLvrPVzE1TnhcCJ4gIvcpM1kAI/R9cKZPXU7YXAivxfdtF4UYSsCCCAAAIIBE2A6EvQqIPTUK9evUSt4Bs1apRs3bq1XKN169aVp556Sh5++OFyz1SCChAuW7ZMP2vevLnLPCQigAACCCCAAAIIIIAAAggEVsAa2OqpHQEEIkyAAGAYTnhqaqps3rxZNmzYIOvXr5fjx49LfHy8tGzZUjp16iRxcXEVjlrla9u2bYXPeYAAAggggAACCCCAAAIIIIAAAgggYC4BAoDmmi+veqsCeQTzvCIjMwIIIIAAAggggAACCCCAAAIIIBB2AgQAw25KGRACCCCAAAIIIIAAAggggIDZBTgCxOwzSP8RMJYAAUBjzUdQe5ObmytLliyR3bt36y3C6tRftX1YnQDMhQACCCCAAAIIIIAAAggggAACCCAQHgIEAMNjHkuNQr3/b+LEiZKdnS1nz56VFi1ayJgxY6R///6OfOq04AcffFDUqb/OV3JyskyfPl26du3qnMw9AggggAACCCCAAAIIIIAAAggggIBJBQgAmnTiKur20qVLpXfv3nLu3Dl9oq/Kpw4Cueeee2TLli3y3HPPSVZWlgwZMkSKi4vLVZOTkyNpaWk6z80331zuOQkIIIAAAggggAACCCCAAAKBF+AU4MAb0wICkSQQFUmDDfexnjhxQgYOHCiFhYU6+Ne8eXNJSUmR6Oho/f3555/XJwOPHj1arFar9OvXT+bMmSMqaDhz5kwd+FNGqvzgwYP16sFwN2N8CCCAAAIIIIAAAggggAACCCCAQLgLEAAMoxmeMWOGHDt2TGJiYmT+/Pmyfft2WbdunagtwQkJCXqkw4cPl507d4raApyRkSEDBgyQW265RdLT02XRokUyefJknW/v3r0yd+7cMNJhKAgggAACCCCAAAIIIIAAAggggEBkChAADKN5X7x4sT7Ao2/fvnobsH1oaiXguHHj9CrAjRs3Sp8+ffSWYPtz55/qXYEdO3bUSSogyIUAAggggAACCCCAAAIIIBB8AWvJ2YxG+wRfgRYRQMBfAgQA/SVpgHq2bdume3HXXXeV602vXr0caWrrr7tLvS/QZrOJChZyIYAAAggggAACCCCAAAIIIIAAAgiYW4BDQMw9f6V6n5ubq78nJiaWSldfGjVq5Ehr0qSJ497VTatWrXTykSNHXD32a9r+4jy/1kdlvgu0i/plm7jvNVDSXwI7o4r8VRX1VFEgVkr+6Z3LEAJNLbUM0Q868YvAj9FIGEVgu5w2Slcivh/RFtZWRPwvAgAQQAABAwsQADTw5Hjbtbi4OMnLy5OoqPJ/+FDP7JfzvT3N+Wft2rX114KCAudk7hFAAAEEEEAAAQQQQAABBIIkYBVbkFqiGQQQiASB8pGiSBh1mI6xQYMGemQHDx6s0gjz8/N1+Xr16lWpHgojgAACCCCAAAIIIIAAAggggAACCIRegBWAoZ8Dv/VAbfNVJ/yqE3xdXTNnztTJrrYIO+fPycnRX+0nBzs/4x4BBBBAAAEEEEAAAQQQQAABBBBAwFwCBADNNV9ue5uSkiLZ2dny9ddfu8yXnp7uMr1s4qpVq3RS06ZNyz7iOwIIIIAAAggggAACCCCAQBAE2AAcBGSaQCCCBNgCHEaT3a5dO3167/Lly30eVWFhoWRmZorFYpGOHTv6XA8FEUAAAQQQQAABBBBAAAEEEEAAAQSMIcAKQGPMg196kZaWJsuWLdN12Ww2HcTztuKMjAxRh4CoT8+ePb0tTn4EEEAAAQQQQAABBBBAAAEEEEAAAYMJEAA02IRUpTsqaNetW7eqVCFqm7CnW4Wr1BCFEUAAAQQQQAABBBBAAAEEKhSwVviEBwgggID3AmwB9t6MEggggAACCCCAAAIIIIAAAggggAACCJhGgACgaaaKjiKAAAIIIIAAAggggAACCCCAAAIIIOC9AFuAvTejBAIIIIAAAggggAACCCCAAAIBFbAK5wAHFJjKEYgwAVYARtiEM1wEEEAAAQQQQAABBBBAAAEEEEAAgcgSYAVgZM234UZbzcIvQaNMypEoXjNslLmIF/5txihzcdooHaEfclTOoWAggbO2YgP1JrK7EiOWyAYw0OjvqtXcQL2hKwgggAACCJQWIPpS2iNsvm3cuFFycnIkOjpaWrZsKc2be/YHkqNHj8o//vEP7fD000+HjQcDQQABBBBAAAEEEEAAAQTMJMAGYDPNFn1FwPgCBACNP0de9fCTTz6RP/zhDzr451zwmmuukb/85S+SlpbmnFzu/siRIzJhwgSxWCxCALAcDwkIIIAAAggggAACCCCAAAIIIICA6QTYZ2a6Kau4w3PnzpXbb79dB/9sNps4fzZt2qSfDRkyRAoKCiquhCcIIIAAAggggAACCCCAAAIhF1Av6DHaJ+QodAABBHwWYAWgz3TGKnj48GEZPny4FBUV6dV7d955p3Tt2lUKCwtlxYoVkpWVJcXFxTJr1izZtm2b/n7RRRcZaxD0BgEEEEAAAQQQQAABBBBAAAEEEEDA7wIEAP1OGpoKp02bJqdOndLv/MvMzJRevXo5OjJmzBj59ttvZejQobJhwwZZt26d3HTTTbJs2TK55JJLHPn8ebNv3z5/VkddCCCAAAIIIIAAAggggAACCCCAAAI+ChAA9BHOaMXUu//Ue/uGDRtWKvhn7+e1114rq1evlgceeEBmzJgh27dvl06dOslnn30mV1xxhT2b334mJSV5VFePxFs9ykcmBBBAAAEEEEAAAQQQQCCSBKzCMSCRNN+MFYFAC/AOwEALB6n+77//XrfUt2/fClusVq2avPnmm/owEPV+wF27dklqaqrs2LGjwjI8QAABBBBAAAEEEEAAAQQQQAABBBAwtwArAM09f47eq+2/6mrQoIEjraKbJ554QtT7/9RqwAMHDujtwEuXLpXWrVtXVMTr9L1793pUZkiHYR7lIxMCCCCAAAIIIIAAAggggAACCCCAgG8CBAB9czNcqfj4eDl58qQcP37co76NGDFC6tSpI+np6XLs2DG5+eabZfHixVK7dm2PyleWKTExsbIsPEcAAQQQQAABBBBAAAEEEKhAgA3AFcCQjAACPgmwBdgnNuMVatq0qe7U+vXrPe7cgAED5IMPPpDq1atLbm6u9OjRQ78T0OMKyIgAAggggAACCCCAAAIIIIAAAgggYHgBAoCGnyLPOpiSkiLqvX6LFi3yrMD/cqnTglWZWrVqyenTp0WdGMyFAAIIIIAAAggggAACCCCAAAIIIBA+AmwBDpO57N69u0ybNk2f9Pvdd99J8+bNPR5Zt27dRJ0ifNttt+ltxB4X9EPGVtEX+qEWqvCHQLRY/FENdfhBIE+K/VALVfhD4KbzNfxRDXX4QaB+UZEfaqEKfwn8u0asv6qinioKVLfw7/lVJPRb8XVFx/xWFxUhoASsMCCAAAJ+FOBPDH7EDGVVt956q17Fp1YBTpgwweuu3HjjjbJ8+XKPDhHxunIKIIAAAggggAACCCCAAAIIIIAAAgiETIAVgCGj92/DcXFxsmbNGlGnAUdF+RbXVacAqzqys7P92zlqQwABBBBAAAEEEEAAAQQQQAABBBAImQABwJDR+7/hVq1aVbnS5ORkUR8uBBBAAAEEEEAAAQQQQACB0AnYhHOAQ6dPywiEnwABwPCbU49HVFBQIEePHtX5L7vsMo/LkREBBBBAAAEEEEAAAQQQQAABBBBAwDwCvu0VNc/46KkbgQULFkjjxo1Z8efGiEcIIIAAAggggAACCCCAAAIIIICA2QVYAWj2Gaxi/9WhIVwIIIAAAggggAACCCCAAALGEuAUYGPNB71BwOwCBADNPoP/6//KlSu9Hsm2bdscZdTBH2WDgTfddJPjOTcIIIAAAggggAACCCCAAAIIIIAAAuYUIABoznkr1+suXbqIxWIpl+5Jggr8qfLOl6qrqKjIOYl7BBBAAAEEEEAAAQQQQAABBBBAAAETChAANOGkuety2VV87vI6P/O1nHMd3COAAAIIIIAAAggggAACCPhHwMopwP6BpBYEENACBADD7BdCQkKCDBkyRGJjYysd2ZYtWyQzM1OvHHz66acrzU8GBBBAAAEEEEAAAQQQQAABBBBAAAHzCRAANN+cuezxuHHjZMqUKXLkyBGZP3++TJs2TVJTU13mtSe+//77OgCovj/zzDP2ZH4igAACCCCAAAIIIIAAAggggAACCISRQFQYjSWihzJp0iRZt26dtG3bVrZv367f6adWAp44cSKiXRg8AggggAACCCCAAAIIIGBGAVtJp432MaMjfUYAgV8EWAEYRr8SWrduLV9++aVMnTpV1JbeWbNmyYIFC+TFF1+UQYMGGXKk31nzDNmvSOxUTQu/HRhl3g8U8d+FUeZid0wto3Ql4vsRG8O/WRrpF8Ht5+ON1J2I7suMqMMRPX4jDb5xzAVG6g59QQABBBBAoJQAf5ouxWH+L+r03tGjR8vWrVslLS1Njh8/LkOHDpXOnTvrlYHmHyEjQAABBBBAAAEEEEAAAQQQQAABBBDwRoAAoDdaJsqblJQkCxculIyMDGnYsKFkZ2eLWiH4xBNPSEFBgYlGQlcRQAABBBBAAAEEEEAAgcgTUKcAG+0TebPAiBEIHwECgOEzly5H0q9fP9mxY4deBVhUVKS3A1999dWSlZXlMj+JCCCAAAIIIIAAAggggAACCCCAAALhJcBLv8JrPl2O5oILLpDp06fLvffeK8OHD5fvvvtOevXqJcnJyS7zk4gAAggggAACCCCAAAIIIBBaAWtom6d1BBAIMwFWAIbZhLobTmpqqmzatEkfEBIbGys5OTnusvMMAQQQQAABBBBAAAEEEEAAAQQQQCAMBFgBGAaT6M0QVOBvwoQJMmDAAJkyZYoUFhZ6U5y8CCCAAAIIIIAAAggggAACCCCAAAImEyAAaLIJ81d3mzdvLtOmTfNXddSDAAIIIIAAAggggAACCCDgRwFbySEgXAgggIC/BNgC7C9J6kEAAQQQQAABBBBAAAEEEEAAAQQQQMCAAgQADTgpdAkBBBBAAAEEEEAAAQQQQAABBBBAAAF/CbAF2F+S1IMAAggggAACCCCAAAIIIICAnwQ4BdhPkFSDAAJagBWA/EJAAAEEEEAAAQQQQAABBBBAAAEEEEAgjAVYARjGk2uGodWwRJuhmxHRR+bCONM8RC4xTmcivCdvFx2McAHjDP/ymAuM0xl6IoX8E7JhfhUkRtc2TF8ivSPVhf8wIv3XAONHAAEEjCxAANDIsxOAvh05ckRmzJghK1askP379+sWLr30UuncubMMGjRILrmEwEMA2KkSAQQQQAABBBBAAAEEEPBKgFOAveIiMwIIVCJAALASILM8nj17tu7qHXfcIXXq1HHZ7bfeekvGjBkjZ86cKfV827Zt8umnn8pf//pXmTx5sowYMaLUc74ggAACCCCAAAIIIIAAAggggAACCJhXgACgeeeuVM/vv/9+sVgskpKSIldddVWpZ+rL9OnTZeTIkTrdZrPpvPXq1dM/jx07JipNBQZHjRolMTExMmTIkHJ1kIAAAggggAACCCCAAAIIIIAAAgggYD4BXlRhvjnzuscHDhyQ0aNH6yCfCu79+c9/lkOHDonaDnz48GH9UWnVqlXTeVReFRTkQgABBBBAAAEEEEAAAQQQCI2AOgXYaJ/QSNAqAgj4Q4AVgP5QNHgd//jHP+Ts2bMSFRUl8+fPl7S0tFI9rl+/vjz11FNy7bXXSu/evfVKQLVd+PHHHy+Vz5sv+/bt8yY7eRFAAAEEEEAAAQQQQAABBBBAAAEEAiTACsAAwRqpWvV+P7U9+K677ioX/HPu52233SZ33nmnXgX42WefOT/y+j4pKUk8+XhdMQUQQAABBBBAAAEEEEAAAQQQQAABBLwSIADoFZc5M//444+64yq4V9l199136yxbt26tLCvPEUAAAQQQQAABBBBAAAEEAiRgLXlPu9E+ARoq1SKAQBAE2AIcBORQN3Hq1CndheTk5Eq7Ys/z888/V5rXXYa9e/e6e+x4NqbjA457bhBAAAEEEEAAAQQQQAABBBBAAAEE/C9AAND/poarUb3jTx364c2lDgupypWYmFiV4pRFAAEEEEAAAQQQQAABBBBAAAEEEPCTQNWiPH7qBNX4T+DgwYMSHx9fqsLmzZvrAKBalde2bdtSz8p+UScDq0sFDbkQQAABBBBAAAEEEEAAAQRCI2ALTbO0igACYSpAADDMJrZHjx4Vjui///2v3HHHHRU+Vw++/fZb/fySSy5xm4+HCCCAAAIIIIAAAggggAACCCCAAALmECAAaI558qiXtpKXxLq7MjMz5YUXXnCXRRYuXKhPDE5JSXGbz18Pi2xWf1VFPVUVsFS1Asr7S2BvjPv/lv3VDvVULnC+iN+jKlcKTo6D1tPBaYhWPBLYFFPNo3xkCrxA++KagW+EFjwS+Dzql/due5SZTAgggAACCARZgABgkMED1dzMmTM9qjovL09q167tMu/GjRtl7dq1OgDYoUMHl3lIRAABBBBAAAEEEEAAAQQQCLyAVfhH4cAr0wICkSNAADBM5jo9Pb3KI6lbt658/vnnup7K3hVY5caoAAEEEEAAAQQQQAABBBBAAAEEEEAgKAIEAIPCbI5GLr/8clEfLgQQQAABBBBAAAEEEEAAAQQQQACB8BEgABg+c+n1SAoKCuTo0aO63GWXXeZ1eQoggAACCCCAAAIIIIAAAggERsDGFuDAwFIrAhEqEBWh42bYJQILFiyQxo0bS3JyMh4IIIAAAggggAACCCCAAAIIIIAAAmEqwArAMJ1YT4dV2cnBntZDPgQQQAABBBBAAAEEEEAAAf8JWP1XFTUhgAACQgAwTH4RrFy50uuRbNu2zVEmOztbygYDb7rpJsdzbhBAAAEEEEAAAQQQQAABBBBAAAEEzClAANCc81au1126dBGLxVIu3ZMEFfhT5Z0vVVdRUZFzEvcIIIAAAggggAACCCCAAAIIIIAAAiYUIABowklz1+Wyq/jc5XV+5ms55zq4RwABBBBAAAEEEEAAAQQQ8I+AlUNA/ANJLQggoAUIAIbZL4SEhAQZMmSIxMbGVjqyLVu2SGZmpl45+PTTT1eanwwIIIAAAggggAACCCCAAAIIIIAAAuYTIABovjlz2eNx48bJlClT5MiRIzJ//nyZNm2apKamusxrT3z//fd1AFB9f+aZZ+zJ/EQAAQQQQAABBBBAAAEEEEAAAQQQCCOBqDAaS0QPZdKkSbJu3Tpp27atbN++Xb/TT60EPHHiRES7MHgEEEAAAQQQQAABBBBAwIwCtpItwEb7PzM60mcEEPhFgBWAYfQroXXr1vLll1/K1KlTRW3pnTVrlixYsEBefPFFGTRokCFHWieqmiH7FYmdShDmwijzvt520ihdifh+NI2pG/EGRgGoa6n81RZG6Wsk9KNPQXEkDNMUY9wVwx/njTJRCw5vMEpX6AcCCCCAAALlBFgBWI7E3Anq9N7Ro0fL1q1bJS0tTY4fPy5Dhw6Vzp0765WB5h4dvUcAAQQQQAABBBBAAAEEEEAAAQQQ8FaAAKC3YibJn5SUJAsXLpSMjAxp2LChZGdni1oh+MQTT0hBQYFJRkE3EUAAAQQQQAABBBBAAIHIFLCWDNton8icCUaNQHgIEAAMj3mscBT9+vWTHTt26FWARUVFejvw1VdfLVlZWRWW4QECCCCAAAIIIIAAAggggAACCCCAQPgIEAAMn7mscCQXXHCBTJ8+Xb744gtp1qyZ7Nq1S3r16iXjx4+vsAwPEEAAAQQQQAABBBBAAAEEEEAAAQTCQ4AAYHjMo0ejSE1NlU2bNukDQmJjYyUnJ8ejcmRCAAEEEEAAAQQQQAABBBAIroDNVnIGsME+wRWgNQQQ8KcAx4b5U9MEdanA34QJE2TAgAEyZcoUKSwsNEGv6SICCCCAAAIIIIAAAggggAACCCCAgK8CBAB9lTN5uebNm8u0adNMPgq6jwACCCCAAAIIIIAAAggggAACCCBQmQABwMqEeI4AAggggAACCCCAAAIIIIBAkAWsYgtyizSHAALhLMA7AMN5dhkbAggggAACCCCAAAIIIIAAAggggEDECxAAjPhfAgAggAACCCCAAAIIIIAAAggggAACCISzAFuAw3l2GRsCCCCAAAIIIIAAAggggIApBaym7DWdRgABowoQADTqzAShX5s3b5YdO3ZITEyMNG7cWFq3bh2EVks3UU9iSyfwLWQCR+VcyNqm4dICtS3VSifwLWQCp2z8dxEy/DINR1ksZVL4GkqBd+JC2TptOwtEy1nnr9yHUGDFRR1C2DpNI4AAAggg4F6AAKB7H9M93bVrl/ztb3+TtWvXyrlz50Sd9jty5Ejp2rWrYyzbt2+X9PR02bBhgyNN3TRq1EgmTZokAwYMKJXOFwQQQAABBBBAAAEEEEAAAQQQQAAB8woQADTv3JXr+aeffip33HGHFBQUOJ5t2bJFMjMzZfz48TJx4kQ5cOCAdO/eXQ4ePCg2W+lTpfbt2ycDBw6UQ4cOyZgxYxx1cIMAAggggAACCCCAAAIIIBBcARunAAcXnNYQCHMBAoBhMsFHjx6V/v37y5kzZxwjql+/vhw7dkx/f+6556Rz587y1ltv6SBgjRo1dLCvbdu2cv78eVmxYoXMnz9frFarPPHEE3L77bdL06ZNHXVxgwACCCCAAAIIIIAAAggggAACCCBgTgFOATbnvJXr9bRp0+TEiRNiKXlH0sMPPyy5ubly5MgROXnypIwdO1av9vvrX/8qH330kdSrV0/Wr18v06dPlxEjRsiDDz4o8+bNk4ULF+r3AaqA4D//+c9ybZCAAAIIIIAAAggggAACCCCAAAIIIGA+AVYAmm/OXPZ4yZIlOvinVu69/PLLjjy1a9eWl156SXJycnTwTwUI1TsCr7rqKkce+81vfvMbGTp0qKhgoloRWJVLbSfmQgABBBBAAAEEEEAAAQQQ8E3AGqFbgHfv3i2vvvqqZGVlyd69e6V69erSpEkT6devnzzwwANSs2ZN30BLSqkdb+ogzHXr1unPV199JZs2bdLvz1eVfv7559KlSxd16/ZSeTz9O3PZV2+5rZiHCARQgABgAHGDWbX6TUxd6nAPV9e9996rA4DqWa9evVxl0WnqHYIqAPjjjz9WmMeTB0lJSZ5kkzGX/9ajfGRCAAEEEEAAAQQQQAABBBAIbwG1K029l/7UqVOOgarXXKkdbOqjXmmlAoO+vq7qX//6l9x///2OurlBIJIECACGyWzn5eXpkVx22WUuR+ScnpCQ4DKPSrQ/y8/PrzAPDxBAAAEEEEAAAQQQQAABBAIrEGkrx7755hv57W9/qw+1jI+P1++m79q1q/6ekZEhb775pnz//ffSs2dPHQxUu928vZxNY2NjpVWrVvqd+Js3b/a2Kp0/JSVFZs6c6VNZCiEQbAECgMEWD1B7tWrV0v9Kok75VQd7lL3Uqb/2S70rsEGDBvavpX7+/PPP+ntVllWrCtRSbU+uv3V6xJNs5EEAAQQQQAABBBBAAAEEEAhjgYceekgH+2JiYmTp0qXSoUMHx2hvvvlmufLKK+XRRx/VQcDJkyfLhAkTHM89vVGvwlLbi9u1ayetW7cWdTimqsfXAKD6e/ivfvUrT5snHwIhFeAQkJDy+69x9U4EdS1YsMBlpc7pn376qcs8KnHZsmX62eWXX15hHk8eJCYmiicfT+oiDwIIIIAAAggggAACCCCAQPgKqHfyZWdn6wEOGTKkVPDPPmp1uGXLli3116lTp+qVe/Znnv5s3769/OEPf5AbbrhBB/88LUc+BMJBgABgOMxiyRhuueUWfdLvrFmzyp3gq070VcuS27RpI9HR0fLUU0/J0aNHy418y5Yt8ve//10fJtKpU6dyz0lAAAEEEEAAAQQQQAABBBAIjoC1pBmjfQI18o8++shR9aBBgxz3zjdRUVFy33336aTc3Fx9YIfzc+4RQMC9AAFA9z6meTpy5EiJi4vTpxqNGjVKGjZsKNdff73+qb6r046efPJJUSf97tq1Sy93fuGFF/TS6sWLF+v3K6ign3r3nzopWJ0GzIUAAggggAACCCCAAAIIIIBAoAVWrVqlm1Bbal290srefufOne23snr1asc9NwggULlATOVZyGEGAbVlV53eq/61RAX7jh07JsePH9erAlX/1enAd955p6h3HqxcuVIOHTqkVwI6j83+QlT17gW1WjAYV11bdDCaoQ0PBPIsxR7kIkswBJKlWjCaoQ0PBNbYCjzIRZZgCBy3ng1GM7ThocCxotMe5iRboAXuiE0MdBPU76HAa9U5RM9DqqBk6xiUVmjEXwLbt2/XVanTfdU7ACu6WrRo4XhkL+NICMHNjh079MKb7777Ts6ePSv169fXAUz1d+8BAwaIOmiECwGjCFT8X5ZRekg/PBa49957JTk5WdTKvjVr1si5c+ekefPm8vvf/14GDx6s61HvTFDvA1S/GakDQ5wvdfDH448/LuPHj3dO5h4BBBBAAAEEEEAAAQQQQCDIAjaxBbnFypvbt29f5ZlKcqj3wXt6qcCZWsCirsrK1a1bV9QqwdOnT3t88KSn/fAl3+HDh0V97Nf+/ftFfdTfuSdNmiQffPCB472F9jz8RCBUAgQAQyUfoHY7duwoCxcudFt7amqq7Ny5U5YsWSLqX03Uv7A0btxYunXrJhdccIHbsjxEAAEEEEAAAQQQQAABBBCITIGkpCSPBm7fXeZJ5ry8PEe2+Pj4/2vvXuCtquoEAK+LXFBL1HiogAhooJTPEgMzRbIxRzHzUVojiELmNKNZajXm61f2HscZlXR8gNmMlKikhqajJCm+IzWkAkFBJS0tBFHgcueubed2uN7nueex9znf9ne8+5y99tr/9a3Lvff8z9prNe+3tZNLAMbpqyq1xfkI4/vnww47LOy5556hb9++IbbjiSeeSObkj++zFy5cGMaNGxfiAidDhgypVKiuS6BZQAKwmaK2dnr16hUmTJiQPGqr5VpLgAABAgQIECBAgAABAmkRiCMAc1t8n9rR1rt376TI2rWVm6rl5ptvDttss807Qo2DbeIc/FOmTAkzZsxIRgeeccYZIZa3Eai0gARgpXuggtePPzBzqwH7RKKCHeHSBAgQIECAAAECBAgQaCGwMYW3AC9fvrxFlN1/uvnmmzdXEqex6mh76623kiJxEcxKba0l/3KxxHn/rr766vDQQw+FODfgLbfcktwWPGjQoFwRXwlUREACsCLs6bhobi7AOHx5w4YN6QhKFAQIECBAgAABAgQIECCQSoGO5ugrJOitttqq+bTO3NYb5/+LW2duF26uuMw7cZqtk08+OZx99tnJlX/5y1+GE044ocxRuByBTQV6bPrUs1oU6Mr8DLXoo80ECBAgQIAAAQIECBAgUBqBOAIwzqEXt44WGXnttdeSBUBi2c7ORxjLVmIbNWpU82XjwiA2ApUWMAKw0j1QpOvff//9Xa4pTkqa2+bNmxdaJgI/8pGP5A77SoAAAQIECBAgQIAAAQJlFGj5/qyMly77pWKyLL4nXbx4cXJ3WhxB19q2aNGi5pd322235v007tTV1aUxLDHVsEDr/6pqGCSrTT/ooINCoT9g4i+WeH7+FutyW3C+iH0CBAgQIECAAAECBAgQKIXAhz/84SQBGG/vffzxx8N+++3X6mXirbS5bf/998/tpvJr/oCbgQMHpjJGQdWWgFuAq6y/YzKvK49c81s7J3fMVwIECBAgQIAAAQIECBAgUCqBT3ziE81VX3fddc37+TsbN24M119/ffJSXIRj3Lhx+YdTtR8H01x77bXNMbm7rpnCTgUFjACsIH4pLr3ddtslk43GlYc62p5++ukwa9asZOTgeeed11FxxwkQIECAAAECBAgQIECgTAJpXAW4VE0fPXp0OOCAA5JRgNdcc02YOHFiGDNmzCaX+8EPfhCeeeaZ5LXTTz89tHzPO3fu3OakYDx/+vTpm5xfrCf33Xdf2HvvvUNbKwGvX78+TJkypTnWI444IvXzFRbLRj3pFpAATHf/dDq6s846K1xyySXh5ZdfTpYZ/+EPf5j8AG2vgpkzZyYJwFjm/PPPb6+oYwQIECBAgAABAgQIECBAoGQCl156aYi39a5duzZ87GMfC1/72teShF58fuONN4arrroqufaIESPCl770pYLjaJkYXLBgQXNdd955Z1i2bFnz81122SXE25PztxkzZoQJEyYkjziV1siRI0OfPn1CXME43r4c48zd/jtgwIAQ22UjkAYBCcA09EIRYvjOd74Tjj/++DB16tTw2GOPJXP6TZo0KXzve98L73nPe4pwBVUQIECAAAECBAgQIECAAIHSCMRRdXGQymc/+9mwatWqJAHY8kox+XfHHXeErbbaquWhTj8/6aST2iwb31fnb3EkYcsEYDwek33/8z//kzzyy+fv77777knictiwYfkv2ydQMQEJwIrRF//Ce+21V3j44YeTTxjiLb3xk42f/exn4bvf/W5o74dc8SPpfI1PhtWdL6xkSQUamuaPtKVDoEddOuIQRQinrd8GQ0oEbt58XUoiEUYUGNBrCxApEXi88fWURCKMn7/yGwgEiirQNLt7UevLQmXxdtknn3wyeU8bE30rVqwIvXr1CnEk3rHHHhu+8IUvhC233LKiTTnnnHNCfO89f/78ZKTfK6+8El599dXQu3fvEKfk+uAHPxiOOeaYcNRRR4XNNtusorG6OIF8gbqmxR9q76dKvkCV7i9fvjycdtppyacjcUXf+KlFvC04f6n0+OlKHDUYjzc0NFRE4tidjqzIdV30nQINNfgHxjsV0vFK/7re6QhEFOHwN31OlpZvAwnAtPTE23HUBZ9UpKVHXm9cn5ZQaj4OCcB0fQuseWNZugIqIJqDBn+0gLNKe8rcFfeU9gJqJ0CgZAJWAS4ZbWUr3nHHHcNtt92WDDmO8w7Mmzcv+ZTiq1/9ajKnQmWjc3UCBAgQIECAAAECBAgQIECAAIFyCUgAlku6Qtc57rjjwqJFi8Ipp5wS4lLk8Xbg973vfcnIwAqF5LIECBAgQIAAAQIECBAg0IHAxqab9dL26CBkhwkQSLGABGCKO6dYoW299dbJSkRxpUexbgAANnNJREFUWfQ4aWpc1SiuWnTuuecW6xLqIUCAAAECBAgQIECAAAECBAgQSKmABGBKO6YUYR1wwAHJhKpxgZD6+vqwZMmSUlxGnQQIECBAgAABAgQIECBAgAABAikSMLt5ijqjHKHExN8FF1yQLP5xySWXhLfeeqscl3UNAgQIECBAgAABAgQIEOiCgNU6u4ClKAECHQpIAHZIVJ0FRo4cmawKXJ2t0yoCBAgQIECAAAECBAgQIECAAIGcgARgTsJXAgQIECBAgAABAgQIECCQEoGNwRjAlHSFMAhUhYA5AKuiGzWCAAECBAgQIECAAAECBAgQIECAQOsCEoCtu3iVAAECBAgQIECAAAECBAgQIECAQFUIuAW4KrpRIwgQIECAAAECBAgQIECgmgTcAlxNvaktBCovIAFY+T6o6Qh2q3t3Tbc/TY1/KaxLUzg1HcuqxvU13f40Nf6+zTdLUzg1HcvT616p6fanrfEf7LV92kKq3Xjq6mu37Slr+cJdd0lZRMIhQIAAAQJ/F3AL8N8t7BEgQIAAAQIECBAgQIAAAQIECBCoOgEjAKuuS7vfoAceeCBcc801oa6uLvna/RrVQIAAAQIECBAgQIAAAQJdEWhstApwV7yUJUCgfQEjANv3qcmjixcvDtOnT08eNQmg0QQIECBAgAABAgQIECBAgACBKhKQAKyiztQUAgQIECBAgAABAgQIECBAgAABAi0F3ALcUsTzogisWLGiKPWohAABAgQIECBAgAABArUoYBXgWux1bSZQOgEJwNLZlr3m+++/vyjXXLRoUbfr2XHHHTtVx9d3OqFT5RQiQIAAAQIECBAgQIAAAQIECBAoTEACsDC3VJ510EEHJQt3pDI4QREgQIAAAQIECBAgQIAAAQIECFREQAKwIuylvWgaVotavnx5pxp51YfP6VQ5hQgQIECAAAECBAgQIFBLAo3BKsC11N/aSqDUAhKApRYuY/29evUK69evD3vssUc46qijCr7yggULwuzZsws+P544ePDgbp3vZAIECBAgQIAAAQIECBAgQIAAgeIISAAWxzEVtcTE32OPPRbq6+vD+eefX3BMM2bM6HYCsOCLO5EAAQIECBAgQIAAAQIECBAgQKCoAj2KWpvKKiqw7777Jtd/6qmnwrp16yoai4sTIECAAAECBAgQIECAQOECcWqntD0Kb40zCRCotIARgJXugSJef/To0WHatGnJbcDxNt74PO3bK2F92kOsmfjWNG6ombamvaFD6rZIe4g1E9+vG16rmbamvaGDe26d9hBrKr7lG9fUVHvT3Ngde7wrzeHVVGwz/rxdTbU37Y09L+0Bio8AAQJlFjACsMzgpbxcfsLv0UcfLeWl1E2AAAECBAgQIECAAAECBAgQIJARASMAM9JRnQlz1113DdOnT0+GiY8aNaozp7RaZuLEiSE+bAQIECBAgAABAgQIECBQGYGNVgGuDLyrEqhSAQnAKurYurq6cOKJJ1ZRizSFAAECBAgQIECAAAECBAgQIECguwJuAe6uoPMJECBAgAABAgQIECBAgAABAgQIpFjACMAUd47QCBAgQIAAAQIECBAgQKA2BeIKwDYCBAgUS0ACsFiSGaxn5cqV4YUXXkgiHzhwYNhhhx0y2AohEyBAgAABAgQIECBAgAABAgQItCfgFuD2dKrwWENDQ7jsssvCyJEjw6BBg0JcOTg+Bg8eHEaMGBEuvfTSsH79+ipsuSYRIECAAAECBAgQIEAgOwJxEZC0PbKjJ1ICBFoKSAC2FMnw84MPPjiMHz8+PPfcc622YtWqVeGQQw4Jp59+eli8eHGyWnAcVp57LFmyJJx55plJmVjWRoAAAQIECBAgQIAAAQIECBAgkH0BtwBnvw+bWzB37twQVwJes2ZN82v5O5MmTQqxTNzq6+uTRN+oUaOScxYuXBjuvvvusG7dujBv3rwwZcqUMHPmzKSs/xEgQIAAAQIECBAgQIAAAQIECGRXQAIwu33Xpchj4u/WW29Nkn177bVXuOmmm8KwYcM2qWPp0qXh6KOPDgsWLEiOP/zww2G//fbbpIwnBAgQIECAAAECBAgQIFB6gaZ7tUp/EVcgQKBmBNwCXCNdPX369KSlffv2DXfeeec7kn/xYEwIzpkzJ/Tr1y8pO2PGjOSr/xEgQIAAAQIECBAgQIAAAQIECGRXQAIwu33XpcgffPDBZPTfqaeeGvr379/mudttt12YOnVqMi9gPMdGgAABAgQIECBAgAABAgQIECCQbQG3AGe7/zod/cqVK5OyBx54YIfnjBs3Llx88cVh+fLlHZbtboG67lbg/KIJbFNXX7S6VNQ9gccbXu1eBc4umkCfHr2LVpeKuiewd9iqexU4u6gCvw9ri1qfygoXmNLwVuEnO7OoApdttrGo9amMwMamBRttBAgQKJaAEYDFkkx5PQ0NDUmE7Y3+yzUhdwvw6tWrcy/5SoAAAQIECBAgQIAAAQIECBAgkFEBCcCMdlxXwx46dGhySmeSemvXvv2pfp8+fbp6GeUJECBAgAABAgQIECBAgAABAgRSJuAW4JR1SDHCueKKK8KAAQM2qaqu7u2bbRctWhTGjh27ybGWT5YtW5a8lBsJ2PK45wQIECBAgAABAgQIECBQWgGrAJfWV+0Eak1AArAKe3zatGltturee+8NkydPbvN4PDB//vzk+PDhw9st5yABAgQIECBAgAABAgQIECBAgED6BdwCnP4+6lKEjU0Txbb3mD17dli1alWbdca5AmfNmpWsGDxmzJg2yzlAgAABAgQIECBAgAABAgQIECCQDQEjALPRT52KcuPG7q88NmfOnNCzZ88wZMiQ0JkVgzsVmEIECBAgQIAAAQIECBAg0CUBqwB3iUthAgQ6EJAA7ACo1g4ffvjhIT5sBAgQIECAAAECBAgQIECAAAEC1SHgFuDq6EetIECAAAECBAgQIECAAAECBAgQINCqgBGArbJ4kQABAgQIECBAgAABAgQIVE7AKsCVs3dlAtUoYARgNfaqNhEgQIAAAQIECBAgQIAAAQIECBD4m4AEoG8FAgQIECBAgAABAgQIECBAgAABAlUs4BbgKu7cLDStITRmIcyaiHHr4MdBWjr643X90xJKzcfx+7q3at4gLQCD19elJRRxNAksrfcZclq+EW7YrFdaQqn5OLapeQEAxRawCnCxRdVHoLYF/PVW2/0f7rvvvnDwwQeH8ePH17iE5hMgQIAAAQIECBAgQIAAAQIEqlPAkJ/q7NdOt+rll18Oc+fODXV1RlZ0Gk1BAgQIECBAgAABAgQIECBAgECGBCQAM9RZQiVAgAABAgQIECBAgACB2hCwCnBt9LNWEiiXgARguaTLcJ3hw4d3+Spr1qxpPqfl+XFU4JIlS5qP2yFAgAABAgQIECBAgAABAgQIEMiegARg9vqszYiXLVuW3Mrb2Nj1hTXiOfH8/K07twWvWLEivyr7BAgQIECAAAECBAgQIECAAAECFRKQAKwQfCkv26dPn7Dtttt26hJxBOCf/vSnJHE4ZMiQTp3TmUI77rhjZ4qFqTsd06lyChEgQIAAAQIECBAgQKCWBKwCXEu9ra0ESi8gAVh647JdYejQockovvXr14dTTjklnHPOOaFnz/a7eObMmeH4449PYly6dGnZYnUhAgQIECBAgAABAgQIECBAgACB8gj0KM9lXKUcAr/97W/DmWeeGdatWxfOO++8sOeee4Z58+aV49LvuMby5ctDZx7vONELBAgQIECAAAECBAgQIBDiIiBp+0+3ECCQXYH2h4dlt101GfkWW2wRvv/974fPfOYzYcqUKeGJJ54IBx10UDjxxBOT1/v27Vs2l8GDB5ftWi5EgAABAgQIECBAgAABAgQIECDQtoARgG3bZPbI3nvvHR555JEk6bfllluG66+/PowcOTJcc801mW2TwAkQIECAAAECBAgQIECAAAECBAoTkAAszC31Z/Xo0SO5HTjeFnzooYeGV199NUydOjUccMABIb5mI0CAAAECBAgQIECAAIH0CjQ2bgxpe6RXS2QECHQk4BbgjoQyfjyu7HvHHXeEG2+8MXzxi18MDzzwQNhnn33CGWecES644IKKt65/qK94DAJ4W2DYBp8HpOV74bc9N6QllJqP482wseYN0gLw856vpyUUcTQJDAtbckiJwFtNM4TZCBAgQIAAAQIdCXjH35FQlRz/9Kc/HZ555pkwefLksGHDhuT24N122y3cfffdVdJCzSBAgAABAgQIECBAgAABAgQIEGhNQAKwNZUqfW2bbbYJV199dbjvvvvCe9/73vD888+H6667rkpbq1kECBAgQIAAAQIECBDIrkDTDcBN9yKk65FdTZETICABWIPfAx/5yEfCk08+Gc4999xQX+8W3Br8FtBkAgQIECBAgAABAgQIECBAoIYEJABrqLPzm9qrV69w0UUXhTffbJrhauPG0NDQkH/YPgECBAgQIECAAAECBAgQIECAQJUIWASkSjpSMwgQIECAAAECBAgQIECgegQaGy3yUz29qSUEKi8gAVj5PqhYBCtXrgwvvPBCcv2BAweGHXbYoWKxuDABAgQIECBAgAABAgQIECBAgEBpBNwCXBrX1NYab/W97LLLwsiRI8OgQYPC6NGjk8fgwYPDiBEjwqWXXhrWr1+f2vgFRoAAAQIECBAgQIAAAQIECBAg0DUBCcCueaW69MEHHxzGjx8fnnvuuVbjXLVqVTjkkEPC6aefHhYvXhzikPL8x5IlS8KZZ56ZlIllbQQIECBAgAABAgQIECBQGYG0rQAc47ERIJBdAbcAZ7fv3hH53LlzQ11dXVizZs07jsUXJk2aFGKZuMXVf2MycNSoUck5CxcuDHfffXdYt25dmDdvXpgyZUqYOXNmUtb/CBAgQIAAAQIECBAgQIAAAQIEsisgAZjdvutS5DHxd+uttybJvr322ivcdNNNYdiwYZvUsXTp0nD00UeHBQsWJMcffvjhsN9++21SxhMCBAgQIECAAAECBAgQIECAAIFsCbgFOFv9VXC006dPT87t27dvuPPOO9+R/IsHY0Jwzpw5oV+/fknZGTNmJF/9jwABAgQIECBAgAABAgTKK5A/XVNa9ssr4GoECBRTQAKwmJopruvBBx9MRv+deuqpoX///m1Gut1224WpU6cmcwPGc2wECBAgQIAAAQIECBAgQIAAAQLZFnALcLb7r9PRr1y5Mil74IEHdnjOuHHjwsUXXxyWL1/eYdnuFvhz2NDdKpxfJIHBdb2KVJNquiuwsvGt7lbh/CIJvNSwukg1qaa7Ag2NG7tbhfOLKLBd/eZFrE1V3RHYc0N9d053bhEFfhze/nu7iFWqigABAgQIFE1AArBolOmuqKGhIQmwvdF/uRbkbgFevdob35yJrwQIECBAgAABAgQIECinwMZGq+6W09u1CFS7gFuAq72H/9a+oUOHJnudSeqtXbs2KdunT5+/ne0LAQIECBAgQIAAAQIECBAgQIBAVgWMAMxqz7UT9xVXXBEGDBiwSYm6urrk+aJFi8LYsWM3OdbyybJly5KXciMBWx73nAABAgQIECBAgAABAgQIECBAIDsCEoDZ6atORzpt2rQ2y957771h8uTJbR6PB+bPn58cHz58eLvlHCRAgAABAgQIECBAgACB0gg0BrcAl0ZWrQRqU8AtwFXW7x0tDz979uywatWqNlsd5wqcNWtWsmLwmDFj2iznAAECBAgQIECAAAECBAgQIECAQDYEjADMRj91KsqNG7u/QuKcOXNCz549w5AhQ0JnVgzuVGAKESBAgAABAgQIECBAgAABAgQIVExAArBi9Om88OGHHx7iw0aAAAECBAgQIECAAAEClROId3fZCBAgUCwBtwAXS1I9BAgQIECAAAECBAgQIECAAAECBFIoYARgCjtFSAQIECBAgAABAgQIECBQ2wIbLQJS298AWk+gyAJGABYZVHUECBAgQIAAAQIECBAgQIAAAQIE0iQgAZim3hALAQIECBAgQIAAAQIECBAgQIAAgSILuAW4yKCq65rAK41vdu0EpUsm8PBmDSWrW8VdEzhk/bu6doLSJRP4ff0WJatbxV0T+EPj6q6doHRJBRY1/KWk9au88wJnDfD7u/NapS353eWvlfYCaq85AYuA1FyXazCBkgoYAVhSXpUTIECAAAECBAgQIECAAAECBAgQqKyABGBl/Yt69eHDh4fDDjsszJw5M7z11ltFrVtlBAgQIECAAAECBAgQIECAAAEC2RRwC3A2+63VqJctWxaee+65cNddd4WtttoqHHvsseHEE08MBxxwQKvlvUiAAAECBAgQIECAAAEC6RTY2NiYzsBERYBAJgWMAMxkt7UfdJwrYtWqVeHaa68NBx10UNh5553DhRdeGJ599tn2T3SUAAECBAgQIECAAAECBAgQIECg6gSMAKy6Lg3hhBNOCHPnzg0vvvhi0ro4MvCiiy5KHmPHjg0TJ04Mxx13XOjTp0/JWr9ixYqS1a1iAgQIECBAgAABAgQIECBAgACBzgvUNY0WM664816pLtmjR49QV1cXnnrqqbDrrruGe+65J1x//fXh1ltvDW+88UYSezwet969e4cJEyYktwgfeuihIZ5bzC13nY7qPHrIER0VcbxMAlvV1ZfpSi7TkcBH1lt5tiOjch3/ff3Gcl3KdToQsApwB0BlPvyXjeYaLjN5m5e7pr9VgNvEKfOBj1oFuMzi7V/uD6883n6BDBzd9t27pC7K11YvTl1MAiJAoHMCxc36dO6aSpVBICb0Pvaxj4UbbrghrFy5svl24HjpmPN98803w09/+tNwxBFHhEGDBoUvf/nL4cknnyxDZC5BgAABAgQIECBAgAABAgQIECBQTgEjAMupXeJr5Y8AHDVqVKtXW758efjRj36UJAYXLVrUXCY3Ym/33XcPkyZNSm4jHjBgQPPxru509hbgM/Y/ratVK18iASMASwRbQLVGABaAVqJTjAAsEWwB1RoBWABaCU8xArCEuF2s2gjALoKVsLgRgCXELaBqIwALQOvEKUYAdgJJEQIpFZAATGnHFBJWZxKA+fU++uijYcaMGWHmzJnhz3/+c3IolwjcbLPNkhGEt99+e/4pRd8/ZqcJRa9ThYUJSAAW5laKsyQAS6FaWJ0SgIW5leIsCcBSqBZepwRg4XbFPlMCsNiihdcnAVi4XSnOrIYE4Nbv3rkUNN2q86+rl3TrfCcTIFA5AbcAV86+4lfed999w2WXXRZeeumlcMstt4Sjjjoq1NfXJ7cIb9iwIcyZM6fiMQqAAAECBAgQIECAAAECBAgQIECgewISgN3zq4qze/bsGY488sgwa9asJBl4+eWXh9GjR1dF2zSCAAECBAgQIECAAAECBAgQIFDrAj1rHUD7NxXYdtttw+c///nksXixFZ421anuZ3+yomNqOvinm61NTSy1Hsj2YctaJ0hN+19reDM1sQiEQJoE+p+8a5rCqelYjvneqzXdfo0vvkBcvNFGgACBYgkYAVgsySqsZ5dd0rfsfBUyaxIBAgQIECBAgAABAgQIECBAoKQCEoAl5VU5AQIECBAgQIAAAQIECBAgQIAAgcoKuAW4sv5FvfrSpUuT+gYNGlTUelVGgAABAgQIECBAgAABAuUV2OgW4PKCuxqBKheQAKyiDt5pp52qqDWaQoAAAQIECBAgQIAAAQIECBAgUAwBtwAXQ1EdBAgQIECAAAECBAgQIECAAAECBFIqYARgSjumHGGtXLkyvPDCC8mlBg4cGHbYYYdyXNY1CBAgQIAAAQIECBAgQKADgcZgFeAOiBwmQKALAkYAdgGrGoo2NDSEyy67LIwcOTLEuQJHjx6dPAYPHhxGjBgRLr300rB+/fpqaKo2ECBAgAABAgQIECBAgAABAgQINAlIAFbRt8HBBx8cxo8fH5577rlWW7Vq1apwyCGHhNNPPz0sXrw4NDZNKpv/WLJkSTjzzDOTMrGsjQABAgQIECBAgAABAgQIECBAIPsCbgHOfh82t2Du3Lmhrq4urFmzpvm1/J1JkyaFWCZu9fX1SaJv1KhRyTkLFy4Md999d1i3bl2YN29emDJlSpg5c2ZS1v8IECBAgAABAgQIECBAoLwCVgEur7erEah2AQnAau/hv7UvJv5uvfXWJNm31157hZtuuikMGzZsk9YvXbo0HH300WHBggXJ8Ycffjjst99+m5TxhAABAgQIECBAgAABAgQIECBAIFsCbgHOVn8VHO306dOTc/v27RvuvPPOdyT/4sGYEJwzZ07o169fUnbGjBnJV/8jQIAAAQIECBAgQIAAgfIK5E/XlJb98gq4GgECxRSQACymZorrevDBB5PRf6eeemro379/m5Fut912YerUqcncgPEcGwECBAgQIECAAAECBAgQIECAQLYF3AKc7f7rdPQrV65Myh544IEdnjNu3Lhw8cUXh+XLl3dYtrsFGpoWIrGlQ2Bgjy3SEYgowntCPYWUCDzR8FpKIhHGZnU+s0zTd8FOm22VpnBqOpb6T51Z0+1PU+Ov/trH0xROzcfyrZoXAECAAIFNBSQAN/Wo2mcNDQ1J29ob/ZdrfO4W4NWrV+de8pUAAQIECBAgQIAAAQIEyijQGAyWKCO3SxGoegEfp1d9F7/dwKFDhyY7nUnqrV27Ninbp0+ft0/2fwIECBAgQIAAAQIECBAgQIAAgcwKGAGY2a5rO/ArrrgiDBgwYJMCdXV1yfNFixaFsWPHbnKs5ZNly5YlL+VGArY87jkBAgQIECBAgAABAgQIECBAgEB2BCQAs9NXnY502rRpbZa99957w+TJk9s8Hg/Mnz8/OT58+PB2yzlIgAABAgQIECBAgAABAqURiCv/2ggQIFAsAbcAF0syJfV0tDz87Nmzw6pVq9qMNs4VOGvWrGTF4DFjxrRZzgECBAgQIECAAAECBAgQIECAAIFsCBgBmI1+6lSUGzdu7FS59grNmTMn9OzZMwwZMiR0ZsXg9upyjAABAgQIECBAgAABAgQIECBAoPICEoCV74NURXD44YeH+LARIECAAAECBAgQIECAQOUE3AJcOXtXJlCNAm4BrsZe1SYCBAgQIECAAAECBAgQIECAAAECfxOQAPStQIAAAQIECBAgQIAAAQIECBAgQKCKBdwCXMWdq2kECBAgQIAAAQIECBAgkE0BawBns99ETSCtAkYAprVnxEWAAAECBAgQIECAAAECBAgQIECgCAJ1TROL+mChCJCqqE2BFStWhB133DFp/PLly8PgwYNrEyIFrdYXKeiEv4WgL/RFegTSE4l/F+npixiJ/khPf+gLfZEeAZEQIECgugWMAKzu/tU6AgQIECBAgAABAgQIECBAgACBGheQAKzxbwDNJ0CAAAECBAgQIECAAAECBAgQqG4BCcDq7l+tI0CAAAECBAgQIECAAAECBAgQqHEBCcAa/wbQfAIECBAgQIAAAQIECBAgQIAAgeoWkACs7v7VOgIECBAgQIAAAQIECBAgQIAAgRoXkACs8W8AzSdAgAABAgQIECBAgAABAgQIEKhuAQnA6u5frSNAgAABAgQIECBAgAABAgQIEKhxAQnAGv8G0HwCBAgQIECAAAECBAgQIECAAIHqFpAArO7+1ToCBAgQIECAAAECBAgQIECAAIEaF6hrbNpq3EDzCRAgQIAAAQIECBAgQIAAAQIECFStgBGAVdu1GkaAAAECBAgQIECAAAECBAgQIEAgBAlA3wUECBAgQIAAAQIECBAgQIAAAQIEqlhAArCKO1fTCBAgQIAAAQIECBAgQIAAAQIECEgA+h4gQIAAAQIECBAgQIAAAQIECBAgUMUCEoBV3LmaRoAAAQIECBAgQIAAAQIECBAgQEAC0PcAAQIECBAgQIAAAQIECBAgQIAAgSoWkACs4s7VNAIECBAgQIAAAQIECBAgQIAAAQISgL4HCBAgQIAAAQIECBAgQIAAAQIECFSxgARgFXeuphEgQIAAAQIECBAgQIAAAQIECBCQAPQ9QIAAAQIECBAgQIAAAQIECBAgQKCKBSQAq7hzNa00Am+++Wa44oorwvjx40P//v1Dr169wsCBA8Nhhx0WbrzxxtJctMZqffnll8Ptt98ezjvvvPDxj3889OvXL9TV1SWPSZMmdVljzpw54aijjgqDBw8OvXv3Tr7G5/F1W/sCxeiLjRs3hoULF4bp06eH0047Ley7775JP+T6dO7cue0H4WizQDH644033gg333xz+PznP5/0xbbbbhvq6+tD3759w5gxY8IFF1wQVq5c2XxNO60LFKMvnnnmmXDZZZeFiRMnhn322Sf52bT55puHd73rXWH48OHhU5/6VJg9e3ZobGxsPQivJgLF6Iu2KOO/l9gXuZ9XQ4cObauo15sEitEX8XdFzrujr7GsrXWBYvRFy5rvueeeEP8O22WXXZKfU1tvvXUYMWJEOOaYY8K0adPC6tWrW57iOQECBAikSKBnimIRCoHUC/zud78LRx55ZIhf87eXXnopxEdMKF133XVh1qxZ4d3vfnd+EftdENhuu+26ULrtojHxNHXq1HDNNddsUuiFF14I8XHrrbeGU045JVx55ZWhRw+fh2yC9LcnxeiLH/3oR8kbhtbq91rXBLrbH08++WTYf//9W32T9uqrr4aHHnooeVxyySXhqquuShJQXYuwdkp3ty+i1De/+c3w4x//uFW0pUuXhvj4yU9+Eg488MDk90pM0treKVCMvnhnrW+/Ej+Iiv1g65xAKfuicxEolRMoZl+89tpr4aSTTko+kMjVn/u6atWq8Ic//CH5GRU/RNprr71yh3wlQIAAgZQJSACmrEOEk16B+EnqIYccEpYvX54EeeyxxyajNuLovxdffDHMmDEj/PSnPw2/+MUvwqc//elkBFt6W5OdyIYMGRJ23XXXxLWrUf/bv/1bc/Jv7733DmeffXbYeeedw5IlS8J3v/vd8Otf/zpcffXVyUjOiy++uKvV11z5Qvsif/RSHGm2++67h/Xr14ennnqq5gyL2eBC+iO+UcuN0IiJwMMPPzx88IMfTEb/vfLKK8nIwP/+7/8OsdxnPvOZ0KdPn2QUbjHjrsa6CumL6NCzZ8+w3377JUnZ+O9i++23T34exTfbixYtSj6cePrpp8Mvf/nLcMQRR4Rf/epXPqzo4Buo0L5ordr4O+I//uM/QhyVGX92vf76660V81obAsXoi7vuuiu5y6KNSySjZts65vW/C3SnL/76178mf/8+/vjjSYXxDoo44i/+PbXZZpslfxfHn1Hxw28bAQIECKRcoOmNmY0AgU4I/PM//3O8Byt5nH/++a2e0TRSoLlMUzKw1TJe7FggOt52222NTbchJoWbRl80uzbdKtdxBU0lmkZpNja9uU7Oa0pwNDbdxrXJeWvWrGmMr8c+jeWaPr3e5LgnbwsUoy8efvjhxv/8z/9snD9/fuPatWuTiuO/ody/p/vuuw93JwW62x8PPPBA43HHHdf429/+ts0rNo2MbWy67S7pn6Y3eI1NI2nbLFvLB7rbF9GuKRHeLuGGDRsaP/nJTzb/W2m6Hbjd8rV6sBh90dIu2n/gAx9I7C+66KLGnXbaKdmPX21tCxSjL5rupGj+no+//22FCRSjL+KV/+mf/inpj6YpVBrb+xkUf1d09DOtsJY4iwABAgSKJWAEYMoTtMJLh0BDQ0O44YYbkmCa/vgPX//611sNrOmPrRDno3n++efDt7/97eQT0lYLerFdgQsvvLDd4505GEdtNL2BS4r+13/9V9hiiy02OW3LLbcM8fV4u0osF295vPzyyzcp40kIxeiL0aNHh/iwdV+gu/0xduzYEB/tbXGag6akUzKaI46WjaOg4vx0tk0FutsXsbY4ArC9LY6uOeuss5KRmbHcvHnzwoQJE9o7pSaPFaMvWsJdeumlIY54GjlyZDjnnHOaR5O3LOf5pgKl6ItNr+BZZwWK0Rdx1HGcxiNu3/jGN9r9+RPna+zoZ1pnY1eOAAECBEojYNKr0riqtcoE4twm8RaIuMXbgOObsta2+Ho8Hrf4xqHpk+vWinmtxAJNn5A0z1MTbx/+0Ic+1OoV4+vxzV3cTLTfKpEXa1Rg3LhxzS2PSUBb5QS22mqr5ovHRahspRd47rnnkkWo4pV++MMfJot9lf6qrkAgfQJxkaK4xcU+vvCFL6QvQBERIECAQJcEJAC7xKVwrQr8+c9/bm56R5Mq5x+PozVs5ReIidc4L2Pc4uT57W2543FRkGXLlrVX1DECNSPw1ltvNbe1rQ88mgvYKalA/ury8QMNW+kF4mrlTdNEhKZbH8NBBx1U+gu6AoEUCqxbt675w9T44XacCzNu8a6YOB92/JvJhxIp7DghESBAoB0BCcB2cBwikBPIX9E3NxIwd6zl1/zjCxcubHnY8zII5Lt39IY5//gzzzxThuhcgkD6BeKE7rltt912y+36WiaBP/3pT6Fpzsxw8sknJysFx8v269cvWZilTCHU7GViwvXnP/952HbbbcMPfvCDmnVIS8PjyrNxsbVevXol/wbiyP1zzz03xA/tbKUV+M1vftOc4IuLFMXFoc4444ykH+KiIsOGDUtGBsbk4Ny5c0sbjNoJECBAoCgC7U8+U5RLqIRA9gV22WWXZAXAuHLp/fff326D8o/HuQBt5RdYsWJF80UHDx7cvN/azo477tj8cm6F5+YX7BCoQYH4pu+OO+5IWh7f9EkAluebII40y0+85l81Jv9uueWWsM022+S/bL/IAnH15ZjgiFucx7d///5FvoLquiqQn1iKd2PER9PCUklyNs71+7nPfa6rVSrfSYH8D1ObFvhIVoyPU+Lkb3GU4D333BP+7//+L3zrW99K5svMP26fAAECBNIlYARguvpDNCkVeNe73hUOPvjgJLonn3wy/O///m+rkcbXn3rqqeZjr7/+evO+nfIJ5Lvnj95sLYLYt7lt9erVuV1fCdSkQLz195RTTklu8YoA3/zmN2vSIU2N/td//dcQRyd/+MMfTlNYVRlLXHDlj3/8Y7I41JQpU6qyjVlp1PDhw8OXv/zlZDGiRx55JMRHHJ157LHHhrjYRLz19NRTTw1XXXVVVpqUuThfffXV5pi/853vhJj8O/TQQ5O+iP4vv/xymDZtWjIKMM69/JWvfKX5luHmE+0QIECAQKoEjABMVXcIJs0CF1xwQfIJZ1wxduLEiSFOjH/iiSeGHXbYIbz00kvh+uuvDxdddFFym0r8RDRua9euTXOTqja2/Dlp4m1D7W29e/duPqy/mins1KhAnOT9scceS1off84dccQRNSpR/mZfd911ybxz8Y30X/7yl6Qf4pvrOAn/s88+G66++uqQP8ds+SOs7ivG0fvXXnttsoppXPgjJplslRE46qijkr+zWvbBvvvuGz71qU+F22+/PVmpPN6V8cUvfjFZmXb77bevTLBVfNU4D2Zui39XxVt9o31uXtg4QjYmYd///vcn8y3HUYJf/epXk/5o2Xe5enwlQIAAgcoKGAFYWX9Xz5BAnHfmyiuvTN4cxD86v/71r4eddtopSfjFr/F5z549w7//+783typ/9cbmF+2UXCA3UXW8UC4Z29ZF8xc72GKLLdoq5nUCVS8Qb9+KSaa4xTfal19+edW3OU0NjPNpxTfS8bbrAw44IElsxBHnhx12WPKmO/ZJ/vQGaYo967HE3wNTp04NMfl6+umnhz322CPrTcp0/HHF2fYSSIcffnjzKs1vvPFGuOaaazLd3rQGn/+3VIwxjgLMJf/yY46jkz/5yU8mL8XRyvl3wuSXs0+AAAEClReQAKx8H4ggQwKTJ09O5p6Jn07n3zoaE38TJkwITzzxRDJHSq5JcRJxW/kF8hOvHd3Wm/8Jd0e3C5e/Ja5IoDwC8cONr33ta8nF4sI4cRGE/J9x5YnCVVoKxDfgcWTglltumay6efbZZ7cs4nkRBOKt7r/73e9CnBP2wgsvLEKNqii1QEzY5pKEbc2dWeoYqr3+/L+l4mi/vffeu80m/8M//EPzsUcffbR53w4BAgQIpEvALcDp6g/RZEBgn332CTfffHOItwLHW3/jCLNBgwaF3CelN9xwQ3Mr3ve+9zXv2ymfQP7CHx2NmMlf+CN/QZDyRetKBCorEOcuPe2005Ig4mjmu+++O1nlsbJRuXpOIC4Asv/++yf9Mnv27BBHoNfX1+cO+1oEgTiyKW4f/ehHw2233dZqjbkPi+LXOBdd3AYMGNA8P3CrJ3mxZALRvm/fviGumG1F4NIw5/9NlP93VWtXyy/7yiuvtFbEawQIECCQAgEJwBR0ghCyKRBH/eX/wZNrxeOPP57bDaNHj27et1M+gVGjRjVfbNGiRc37re3kH7faaWtCXqtmgZ/97GfJXKZx7qY4n2lcybGjN3rV7JHWtuVWo423O8aER+wrW/EEclNFxNGW8dHeFv2PP/74pMiBBx4oAdgeVomP5UYAlvgyNVt9/ofYDQ0N7TrkH49/H9sIECBAIJ0CbgFOZ7+IKqMC8Q+gODowbjE5OHbs2Iy2JNthx7m0Bg4cmDSio1uD4sTvcYujOIcOHZrs+x+BWhCIyb7jjjsuGc0cR9LEkX8777xzLTQ9c23MH+FkqoLMdZ+ASyAQR5nFZGzccr/vS3CZmq4yjggfMmRIYrBs2bJkjsy2QOLCeLkt/j1lI0CAAIF0CkgAprNfRJVRgTgR9fPPP59E/7nPfa7VyZIz2rRMhR1HBRx55JFJzHGE30MPPdRq/PH13AjAWN5oglaZvFiFAg8++GDybyQufhAn3L/rrrtC/miPKmxyZpsUpzGYP39+En98Q54/L1dmG5WywOPiHx09on3c4tdc2blz5yav+V/5Ba666qrmhFQciWkrjcDRRx+dVLxq1apkhHhbV8l9+B2Px0VBbAQIECCQTgEJwHT2i6hSKpA/CqNliPfee28444wzkpdHjBgRvvSlL7Us4nkZBWJf5Far+5d/+Zewdu3aTa4en8fX4xZvV8n13SaFPCFQhQILFiwI//iP/xjiXGZxoY877rgjfOADH6jClqa7Sb///e9D/L3R3vbXv/41nHDCCc2rmZ944ontFXeMQOYF4kizX//61+224/bbbw8XXXRRUmaLLbYIJ510UrvlHSxcIP5tlJvj+swzzwwxEdhyi3Nf55Lh8XdLa9PjtDzHcwIECBCojIBJGirj7qoZFXj/+98f4ifN8Q+cOFqmd+/eyYi/W265Jfz4xz8OcR6t97znPeEnP/lJ8x9MGW1qRcP+1a9+FRYvXtwcQ+42n/hCfH369OnNx+LOpEmTNnken8Qk7FlnnRW+/e1vh8ceeyyZRP+cc85JbnGMt6rESd9zbzJiufe+973vqMMLIRSjL6Jjyz6LSajcduedd4b4pi+37bLLLkYQ5DBafO1uf8Tv/bha41/+8pek5m984xvJCMCnn366xZX+/jROth8ftk0FutsXL774Yhg/fnzYc889wyc+8YkkCbv99tsnH0isXLkyPPDAAyGOKo/7cYu/f77yla9sGoRniUB3+wJj8QS62xfxd8G4cePCmDFjwhFHHJH8+8j9/Hn22WfDTTfdlDziKMy4ff/730+m8CheC6qnpu72RZSItwDHZGtcgfypp55K5raOf0vtscceSTIwjvybNm1agtanT59wySWXVA+glhAgQKAaBZp+gdoIEOikQNNomfgXZ5uPpqRgY1Nio5O1KdaWwMSJE9s0bs2/rXqa5mRsnDx5crt1nXzyyY2xnK11gWL1RWv91tZr8Zq21gW62x9NCxy0+++htT45//zzWw+mxl/tbl/cd999ne6Lpg+dGl9++eUaF2+7+d3ti7Zr/vuRplt/k/6KX21tC3S3Lzr772LLLbdsvPLKK9sOxJHG7vZFPmHThw+NTdOktPkzqylJ29g0tUT+KfYJECBAIIUCRgA2vduxEeiswNVXXx1+8YtfhEceeSS89NJLYfXq1SGuzhg/CT322GPDZz/72VBfX9/Z6pQrsUCPHj2SETRxDps4X9Cjjz6aTBrer1+/sO+++4Y4T+PHP/7xEkehegIECLxTYP/990/mXrznnnuSUcpxrr8//vGPIa70G0fSxMWMPvShDyUrzsayNgK1IBCnI4i3lMZ5L+Po/fi3VrwLYMOGDWHbbbdN7r6II2dPOeUUI5PL+A3xrW99K0yYMCEZ7Tdv3rykX+KtwfFui/h6nFIlzidrI0CAAIF0C9TFpGS6QxQdAQIECBAgQIAAAQIECBAgQIAAAQKFClgEpFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAv8PaeKIwehUTKcAAAAASUVORK5CYII=\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.heatmap(vol_df)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = Axes3D(fig)\\n\",\n    \"#ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([0.7, 1, 0.7, 1]))\\n\",\n    \"X = vol_df.columns\\n\",\n    \"Y = vol_df.index\\n\",\n    \"X, Y = np.meshgrid(X, Y)\\n\",\n    \"x = X.reshape(np.size(X))\\n\",\n    \"y = Y.reshape(np.size(Y))\\n\",\n    \"z = vol_df.values.reshape(np.size(vol_df))\\n\",\n    \"f = ax.plot_trisurf(x, y, z, linewidth=0.2, cmap='viridis')\\n\",\n    \"plt.xlabel('hour')\\n\",\n    \"plt.ylabel('sec')\\n\",\n    \"plt.colorbar(f)\\n\",\n    \"ax.invert_yaxis()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/.ipynb_checkpoints/MarketImpact-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Market Impact\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import environment\\n\",\n    \"import utility\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import stats\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.rcParams.update({'figure.max_open_warning': 0})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tmin=9.5*1e9*60*60  # 9:30\\n\",\n    \"tmax=16.5*1e9*60*60 # 16:30\\n\",\n    \"symbols_liquid = ['AAPL', 'INTC', 'MSFT', 'SPY']\\n\",\n    \"symbols_illiquid = ['ABBV','WRB-C', 'BPOPN','GDXJ', 'ALPN', 'EOS', 'VFMF', 'PIN', 'EWGS', 'WNS']\\n\",\n    \"\\n\",\n    \"symbols = symbols_liquid + symbols_illiquid\\n\",\n    \"\\n\",\n    \"dates = ['01302019','03272019','05302018']\\n\",\n    \"LOCATION = '/Volumes/LaCie/'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def avg_price(q,book_slice):\\n\",\n    \"    \\n\",\n    \"    total_liquidity = np.inf\\n\",\n    \"    \\n\",\n    \"    if q>0:\\n\",\n    \"        if q > book_slice['1_ask_vol']:\\n\",\n    \"            prices = book_slice.iloc[3::4]\\n\",\n    \"            vols = book_slice.iloc[4::4]\\n\",\n    \"            total_liquidity = book_slice.iloc[4::4].sum()\\n\",\n    \"                \\n\",\n    \"            remaining_size = (q-vols.cumsum())[(q-vols.cumsum())>0].iloc[-1]\\n\",\n    \"            remaining_price = prices[((q-vols.cumsum())>0).values].iloc[-1]\\n\",\n    \"\\n\",\n    \"            avg_price = (((vols.multiply(prices.values))[vols.cumsum()<=q]).sum()+remaining_size*remaining_price)/q\\n\",\n    \"\\n\",\n    \"        else:\\n\",\n    \"            avg_price = book_slice['1_ask_price']\\n\",\n    \"            \\n\",\n    \"    elif q<0:\\n\",\n    \"        q = -q\\n\",\n    \"        if q > book_slice['1_bid_vol']:\\n\",\n    \"            vols = book_slice.iloc[2::4]\\n\",\n    \"            prices = book_slice.iloc[1::4]\\n\",\n    \"            total_liquidity = book_slice.iloc[4::4].sum()\\n\",\n    \"            \\n\",\n    \"            remaining_size = (q-vols.cumsum())[(q-vols.cumsum())>0].iloc[-1]\\n\",\n    \"            remaining_price = prices[((q-vols.cumsum())>0).values].iloc[-1]\\n\",\n    \"            \\n\",\n    \"            avg_price = (vols.multiply(prices.values)[vols.cumsum()<=q].sum()+remaining_size*remaining_price)/q       \\n\",\n    \"        else:\\n\",\n    \"            avg_price = book_slice['1_bid_price']\\n\",\n    \"    \\n\",\n    \"    else:\\n\",\n    \"        avg_price = (book_slice['1_bid_price']+book_slice['1_ask_price'])/2\\n\",\n    \" \\n\",\n    \"    if q>total_liquidity:\\n\",\n    \"        return np.nan\\n\",\n    \"    else:\\n\",\n    \"        return avg_price\\n\",\n    \"    \\n\",\n    \"def mkt_impact(ticker, date='01302019', plot=True):\\n\",\n    \"    Q = np.linspace(-100000,100000,200)\\n\",\n    \"    T = np.linspace(0.3,0.7,11)\\n\",\n    \"    P = np.zeros((len(T),len(Q)))\\n\",\n    \"    a = environment.ts(date,'NASDAQ',ticker,PATH=LOCATION,levels=20)\\n\",\n    \"    \\n\",\n    \"    for i,t in enumerate(T):\\n\",\n    \"        book_slice = a.book.iloc[int(len(a.time)*t)]\\n\",\n    \"\\n\",\n    \"        mid = (book_slice['1_bid_price']+book_slice['1_ask_price'])/2\\n\",\n    \"\\n\",\n    \"        for j,q in enumerate(Q):\\n\",\n    \"            P[i,j] = (np.abs(avg_price(q,book_slice)-mid)/mid*1e4)\\n\",\n    \"\\n\",\n    \"    df = pd.DataFrame(P, index = T, columns = Q)\\n\",\n    \"\\n\",\n    \"    try:\\n\",\n    \"        if plot:\\n\",\n    \"            m = df.dropna(axis=1, how='any').mean()\\n\",\n    \"            s = df.dropna(axis=1, how='any').std()/np.sqrt(len(T))\\n\",\n    \"            plt.figure()\\n\",\n    \"            ax = m.plot()\\n\",\n    \"            ax.fill_between(m.index, m - s, m + s,alpha=.25)\\n\",\n    \"            #plt.tight_layout()\\n\",\n    \"            plt.ylabel(\\\"mkt impact bps\\\")\\n\",\n    \"            plt.xlabel(\\\"size (shares)\\\")\\n\",\n    \"            plt.title(a.stock + ' ('+ str(int(mid))+'$) ' + date)\\n\",\n    \"            #sns.despine()\\n\",\n    \"            plt.show()\\n\",\n    \"    except:\\n\",\n    \"        pa\\n\",\n    \"    \\n\",\n    \"    return df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Results\\n\",\n    \"\\n\",\n    \"For some securites the market impact is defined as \\n\",\n    \"$$I_t(q) = \\\\frac{\\\\overline p_t(q)-M_t}{M_t}$$\\n\",\n    \"where $p_t(q)$ si the average price aid when sending a merket order to the exhange of $q$ shares, and $M_t$ is the mid price at time $t$. \\n\",\n    \"$p_t$ is computed using order book data at time $t$. \\n\",\n    \"For each security is recorder $I_t(q)$ for different times troughtout the day and in the picures is highlighted the mean $\\\\pm \\\\sigma$ \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAQABJREFUeAHs3QecHGX5wPHnciW5Sy69F9I7vQlIhxBQRIE/IigCFlApIoggRQGRTgA7JYACIlVASmhKAhh6J+VCes/lUq73/b/P4Awze7t7uztb537v53PszOzM+77znb0j99zzvm9ByBShIIAAAggggAACCCCAAAIIIIAAAggggEAgBboF8q64KQQQQAABBBBAAAEEEEAAAQQQQAABBBCwBAgA8kFAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBBBAAAEEEEAAAQQQQAABBAgA8hlAAAEEEEAAAQQQQAABBBBAAAEEEEAgwAIEAAP8cLk1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyGUAAAQQQQAABBBBAAAEEEEAAAQQQQCDAAgQAA/xwuTUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIZQAABBBBAAAEEEEAAAQQQQAABBBBAIMACBAAD/HC5NQQQQAABBPJZ4IknnpCCggLr6/HHH8/nW8l431955RXL7d5770247X333de6dtq0adLS0pLw9VyAAAIIIIAAAgggkHsCBABz75nQIwQQQACBLiCgAZqf/OQnsueee8qgQYOkpKRESktLZfDgwdaxk08+WW655RZ55513JBQKRRW54oornCCZHSxzv2qdw4cPl0MPPVR+9atfyfLlyzvU9Ze//MWpo3v37rJgwYIO58Q6sHDhQunRo4dTx+233x7r9Ljea2hokJ/97GfWufvss48cd9xxUa/bunWrPPLII5bnfvvtZxmqZ+/evWX8+PHyrW99Sx544AFfwaympiZ59NFH5aSTTpKpU6dKnz59pGfPnlb9Bx10kFxyySXy4osvSnNzc8R+aiDO/VwS3dbPS6bKddddZzWlz/X3v/99Wputra2VP//5z3LIIYfIyJEjRT9/+qqfV/1c6vvxFv0+Wbx4sdx///3y05/+VPSzUFZW5riPGTMm3qqs8zT4OX/+fJk1a5aceuqp8qUvfUmGDRtmfZ8WFxfLwIEDre9V/T5+9dVXE6rbPnnVqlWi38N77LGH9XNAv1/1M/t///d/8s9//jPm975dh/2qz+t3v/udfPOb3xQN3upn1N1PNXn//fft0xN+ffnll+W73/2uTJo0yfrs9+/fX3beeWe58MILZdGiRQnVt3nzZpkzZ4785je/kWOOOcb6GeX+nkj2897a2mo9f/UbPXq09fzVQb9nf/jDH8rcuXMT6icnI4AAAgggECgB848lCgIIIIAAAghkSMAE10ImoKURvbi/pk+fHrV3v/71r+OuR9vs1q1b6IILLgiZgJZTZ3t7e8gEYJx6tH9tbW3O+7E29DyTMeZcawI3Ia3Pb7n++uudOk2gIGJ1NTU1oaOPPjpkgn3OubFcTQAoZAIAEeuKdfDf//53yAQ94mrj7bffjljVPffcE9f10fpvgjsR64128D//+Y/VnrabTDnwwAOt6/v27RsyAdZkquj0mv/+97+hsWPHxnQZN25c6I033ui0LnU3Ad+YdZmAUKf1uE8wQeWY9YU/q8MOOyy0cuVKdxUxt++6666QCSLHbOPwww8PrV+/PmY9L7zwQkh/RoT3J9r+CSecEKqqqopZp/vN7du3h0488cSY9ZtAY+iaa65xXxZ12/3zIlof9fObaDHBzdDEiRNj9lPbM39cCW3bti3R6jkfAQQQQACBvBcoMv8jpCCAAAIIIIBABgQ0+0Yzm8wvn05rQ4YMsbKIhg4damUqmV/M5ZNPPpHPPvvMyf5xn+9cGGFDM/2OPfZYzzt1dXWimUFvvfWWVZ8JzsnNN98sJqhgZcrYWTcmGCE77bST1NfXiwm4yG233eZk4HkqDNvRDDHNktKi2VZ33nmndR9hpyW0W11dLSYAaF2z6667ysyZMyNer9lhTz/9tOc9t6dmcH3wwQfy0UcfWeesWLFCTJDGyqwygUPPddF2/vrXv8r3vvc9UTctmlmoGYk77LCDdb/6vDRjUjOgzL8Ko1VjZSCdddZZUd8Pf0OzDTdu3Ggd1uywKVOmhJ+S1v2LL75Y5s2bZ31Wb7rpJrn66qtT2t6HH34oRxxxhJPhp5lq+r2h2X+rV68WE3QVzeZatmyZdd7rr78uO+64Y9Q+6GdBPzfpKvrcNYtswoQJoplv+qw3bNhgfa9oNpsWzZDbf//95bXXXrM+H7H6cvfdd8sPfvAD5xQTaLXuX7PV9PvfBDSt91566SXr86/336tXL+d898a7774rn376qXNIv6c1M08z9fr16yeVlZVWn/RVi2bL6mdWs+EGDBjgXBdpQ7+H9GeKPg+76HPYfffdpbGx0cp81J8lep5mweqrZhrHKuvWrYv1dlLvqdfBBx9s/fzSCtRAs6u1r+aPFKKfN/3S8ve//13WrFkjJnBqZZxaB/kPAggggAACXUEg70OY3AACCCCAAAJ5IGCGhoZMEMfJTjHBupAZ4hc1027Tpk0hE0wLaSaWCYpEvUN3BqAZihr1PBME65Al9PDDD3vOv/XWW53+mWBeyAQhPe+H75iAmieDyQQNw09Jat8EnJx+qEG0oplR5t9qIRPkCJnhjSET7It4qhme6ck000wxE7yJeK77oJmD0MqY1DaKiopCl112WUizoSIVrc8EQ0MmYBXp7YSOaXaWO6vRDOvs9Hq95oYbbgiZIEjIDCm3+qv91ozP8vJy67NnhkWG/vjHP4ZMsLLT+jSL087O0yxAE2Dr9Jp4T9DvBTPM1XnGu+yyS2j58uWey3Vfj+s96JdmYJrgkucc946d8aifhRkzZoRMMMr6/jLDmZ06Es0ANIE865nrqwl2uZtztrVPs2fPDpmhu047X/3qV533I22ov2bM2ff27W9/u4OvCSZan2v7nNNPPz1SVdaxa6+91qrLBMut52sCkh3O1YxftSgsLHTaNUOFO5wXfuDyyy93zjfD/EMPPvig5xSt1wwBds4xgbeQGb7rOSd8R5+D3v9uu+0WOuOMM6yfc/q9a9+rviaSAWj+yGH9jLSv1/rNHzzCmw2pqX5v2Oedd955Hc7hAAIIIIAAAkEW0L9gUhBAAAEEEEAgzQIabLN/8dRggZmrLO4WYwXi4g0AamMm6yVkMoycfuhQPHfR4bxm3jTnfQ0mxRrOazK4nHO//OUvRw1mutvobNtkfYX0F3i10uGROsw3WtGgl8k2ihqUc1+nASX3EFGT4eZ+u8O21m2yCa1+aBBNg4GZKhqksz8rGigxmVsxm9ZgiZk70rnGvjbWq5ljMWad+qaZn82p8w9/+EOn58d7gvv+NGAXbYiryRTzBMHM3JJRm9iyZUtoyZIlHd53D73Wz1W6iskUdaw0CGayGKM2pUNw7WcT6/vm2Wefdc7TwJ1OHxCpPPbYY3F/Ps18hk6d2odYQ8tNBqonwG/mZIzUvHXMPUQ4/OdK+EUmEy8U6fNnm+hrIgFA9x8M9GdGRUVFeJPOvgYGNZivbWiQfenSpc57bCCAAAIIIBB0AQKAQX/C3B8CCCCAQE4ImOF+zi/eZlGKlPUpkQCgNqqZcvYv2hpUCM9o0+wkzfSxz4n2S787sKLnJxLQjHXzzz//vNN2PBlKseoKf+8Xv/iFU7cZ7hz+tmdfs4Nsg0xnCu29995O22b4padf4TvqboaGOudrnzWzSudG1G0N4urnTbPp7MCHHo8UgIlUt21ghnyGv530vlmgwunvb3/725j1mKHHzrlmWGvMcyO96f6cpjMAqFmN7izAf/3rX5G6Y2WeakDZdjVDeyOeZx/UOQDtc88991z7cNKvGmDX7GO7TjMdQNS6NKPUPk8zMGP9MUDnPnTf13vvvRe13mhv2G3payIBQLM4i9PPeIz0+8FuS7MXKQgggAACCHQVAVYBNv8CoCCAAAIIIJBugbVr1zpNmECEs53pDV0Z1S46N5auQuoukydPliuvvNI5ZIJm1pxszgGzoXPTnX/++c6hq666yppvzDngY0NX67XLN77xDXszJa8m28qpR+cDjFZ0bjOd+0+LCZJaq5xGOzfVx3UuQZ2v0S66+mysos/KXil31KhR1rx9JvgiZqEX6zK93gzbtOZCNMPKRed61Pnh4ik6h5yuJqtF69S5JP0WndtS55+zy2mnnWZvRnx1v69zOeqcgLlYdA5Dnb/PLiZz1d70vD711FPOfJLq6/5+9Jz4vx33/Zss1EinJHRMP8+6mrFdYn0fuNvTfui8etGKzompczjaRVcwzkTRn2G6UrpdvvKVr9ibUV/NEG3nPZM96WyzgQACCCCAQNAFCAAG/QlzfwgggAACOSFgsmOcfpjhqM52pjd0UQB3ibRwggaP9tprL+s0ff/MM890XyJnn322mJVhrWMmW80TDPScmOCOLrRhhj06V7kDCs5BHxvuAIYGDqIVXVjEvj9d8EMXV8lU+dvf/uY0ZeYrk1gBDfVyL4Ki1x5wwAHO9eEb+uy///3vW4shmKzN8Lcj7rufgbutiCfHcdC9mIQGwDqzHTFihJiVXZ2a3dc7B3NgQxcCsRfZ0O6MGTMmYq9MZptz3GRnOtvRNszq3M5bGqzXAKrfEs/3gQbBdTEguyTa10w9JzNU31rkw+5nPH9ccZ+jAeVYQVC7Xl4RQAABBBAIgsAXv40E4W64BwQQQAABBHJUwCx64PTMDA/0ZEE5b2Rgww5s2U25s5bsY5olpKuU6sqnWp577jm57777rG3NCtIVarXo+3qenp+Koqsk2yuqatBHV/RNZfn444+d6jRbLlrRFVftoivwatGVaS+99FJrpWSzqIbolwawNKCmq+WmomhAz3bW+k4++WTRzLJoRYMfdgBXV4g1i8BEOzXp4+6A4osvvph0PfaF7ixCXUk2nuI+z319PNdm4hwzbEg0U9YOKms2nB1AD2/f3X/3fYWfZ+9rgNT9feC+3j4n0dd4vg/M0HInU1EDhmZYeafNuO8nFf3stEFzgtr7Le4VlP3WxfUIIIAAAgjksgABwFx+OvQNAQQQQCAwAu7hrGb+NTGr+8qNN94o7qHBmbhZd3BLA3fRAmE77rijmFVvnS6ZefBEgwI/+clPnGNmhVCZPn26s+93480333SqiHeYqnNBJxvhwTUzt1rUK95++23nPQ3mPPLII6Ie11xzjXzyySfWkFsddmsWnbACoBp4Mws7iFmN1LkumQ3NmjILtTiXdjb81znRbGjwSe8x1cWsLOtUqS5+Ay76GbKLOxPLPhbpVZ+BXXSIdC4UM5eemEVK5PHHHxfN0jNzDVrd0oDtn//8ZzHzLUbsZrbv/7///a/1ubU7F+37wN1Ps8CMxJMx6n5OZlEWT0ak3V6qXwcMGCDu7OrwKQ0itRd+TqaClZH6wjEEEEAAAQQyKUAAMJPatIUAAggg0GUFNEjwta99zbl/zd7SrCENwE2ZMkW++93vyu9+9ztr/jcNLqSjaLDRnttO69csJbMybtSmzEq5YhaPsN7XX+j1fLNiq7WvgSF9P5VF53izi5qksvzpT38SO3ikAYMf//jHUavXbD+7zJ8/38rE00w7De4cdthhYhZ0sQJ+Ghixi2ZFHnnkkdLS0mIfSvjV/WzMIiWdZl1p8MMe0q1B5XTMuzZhwgQnmLVt27YOc0YmepP6ubeLO7PNPhbpdejQoc5h/Rxmq6iFZsPpl34WdHjy8ccfL3PnzrW6NGzYMNEsyWjDtvUZ6ZddMn3/GiDWQL5ddC7APffc0971vPp9TlpZJp6VBlrdfyyYM2eO5z4i7binGchUPyP1g2MIIIAAAghkWoAAYKbFaQ8BBBBAoMsK/P3vfxezqqvn/jWjSrNtdOinWaHXmqC/b9++YlaqFPd8YZ6LktjRzLWZM2eKWfXXufpnP/uZsx1pQ4McmtlkZzPZCxvovg79tY9HujaZY+65EUeOHJlMFRGv0SF+v/zlL533dNhurMxFDXTZRRcJ0ICsBkv0Ob300kty5513ysMPP2wNC3YHQV977TX5zW9+Y1+a0KtmFLoDeLroQmdFA5lmpWTnNL2v22+/3RNkct5MckPb0MCWXfzOl2YvWKL1mVVz7WpjvrrPc18f86IMv6n2mhEaaxh2eN/d9xWru+7zwuuIdV34e/rZtLNb9bnedNNN4ac4++523O07J0TYCD/PXUeE01N26Otf/7pTly5y4/454rzxvw2dZkAzet3F/rnmPsY2AggggAACQRQgABjEp8o9IYAAAgjkpIDO06ZDBp955hmZMWOGZ+iau8M6lPShhx6yVtXUX27D5+1zn+ve1gCELtDh/vre974nupCFZsm457rSwJEOW+2s6NxfF110kec0DXrFMyeY56I4dnR1YbtodlsqigbzdPi1HYzQuQVnzZoVs+rwobyapfn888/L2LFjPdfpHIjXXnutJ5vw1ltv9QRZPRfE2NEMQrtdDax++9vfjnH2F2/pKsD2UFrNUvzRj34kamcP1dYVhfW5+xkePHDgQKfBDRs2ONvJbOjiEnax55i096O9du/e3XnLnUHnHMzQximnnCJnnXWW9aUBv6OOOkr69+9vtT579mxrsRL3HI7h3XLfu76XyfvXeUfdq3vr9/T+++8f3kVn393XZPqpFWXqWZ1zzjnOCswazNOfrbpqdXjRuTo1OzM8SzdT/QzvD/sIIIAAAghkWiDyBCWZ7gXtIYAAAggg0IUE9JdQ/dJVQ1955RXRebneffdd0ewUO1Blczz11FPWyq46FFUXnohVdE6yP/7xj7FOsYYvaqbhddddZ23HPPl/b+qQ19/+9rfOqRr8SEexA2Bad1lZme8mNIihAVR75VQd7qyBNg3Exio635m7LzoXYqTFUuw6NLNKMyKbmppEAxC6aIpmcCZS3MN/NVMz3uGhep5mHmqg116kQwMa9rxmOh+dful5OlxVsz51KGsixf0s3C6J1GGf655Lrrm52T4c81Vd7RKeZWYfz8Trr3/96w7N6D1o0E9Xztbh8TqUX7MkdX7M8OK+d30vU/evWX8nnXSSM3+jzvt31VVXhXfPs+/uazL91Moy9aw04K0rYGt2tQa6ly5dag1t1hXKNdNXj3344YfWz1ft1+TJk62FixYsWKC7nf5ctU7iPwgggAACCARAgAzAADxEbgEBBBBAID8FBg0aZGXh3XLLLdZKsprp9+qrr1rBHPfwWs3g0hVokyn6i7zOoXbwwQdbi3poMEzbc2dVJVNvuq/xu9iEDts98cQTnRV61UGDqe75wqLdQ3iA0L2AS6RrNAChi7rYRQO6iZSVK1c688jpdfEM/3XXr8OlX3jhBdEgsWb+uVects/T7EqdB3Hq1KlW1qJ9PJ5Xv8/C3YbbNt7MK/d57uvd9WZrW7PjNCCu/jpkXosGCt2L7dh9C++7+77scyK9us8LryPS+e5jGuTSTEU7cKvzeOpQc/fPF/f59ra7HXf79vuRXsPPc9cR6fxUHjvmmGNEsxztzGH9zOqiQhqYv/fee53gn67qrfMEulcu1ykXKAgggAACCHQFAQKAXeEpc48IIIAAAnkhoL+U67A8HU6oCwu4f4HWeefCf8EOvymdf0x/8XV/6TWamaTzCWqm2rhx48Ivy5n9nj17On3p7F6dEyNsaMaPBtE04KdFXXXer1jzs7mrsYMIekyHv7oX+3Cf597WwJpdEl3ZWTPI7CCbLurhXizGrjOeVx3qrRmgGuS1FzrQ4I/et875pkUDo5dccknM+d/C23I/C/czCj8vnn23rXvId6xr3cOO7SG3sc7PxnuababZf1r0WWqQPbxoRpw7Ky7d969z4elwWHtBD82G0+CX++dKeB/tfb/PSevJ9LPSrGq955tvvtlarEe/bzUoq6+6CJPOD/jGG2/ImDFjZPPmzfatRl0J3TmBDQQQQAABBAIiQAAwIA+S20AAAQQQCJbAfvvtZwVq7LvS4az2BP72saC9uld7df+Cnuh96jx4DzzwgHWZBr50eODRRx8ddzXuFYjjCZZoxe7h2YkuKqD9s4sOHU5FdqYdaNK5HnWYeUVFhScAqllq8a7SqkPV7eJ+RvaxRF51+KVdNPMxnrJq1SrnNPezcQ7myIYG2+wSKQNQ38vU/WsQWles1mkBtGhWqA4Rjzco5+7npk2bxD0noFVhhP+4n5O2oxnOmS76fXj++edbi/VogFWHL+vrv//9bytTU/8YoJnW9mrm2j/NiqQggAACCCDQFQQIAHaFp8w9IoAAAgjkpcCRRx7p6bf7l1bPGwHZcS+ysWbNmqTuSue402xJu+iquDr/WSJlxx13dE4Pn5PReSNswx30izVfYNhl1vyPuniLXRId/mtf19mrBoCefPJJJ9upvr7emTMw1rWaTenOwNPsKT/FnSmpc17GU9wLOrivj+faTJ6j2Zt2sbPu7H371d3/eO5fA3juTEH39Xad4a96vgb/7NVwdYi4rl7tXs05/JrwfQ0A2lmjmtH4wQcfhJ/SYT9fnpN7iL5+r06bNq3DvXAAAQQQQACBIAoQAAziU+WeEEAAAQQCIeCeiF9vKBWZYbkM456fb/HixQl3VedJ1FV47aLDMHUBk0SLBk/sopmI7gw4+3j4q73ohh7XVYPjLe7sP81u06Gk6Soa7HAHlXWxis6KBid12LAWnSvNXnG4s+uiva9DMe2iz7izoLYGwNwB0kMPPdS+POde3fcSLdPOff+amdlZ0akA7LLDDjt0uoCLBh41E9H+/tHhrxr8SzRwqz97dEi5XRLtay4/J11h3S4nn3yyZz5A+zivCCCAAAIIBFGAAGAQnyr3hAACCCAQCAFdudJdNAAQ5OIOfoXfe2f3rasUX3PNNc5pusrpeeed5+wnsqHzMLrn/XviiSdiXq5DaXXxFrvEO9egrm7rDkaceuqpdhVpe9WVkO2iC1h0VtzPIRVDJSdOnOjJuHKvfhypL+73d9ppp5yew/Lpp592biFapp4uVmFn1mmQTueki1V0AQu76IrWsUp1dbXoCtIff/yxdZpmJOqwX/dw3ljXh7/nXvzG3Y/w83R/9erV8vLLLztvua91DubAhgbqH3zwQasnBQUFotMFUBBAAAEEEOgqAgQAu8qT5j4RQAABBLIqMGvWLCsTJ95O6BBNd0BryJAhsuuuu8Z7eV6et9tuu1mLbmjndSEL99DHWDd02223WSsc2+f84he/kMsvv9zeTfhVAzRnn322c50GFzW4Eq3ofHr2HGk6R54GYeIpOiR327Zt1qna5imnnBLPZc452ic708s5GGOjra3NWRxET5s0aVKMsz9/yx3YdM9x1+mFMU7QlYrtctNNN0V9zjr0WN+3y1lnnWVvpv1Vsx63b98edzu66Mpjjz3mnH/88cc72+4N/T4+7rjjnEP6WbUXgHEO/m9DVxbWLy26am2sYJX+vPjqV78q7777rnW+zoWnC364s2qtNxL4jwak7UVf9HOmi2hEKxdddJHo50vLvvvuK7vvvnu0U7N2XL/XNOPPzmj94Q9/6MsnazdCwwgggAACCCQpQAAwSTguQwABBBBAIBGBt956yxqap1lUf/rTn6IGPbTON99801qwwc7k0WP6C7adOaT7QSx6f7qSp1104v7Oyt133y06759dNEh0/fXX27tJv+pCAiNGjLCu18UqdOhs+KIVusCABhr/8Ic/OO1oMDB86LbzZtiGO7vt8MMPd9oLOy3qrmYe6nyFP/7xj+XTTz+Nep6+oXMZ6vyC9lBlXeXVPRw12sXuZ5DIQirR6tPjZ5xxhrUohW7rkFVdqTjcVvf1s2AvVKLByu9///t6SUaKeumclL/61a9iBll1QYmrr77aCurZgbwJEyaIBpeiFV2NW1en1aIBVg201dXVeU7XVbs1WGUXXWE42lx1mkmqGXevvfaadbouAKPZiO6MWrueRF41C1a/D+xy7rnnysMPP2zvWq8tLS1y8cUXO1l1evDaa6/1nJOJHR3uf8cdd1gLfERqTz11YSV7LkNdDf3GG2+MdCrHEEAAAQQQCKxAgfnHSiiwd8eNIYAAAgggkCMCurqre7indksXZpg+fbqV9aarU+pcc/oLqj15v931Y4891vrFW88JL1dccYVceeWV1mEdehrPXF3hdXS2r3PFuRfo0P4lOqdYZ23Y72vGk51BpyvYhpvZ5+mrBkg1K1IXqtCi2UoaTNGhffGUn/70p6JDUqMVXXVZg2R2cEaHzB544IGWhWaH6fxs7izF73znO3LfffdFq85zXK/TxRnsbCRdtdgd8PGcHGUn/LlottcBBxxg3ZNmz1133XWinzsNSGq2oQar7KLZXJ0F1HTuPTtLULMz3Ys82PUk+6pDi3Wotb3IigbEdO5FDbrqAjAaeNTgkhYdtqyr6roXZ4nUrs73+M4773je0gCiDk/Vom1ECqKpxZ577um5TrPF3It6DB8+3MoW06BYWVmZ1W/NUlUTDQTbRTNAte/RhgDb582ePdszP6W2pfPm6b0uWLDA+iOAfa4+Vw3uuVeatt/TV80idAezNPtOs/DiKRoItn9+RDpfn4EGv92BYB2KrW1o1uu8efM88zhqXRo0jVWeeuqpiOe4h5vrz8bwFbh1+LQO7Y9UNDtSF/zRn5G77LKL6Hyaer3O4alZke75LvVnmf6cDPqUCpGcOIYAAggg0MUFNABIQQABBBBAAIH0CpjslJD5xVP/6Bb3l8nkCZlfeEPml/ConTMZZ059JgAY9Tw/b5iAn9OG9l/301VMQCxkFpqw2jMBvZBZXTdqUyarx9OvRGz1XL2+s2JWDA2ZYETMdkzQIWQyNENmCGRn1Tnv33zzzU6dJugTMkM4nffi3TABvZAZlhsymZNOXZ0ZmCBYSNuOp5jMNqfe3//+9/FcktA5atvZ94TJ1ArNnz8/rnr189/Z/Ud6P9LnwAR4Q/pcI50f7ZgZ2hsywcu4+qonmdWqQ/oZj1afHjdB0ZBZCCVmnSboHbOOWPXr91pnxQRDQyYYH7MN/VyZofKdVWW9f88998SsK1p/9T6jlTPPPDOuOk1APGSC49Gq4TgCCCCAAAKBFuiYSmD+r0tBAAEEEEAAgdQK6JBA/frkk0+szDGd/H/RokXW0EfNJjP/2rAyfDSDSDN+NPPshBNO8GQhpbZHuVmbznV2zjnnyM9//nMr804n7I81nDLdd6GZVB999JH8/e9/t7IwdQjtpk2brGxDXRFXs9Z0SKudKRdvf9zDfzXTUYdtJlp0VV7NmFy7dq0888wzVlaTfr40g07nB9Q52dRTs8p0WOrBBx9s9VW3Oyv6edTh1Vp09WAdPpzqYtvqSsg6tLSiosIaEqxZaeqpLjr0NTwTLNX9iFSfmunwZF1BV7MP33//fVm2bJmVpatDbjXbVLP2NIP3S1/6kpW9GY+ruy3NWDziiCNEswH/9a9/yapVq6zMwmHDhlkZdppRqkN7481oddedym19/pqJq9+H+rk1AVkr608zKnXFa83Y1WzSzrIeU9mn8Lo061Cfgy5Eot+vmmGrGa/6jDSrVL9PNRt2jz32CL+UfQQQQAABBLqMAEOAu8yj5kYRQAABBBDIDwGT9WcNs9UAjA7ns+ftyo/e50YvdYijBpFNtlVSwbvnnnvOmY/xkksuEV0IhYIAAggggAACCCCQvwIsApK/z46eI4AAAgggEEgBnetM5zXTovOC6WqmlMwK2AupaJbhhRdemNnGaQ0BBBBAAAEEEEAg5QIEAFNOSoUIIIAAAggg4FdAhwHbC41Em/jfbxtcH1lAF3bQBU60XHbZZaJBQAoCCCCAAAIIIIBAfgsQAMzv50fvEUAAAQQQCKSAzol3yy23WPemc449/vjjgbzPXLypiy++2OqWzul27rnn5mIX6RMCCCCAAAIIIIBAggIsApIgGKcjgAACCCCAQGYEdAEEXYyCklkBszpvZhukNQQQQAABBBBAAIG0C7AISNqJaQABBBBAAAEEEEAAAQQQQAABBBBAAIHsCTAEOHv2tIwAAggggAACCCCAAAIIIIAAAggggEDaBQgApp2YBhBAAAEEEEAAAQQQQAABBBBAAAEEEMieAAHA7NnTMgIIIIAAAggggAACCCCAAAIIIIAAAmkXIACYdmIaQAABBBBAAAEEEEAAAQQQQAABBBBAIHsCBACzZ0/LCCCAAAIIIIAAAggggAACCCCAAAIIpF2AAGDaiWkAAQQQQAABBBBAAAEEEEAAAQQQQACB7AkQAMyePS0jgAACCCCAAAIIIIAAAggggAACCCCQdgECgGknpgEEEEAAAQQQQAABBBBAAAEEEEAAAQSyJ1CUvaZpuSsLNDY2yscff2wRDBo0SIqK+Ch25c8D944AAggggAACCCCAAAIIIJAegdbWVqmsrLQq32mnnaRHjx7paYhac1qAqEtOP57gdk6Df3vvvXdwb5A7QwABBBBAAAEEEEAAAQQQQCDHBN566y3Za6+9cqxXdCcTAgwBzoQybSCAAAIIIIAAAggggAACCCCAAAIIIJAlATIAswTf1ZvVYb920b9ADBs2zN7lFQEEEEAAAQQQQAABBBBAAAEEUiSwfv16ZwSe+3fxFFVPNXkiQAAwTx5U0LrpnvNPg38jR44M2i1yPwgggAACCCCAAAIIIIAAAgjklID7d/Gc6hidSbsAQ4DTTkwDCCCAAAIIIIAAAggggAACCCCAAAIIZE+AAGD27GkZAQQQQAABBBBAAAEEEEAAAQQQQACBtAsQAEw7MQ0ggAACCCCAAAIIIIAAAggggAACCCCQPQECgNmzp2UEEEAAAQQQQAABBBBAAAEEEEAAAQTSLkAAMO3ENIAAAggggAACCCCAAAIIIIAAAggggED2BAgAZs+elhFAAAEEEEAAAQQQQAABBBBAAAEEEEi7AAHAtBPTAAIIIIAAAggggAACCCCAAAIIIIAAAtkTIACYPXtaRgABBBBAAAEEEEAAAQQQQAABBBBAIO0CBADTTkwDCCCAAAIIIIAAAggggAACCCCAAAIIZE+AAGD27GkZAQQQQAABBBBAAAEEEEAAAQQQQACBtAsQAEw7MQ0ggAACCCCAAAIIIIAAAggggAACCCCQPQECgNmzp2UEEEAAAQQQQAABBBBAAAEEEEAAAQTSLkAAMO3ENIAAAggggAACCCCAAAIIIIAAAggggED2BAgAZs+elhFAAAEEEEAAAQQQQAABBBBAAAEEEEi7AAHAtBPTAAIIIIAAAggggAACCCCAAAIIIIAAAtkTIACYPXtaRgABBBBAAAEEEEAAAQQQQAABBBBAIO0CBADTTkwDCCCAAAIIIIAAAggggAACCCCAAAIIZE+AAGD27GkZAQQQQAABBBBAAAEEEEAAAQQQQACBtAsQAEw7MQ0ggAACCCCAAAIIIIAAAggggAACCCCQPQECgNmzp2UEEEAAAQQQQAABBBBAAAEEEEAAAQTSLkAAMO3ENIAAAggggAACCCCAAAIIIIAAAggggED2BAgAZs+elhFAAAEEEEAAAQQQQAABBBBAAAEEEEi7AAHAtBPTAAIIIIAAAggggAACCCCAAAIIIIAAAtkTIACYPXtaRgABBBBAAAEEEEAAAQQQQAABBBBAIO0CBADTTkwDCCCAAAIIIIAAAggggAACCCCAAAIIZE+AAGD27GkZAQQQQAABBBBAAAEEEEAAAQQQQACBtAsQAEw7MQ0ggAACCCCAAAIIIIAAAggggAACCCCQPQECgNmzp2UEEEAAAQQQQAABBBBAAAEEEEAAAQTSLkAAMO3ENIAAAggggAACCCCAAAIIIIAAAggggED2BAgAZs+elhFAAAEEEEAAAQQQQAABBBBAAAEEEEi7AAHAtBPTAAIIIIAAAggggAACCCCAAAIIBEUgFAoF5Va4jy4kQACwCz1sbhUBBBBAAAEEEEAAAQQQQAABBJIX0ODfss11yVfAlQhkSYAAYJbgaRYBBBBAAAEEEEAAAQQQQAABBPJLYEVVvWyrb86vTtNbBIwAAUA+BggggAACCCCAAAIIIIAAAggggEAnAmu3NciG7Y2dnMXbCOSmAAHA3Hwu9AoBBBBAAAEEEEAAAQQQQAABBHJEYHNtk6wy2X8UBPJVgABgvj45+o0AAggggAACCCCAAAIIIIAAAmkX2N7QIks31aa9HRpAIJ0CBADTqUvdCCCAAAIIIIAAAggggAACCCCQtwL1za1SsbFG2ln4N2+fIR3/XIAAIJ8EBBBAAAEEEEAAAQQQQAABBBBAIEygubVdFm2okdY2on9hNOzmoQABwDx8aHQZAQQQQAABBBBAAAEEEEAAAQTSJ9BmUv4WbaiWppb29DVCzQhkUIAAYAaxaQoBBBBAAAEEEEAAAQQQQAABBHJbIBQKWcN+65racruj9A6BBAQIACaAxakIIIAAAggggAACCCCAAAIIIBBsgWWb62RbfUuwb5K763ICBAC73CPnhhFAAAEEEEAAAQQQQAABBBBAIJLA6i31sqm6KdJbHEMgrwUIAOb146PzCCCAAAIIIIAAAggggAACCCCQCoFNNY2yZmtDKqqiDgRyToAAYM49EjqEAAIIIIAAAggggAACCCCAAAKZFNhuhvwuq6zLZJO0hUBGBQgAZpSbxhBAAAEEEEAAAQQQQAABBBBAIJcE6ppapWJTjZi1PygIBFaAAGBgHy03hgACCCCAAAIIIIAAAggggAACsQSaWttk0YYaaW0j+hfLiffyX4AAYP4/Q+4AAQQQQAABBBBAAAEEEEAAAQQSFGhta5dF62ukubU9wSs5HYH8EyAAmH/PjB4jgAACCCCAAAIIIIAAAggggIAPgfb2kFRsrJX65jYftXApAvkjQAAwf54VPUUAAQQQQAABBBBAAAEEEEAAgRQILNtcK9sbWlJQE1UgkB8CBADz4znRSwQQQAABBBBAAAEEEEAAAQQQSIHAyqo6qaxpTkFNVIFA/ggQAMyRZ1VdXS3/+Mc/5IILLpCDDjpIJkyYIH369JGSkhIZPHiwHHzwwXLDDTdIVVWVrx7fe++9UlBQENeXnktBAAEEEEAAAQQQQAABBBBAICgC67c3yLptjb5uR+cOpCCQbwJF+dbhoPb3rbfekpNOOini7VVWVsrcuXOtrxtvvFHuv/9+mTlzZsRzOYgAAggggAACCCCAAAIIIIAAAh0FqmqbZGVVfcc3EjjS2NIm1zy7UE7ca5T84IBxCVzJqQhkV4AAYHb9Pa2PGjVKDjnkENljjz1Et4cNGybt7e2yZs0aefTRR+Xxxx+XzZs3yzHHHCMaMNxll1081ye68/zzz8vw4cOjXjZy5Mio7/EGAggggAACCCCAAAIIIIAAAvkioPP9fbapVkKh5HvcZhYO+d3LS2SJqefqZxbK2m0NctlXp0lht4LkK+VKBDIkQAAwQ9CdNaOBv1WrVkU97Zvf/KY88cQTcuyxx0pzc7NceeWVVkAw6gVxvDFp0iQZM2ZMHGdyCgIIIIAAAggggAACCCCAAAL5KVDf3GpW/K0RE79LuoRM5PCe15fL+6u3OXXc8/oKK6B4xTHTnWNsIJCrAt1ytWNdrV+FhYWd3vI3vvENmTx5snXeq6++2un5nIAAAggggAACCCCAAAIIIIBAVxZoam2ThetrpLXNR/TPAD754Tp5edEmD2XfsmI5Zd/RnmPsIJCrAgQAc/XJROlXeXm59U5jo79JS6NUz2EEEEAAAQQQQAABBBBAAAEEAiGgi3UsMsG/5lZ/i3a8uqRSHnp7tcekpKib3PXdPWX8oF6e4+wgkKsCBABz9clE6NfixYvlgw8+sN6ZMmVKhDM4hAACCCCAAAIIIIAAAggggAAC7Wa872Iz7Le+uc0Xxidrt8vt85Z56tAZ/247cVfZc0x/z3F2EMhlAQKAufx0TN/q6+tlyZIlMmvWLDnooIOktbXV6vF5553nu+enn366tQhISUmJDBw4UPbZZx+57LLLZO3atb7rpgIEEEAAAQQQQAABBBBAAAEEsiWwtLJWqhs+//052T6s2lIvs16sEF38w11O22+MHLXTMPchthHIeQEWAcnBR3TvvfeKBueilYsvvlhOPvnkaG/HffyVV15xzq2qqhL9evPNN+Xmm2+WW2+9Vc4880zn/UQ3dOXiWGX9+vWx3uY9BBBAAAEEEEAAAQQQQAABBJISWFlVJ5trm5O61r6oqrZJrp+zSBpavBmER+04VI7eheCf7cRr/ggQAMyfZyW77rqr3HHHHbLXXnv56vW4cePkuOOOk3333VdGjRpl1bVs2TJ57LHH5NFHHxWdX/BHP/qRFBQUyBlnnJFUW3a9SV3MRQgggAACCCCAAAIIIIAAAggkIbB+e4Os2+ZvznxdNfj65xfLljpvEHHvsf3lO/uw6EcSj4VLckCgwCxl7c1lzYFOdfUubNu2TewMuoaGBlm6dKk8/PDD8s9//lPGjx9vZecdffTRSTFt375devfubQX3IlXw9NNPW8HBlpYWKSsrs9oeOnRopFNjHtPgYbxl9erVMnLkyHhP5zwEEEAAAQQQQAABBBBAAAEEOgho1l7FxtoOxxM5oAuHaObfJ+uqPZdNGtJLLv3KNNHFP0qKCmSP0fkz/5/GF+wkHX7/9jzWLrVDADCPHvd9990np556qhW8mz17tpx22mlp6f3VV18tl19+uVW3bl966aUJt2MHMKNdqEOA9957b+ttfgBFU+I4AggggAACCCCAAAIIIIBAPALbG1rMir/VEjZdXzyXOudoftSf5y6VV5dsdo7pxrA+PeTKY6ZLeY9i6zgBQA8PO3kiQAAwTx6U3c0TTzzRygbs2bOnrFq1Svr3T/1fHTZt2iSa9ac//GbMmCEvvPCC3XzKXvkLRMooqQgBBBBAAAEEEEAAAQQQ6NICOmT3U5Ox19rmb4Djw++sln++710Us3dpsfzGBP8G9+7hGBMAdCjYyCMBVgHOo4elXf36179u9biurk7mzJmTlt4PHjxYBgwYYNXNisBpIaZSBBBAAAEEEEAAAQQQQACBFAg0tbbJwvU1voN/Ly/c2CH4190M9/3FzMme4F8KukwVCGRFgABgVtiTb3TQoEHOxStXrnS2U72RyBx+qW6b+hBAAAEEEEAAAQQQQAABBBDoTKDNjPddZIJ/za3tnZ0a8/33V22Vu19f7jlHp7U/99CJMn5QL89xdhDIVwECgHn25NwZeb16pecHUWVlpWze/PmcB8OHD88zIbqLAAIIIIAAAggggAACCCAQdAGdsqpiY43UN7f5utWllbVy28tLOswd+L0vj5XdR/fzVTcXI5BLAgQAc+lpxNGXRx55xDlrp512crZTuXHHHXdY8/9pnQcddFAqq6YuBBBAAAEEEEAAAQQQQAABBHwLLN9cJ9vqW3zVs6m6UW54frE0hWUQfmPX4XL41CG+6uZiBHJNgABgjjyRe++9VxobG2P25pZbbpFnn33WOmfs2LFywAEHeM5/5ZVXrBWCdfhupBWCV6xYIe+//77nmvCdp59+Wq666irrcGlpqZx++unhp7CPAAIIIIAAAggggAACCCCAQNYE1m1rkI3VTb7ar2lskevnLJJqs3qwu+w/YaB8c89R7kNsIxAIgaJA3EUAbuKKK66QCy64QI4//njZf//9Zfz48aJDfGtqauTjjz+WBx54QF5//XXrTktKSkSz9AoLCxO6cw0AHnLIIbLvvvvK1772Ndlll11EF/zQsmzZMnn00UetL02l1nLTTTfJiBEjrG3+gwACCCCAAAIIIIAAAggggEC2BbbUNcuqLfW+uqFzBt78QoWs2+5Nwpk+vLeceeA4K7HGVwNcjEAOChAAzKGHsmXLFrnzzjutr2jdGjlypNx9991y+OGHRzul0+Pz588X/YpWysrKRLMNzzjjjGincBwBBBBAAAEEEEAAAQQQQACBjArUNrXKZ5tqzZRVyTfbbhYO+eN/PpPFZv5AdxnVr1TOnzFJigoZKOl2YTs4AgQAc+RZPv/88/LMM89YWX6fffaZbNy4UaqqqkSH4WqW3q677ipHH320fPOb3xQN0CVT9thjD7n//vut4N8777wj69evtxb7aG1tlX79+sn06dPlsMMOkx/84AdOZmAy7XANAggggAACCCCAAAIIIIAAAqkUaGxpk8UbqkVX/k226Gi32Wa137dWbPFU0b9niVx05BQpKyFE4oFhJ1ACBeYbIPnvnkBRcDOZFFizZo2MGvX5vAqrV68WzWykIIAAAggggAACCCCAAAIIIBAu0NrWLp+uq/a94u8/3l4lT36wzlN9aXGh/Ppr02T0gJ6e47F2SooKZI/R/WOdklPv8ft3Tj2OrHWG3Nas0dMwAggggAACCCCAAAIIIIAAAgjEEtCcpSVm2G99c1us0zp979mP13cI/hV1K5ALjpiUUPCv04Y4AYEcFSAAmKMPhm4hgAACCCCAAAIIIIAAAggg0NUFlm+uk2313pV6EzWZV1Ep972x0nNZQYHIuYdOlOnD+3iOs4NAUAUIAAb1yXJfCCCAAAIIIIAAAggggAACCOSxwLptDbKxusnXHby7cqvcPm9phzp+uP842Wts/gzj7XADHEAgQQECgAmCcToCCCCAAAIIIIAAAggggAACCKRXYEtds6zaUu+rkUXrq+W2lyskfN2Qk/YaJYdMGeyrbi5GIN8ECADm2xOjvwgggAACCCCAAAIIIIAAAggEWKC2qVU+M/P++VmydGVVndz4wmJpafOue/rVnYbJ13YZ7lPPjB+mIJBnAgQA8+yB0V0EEEAAAQQQQAABBBBAAAEEgirQ2NImizdUS1t42l4CN7yxulGue25Rh4VDDpw4UL79pR2kQCcATLLopWMGlCV5NZchkD0BAoDZs6dlBBBAAAEEEEAAAQQQQAABBBD4n0BrW7sJ/tVIc6s3ay8RoG31zXLNswtlW4N34ZA9RveTMw4c7yv4p/0YbYJ/A3p1T6RLnItATggQAMyJx0AnEEAAAQQQQAABBBBAAAEEEOi6AiEz3neJGfZb39yWNEKdGTp8rcn821TjXThkytBya8Xfwm7JZ/5pp4b16WG+SpPuHxcikE0BAoDZ1KdtBBBAAAEEEEAAAQQQQAABBBCQ5ZvrZFu9N2svEZbm1na5ycz5F75wyOj+ZXLhzMlSUuQv/DGwV4mMGdgzkS5xLgI5JeDvOyCnboXOIIAAAggggAACCCCAAAIIIIBAvgms3dYgG6u9WXuJ3IPOF3jby0tkkRk+7C5DeneXi4+aImUlRe7DCW+X9yiS8YN6JXwdFyCQSwIEAHPpadAXBBBAAAEEEEAAAQQQQAABBLqQwDoT/FtVVZ/0HbebocN3zFsq763a6qmjb2mx/PKoqdK3rMRzPNGd0pJC0SHE3XwOH060Xc5HINUCBABTLUp9CCCAAAIIIIAAAggggAACCCDQqYAG/1b6CP7pvIEPvLlK5i3Z7GmrzATtNPNvSO8enuOJ7pQUFVjBv6JCQieJ2nF+7gn4y4PNvfuhRwgggAACCCCAAAIIIIAAAgggkHL2FrAAAEAASURBVOMCOuzXT+af3t5TH66TZz9e77nT4sICa86/0QP8zdenC4ZMHtpbehQXeupnB4F8FSCMna9Pjn4jgAACCCCAAAIIIIAAAgggkIcCqQj+za3YJP94e7Xn7nWU7nmHTzJZe709xxPdKTD1TBrSS3p1J2cqUTvOz10BAoC5+2zoGQIIIIAAAggggAACCCCAAAKBEkhF8O+TtdvlznnLO7j86KDxsvsO/TocT/TAOLPar9+5AxNtk/MRSLcAAcB0C1M/AggggAACCCCAAAIIIIAAAghIKoJ/WsetL1VIm5n/z11O2We0HDBxkPtQUtsj+5XKYJ9zBybVMBchkGYBAoBpBqZ6BBBAAAEEEEAAAQQQQAABBLq6QCqCf9UNLXLDnEVS19zm4Tx652HylZ2GeY4lszOovLuM6l+WzKVcg0DOCxAAzPlHRAcRQAABBBBAAAEEEEAAAQQQyF+BVAT/mlvb5eYXF8ummiYPxF5j+slJe+/gOZbMTt+yYhk/yN/CIcm0yzUIZEqAAGCmpGkHAQQQQAABBBBAAAEEEEAAgS4mkIrgX7sZ7vuXeUulYmOtR0/n6jvrkAnSTVft8FF6di80i36US4HPenx0gUsRSLsAAcC0E9NA0ATWmTknKAgggAACCCCAAAIIIIAAArEFUhH80xYefXeNzF9a5WlsQM8S+fnMydK9qNBzPNGd7sXdrFWDC3UJYQoCARYgABjgh8utpUdg/fYG2VzrTTtPT0vUigACCCCAAAIIIIAAAgjkp0Cqgn9zKyrln++v9SCUFhfKL46cIv3KSjzHE90pKiwwwb9yKSkiNJKoHefnnwCf8vx7ZvQ4BwSWVdZJXVNrDvSELiCAAAIIIIAAAggggAACuSWQquDfgnXb5c5Xl3luThP1zj1souzgc7EOrUeH/ZaVFHnqZweBoAoQAAzqk+W+0irQ1h6SxRtrpKWtPa3tUDkCCCCAAAIIIIAAAgggkE8CqQr+6dRLs16qEP3dy11O22+M7Dqqr/tQwts61Z8G//qUFid8LRcgkK8CBADz9cnR76wLNLW0yxIzCW3ITEhLQQABBBBAAAEEEEAAAQS6ukCqgn/VjS1yw/OLzKirNg/pV3YaJjOmDfUcS3RHg38TB/eSfmYOQQoCXUmAAGBXetrca8oFtje0yMqq+pTXS4UIIIAAAggggAACCCCAQD4JaMbeqhT8bqSjrGa9UCEbq73zru85up98e+8dfJOMG9RTBvTq7rseKkAg3wQIAObbE6O/OSewfnujVNZ4/+eUc52kQwgggAACCCCAAAIIIIBAmgT096FUJEbo6Krb5y61pltyd3XswJ5y1iETpJvPlXq1nsHlPdxVs41AlxEgANhlHjU3mk6BZZW1UsuiIOkkpm4EEEAAAQQQQAABBBDIQYHt9S2ivw+lojz63hp5fWmVp6r+Zqjuz4+YLD3Myr9+yg4DymRoH4J/fgy5Nr8FCADm9/Oj9zkioPPSLt5QI82tLAqSI4+EbiCAAAIIIIAAAggggECaBepMEoQujhi2TkdSrb66pFIef2+t59oexd3kFzMniwYB/ZSR/UplRN9SP1VwLQJ5L0AAMO8fITeQKwIa/Ksw//NjUZBceSL0AwEEEEAAAQQQQAABBNIl0NjSJos2VHdYpTeZ9haur5bb5y3zXKqLdZx76EQZPaCn53iiO8NM1t+o/mWJXsb5CAROgABg4B4pN5RNgZrGVlmRgolvs3kPtI0AAggggAACCCCAAAIIxBLQhTo0aNfcaoZC+SzrtzfIrBcrOgQST9tvjOy2Qz9ftQ/u3V3GmHn/KAggIEIAkE8BAikW2GAWBdlU3ZjiWqkOAQQQQAABBBBAAAEEEMi+QJsZ76vTHzW2+J/+aFt9s9wwZ3GH+dSP2nGoHDFtqK+bHdirRMYR/PNlyMXBEiAAGKznyd3kiMDyzXVS09iSI72hGwgggAACCCCAAAIIIICAfwGd7mjJphrzu06r78o0+Hf1MwtlQ1jyxB6j+8l3vjTaV/39ehbLhMG9pEDHEVMQQMASIADIBwGBNAjoJLgVG2tZFCQNtlSJAAIIIIAAAggggAAC2RFYWlknW+v8JzpsNcG/3zyzQNZua/DcyBizUu/Zh0yQbt2SD9z1KS2WSYPLCf55ZNlBgCHAfAYQSJuAvShIeyqWxEpbL6kYAQQQQAABBBBAAAEEEOhcYPWWeqmsaer8xE7O0ODf1U8vkHXbvNMmDS7vLhfOnCI9igs7qSH62+U9imTy0HJfAcTotfMOAvktQAZgfj8/ep/jApoav7yqLsd7SfcQQAABBBBAAAEEEEAAgegCOs/5mq3ebL3oZ0d/Z0udyfzT4J+pz12GmMU6fnX0NOnfs8R9OKHtXt2LZIoJ/hX6yB5MqEFORiDPBAgA5tkDo7v5J7Cpukk2hs1rkX93QY8RQAABBBBAAAEEEECgKwpU1TbJihQkNdjBv/Vhwb+hvXuY4N90GdCre9K8ZSWFMmVYuRQVEuJIGpELAy/Ad0fgHzE3mAsCuihINYuC5MKjoA8IIIAAAggggAACCCAQp4D+DvPZploxa3/4KhpE1My/8AU/NPh3uc/Mvx7F3WTqsN5STPDP1zPi4uALEAAM/jPmDnNAQP+HuWRjjTS1tuVAb+gCAggggAACCCCAAAIIIBBboL65VRZvqBG/U5pbwT+z4Ed48G9YH//Bv5KiAiv4V1JEaCP20+RdBFgEhM8AAhkTaG4NmSCg/vXM55/PMtZjGkIAAQQQQAABBBBAAIGuKKCJCwvX10hrm7/fXTabzL+rTObfRjMtkrsMT0HwT6f6mzSk3NeiIe4+sY1A0AUIkwf9CXN/OSWgi4KsrKrPqT7RGQQQQAABBBBAAAEEEEDAFmhta5dFJvjX3NpuH0rqVVcM1mG/m8JWDh7e9/PMv35lyS/4oR0aP7iXlPcoTqpvXIRAVxQgANgVnzr3nFUBnfRW/xJGQQABBBBAAAEEEEAAAQRySaDdjPddZIb91jf7m7qosqYxYvBvRN9Sufyr06Svz+DfqP6lMtDHoiG5ZE5fEMiUAAHATEnTDgIugWWVdeZ/qq2uI2wigAACCCCAAAIIIIAAAtkT0KmKlpgFP3TUkp+iwT8d9lsZlvQwsl+pXPbVqb6Df4PKu8vIfmV+usi1CHRJAQKAXfKxc9PZFmgzf1mrMPMB6isFAQQQQAABBBBAAAEEEMi2wLLNdbKlrtlXNzZVfx7821zrrWeUFfzzn/nXu7RIxg/q6auPXIxAVxUgANhVnzz3nXWBBpNWv7SyNuv9oAMIIIAAAggggAACCCDQtQVWb6mXTWELdSQqsjFa8K9/mcn8myZ9Sv3N11daUiiTzaIfBQVm9Q8KAggkLEAAMGEyLkAgdQJV5i9j67c3pK5CakIAAQQQQAABBBBAAAEEEhDYYOYoX7PV3+8kdvCvKiyDcAcr+DdVevsM/hUXFsiUoeVSVEgII4FHy6kIeAT47vFwsINA5gV0VeDqxpbMN0yLCCCAAAIIIIAAAggg0KUFdHHCFVV1vgy0Dp3zL3z48OgBmvlngn8+V+rtZhL+JpngX4/iQl/95GIEuroAAcCu/gng/rMuYObalSUba6S5tT3rfaEDCCCAAAIIIIAAAggg0DUEtte3yFKz6If+PpJs0USGa59b2CH4N8YE/y79ylQp9xn8036NG9TLdxAx2fvjOgSCJEAAMEhPk3vJW4HmVl1xq8b8z9fH/33z9u7pOAIIIIAAAggggAACCGRSoLapVRabJAQ/axI2trTJDXMWybptjZ6ufx78m5aS4J+uHKyr/lIQQMC/AAFA/4bUgEBKBKobWmWVmXyXggACCCCAAAIIIIAAAgikS0ADd4vWV0ubj+hfa1u73PJihVnU0Dt8eETfUrnEZP716lHku/uDyktklJlDkIIAAqkRIACYGkdqQSAlAvrXsyozhwYFAQQQQAABBBBAAAEEEEi1gE47tMAE/1rakh951G5GLf1p7lL5aO12T/cG9CyRXx41JSWZf+UmgDhuYC9P/ewggIA/AQKA/vy4GoGUCyzbXCcNzW0pr5cKEUAAAQQQQAABBBBAoOsKaNbeog3V0tSS/NzjOmXRX/+7QuYvrfJA9upeJL80mX8DevkfrtujuJtMNot+dNPVPygIIJAyAQKAKaOkIgRSI9Bq/hpXYebj8JOSn5qeUAsCCCCAAAIIIIAAAggEQaDdDPfVOf/qmvwlGvzz/bXywoKNHpLuRd3koiOniA7/9VuKCwtkytDeUlxIqMKvJdcjEC7Ad1W4CPsI5IBAvckAXL65Ngd6QhcQQAABBBBAAAEEEEAgnwU0a2+JWe1X5xz3U15auFEeeXeNp4pCk6V3/oxJMmGw/+G6mvA3cUi5lJYUetpgBwEEUiNAADA1jtSCQMoFKmuaZcN274paKW+EChFAAAEEEEAAAQQQQCDQAsvNFENb6pp93eOby6rk7teWe+rQAbpnHTxedh7Z13M82Z2xg3pKn9LiZC/nOgQQ6ESAAGAnQLyNQDYFVlbVSU1jSza7QNsIIIAAAggggAACCCCQpwKrt9TLxmp/iwx+Yhb7+MN/PpPwZUNO3W+M7Dt+YEpkRvYrlcHlPVJSF5UggEBkAQKAkV04ikBOCJipOsx8gLVmla7kJ+rNiRuhEwgggAACCCCAAAIIIJBRAR1NtGZrg682l1XWys0vLpZW/cXEVY7bfYTMnD7UdST5TQ3+jepflnwFXIkAAnEJEACMi4mTEMieQHNru7UoiE7cS0EAAQQQQAABBBBAAAEEOhOoqm2SFWY0kZ+yfnuDXD9nkTSGrRp8+NTB8n+7j/RTtXPtqP4E/xwMNhBIswABwDQDU32wBDSF/vH31opOpJvJohP2LjV/faMggAACCCCAAAIIIIAAArEENPj3mVn0w8+vLDpn4LXPLpLqRu/CIV8a219O32+sFBToDID+yg4DymRkPzL//ClyNQLxCxTFfypnItC1Bd4wE9/+5IH3rAl0S8xS90dMS03Ke7yqm2ubpaSoTkYP6BnvJZyHAAIIIIAAAggggAACXUigsqbJShzwE/yrbWqV655bKJUmkOgu04f3lrMOmSDddLlen2W0Cf4N71vqsxYuRwCBRATIAExEi3O7rMBDb6+S79z1prN61l//u0J0MtxMl3XbGkVT8SkIIIAAAggggAACCCCAgFtgY3Wj7+BfU2ub3PT8YlkdNnfg2IE95YIZk6W40H8IYcxAgn/u58Y2ApkS8P/dm6me0g4CWRTo2b3IM/GtTsd328tLzIpajRnv1YrN9bI57K9xGe8EDSKAAAIIIIAAAggggEDOCGiSwLLKOl/Dflvb2+V35necxRtrPPc1tHcPuejIKVJaUug5nsyOBv+G9SHzLxk7rkHArwABQL+CXN8lBI7eebicc+gEz71qavxNLyyWhuY2z/FM7Cw1c3psb2jJRFO0gQACCCCAAAIIIIAAAjkssGZrvWiSgJ+ic5zf9epyeW/VNk81fcuK5ZKvTJE+pcWe48nsaBYhwb9k5LgGgdQIEABMjSO1dAGBnx0+SWZMG+K50zUmNf6Pr3wm7X4m2fDUGN+OZiBWmL/M1ZkgJAUBBBBAAAEEEEAAAQS6psCqqnpZvcX/FEH/eHu1zK2o9CD2NBl/vzxqqgwq7+E5nszOuEE9ZWgf//Uk0zbXIIDA5wIEAPkkIBCngE52e8uJu8oOZql6d3l35VZ55J017kMZ2W5tC8miDTXS2JL5DMSM3CCNIIAAAggggAACCCCAQFSBFZvrZO02/8G/Zz9eL099uM7TTnFhgfx85mTzu4//VXrHm+DfEDOMmIIAAtkVIACYXX9azzOBXmYuwIuPmiL66i5PfLBW5i/d7D6Uke3m1nZZbIKArW3tGWmPRhBAAAEEEEAAAQQQQCC7Ajpcd2llrVkc0P985K99tlnue2Ol54Z0kd+fHjZJpgzt7Tme6E6BqWf84J4ymOBfonScj0BaBAgApoWVSoMsoH+9+tnhE6VQ/4/mKn+Zu8xMvFvrOpKZzXozB6FmArbruGAKAggggAACCCCAAAIIBFZAg3+fmfnAN1U3+b7Hj9Zsk7+8srRDPT88YJzsMbpfh+OJHNBflXTY7+AUDB9OpF3ORQCB6AIEAKPb8A4CUQWmDe8jp+432vN+s8nCu/nFCtlW3+w5nomdmsZWWWL+IaD/IKAggAACCCCAAAIIIIBA8AT0D/4VG2tlc63/3zc0g3CW+d2lLez3h2/tNUoOnjzYF56V+TeoF8E/X4pcjEDqBQgApt6UGruIwIxpQ+Xwqd7/OW6pa7b+R9qShSG52vYKMwkwBQEEEEAAAQQQQAABBIIloMG/xWYRQP03v9+y3swbeP2cRdJkphNylyN3HCrH7DLcfSjhbQ3+TRjcyywc0j3ha7kAAQTSK0AAML2+1B5wgVP3HSNTh5V77lIz8Wa/tjwr2XgbzDwgqZgI2HND7CCAAAIIIIAAAggggEDWBNpM8G/hhmoz0qjFdx80gHjNcwtFRxC5y37jB8gp+4yWgrBpjtzndLZtB/8G9iL415kV7yOQDQECgNlQp83ACBQVdpPzzAS5g8L+Jze3olKe+2RDVu5zlckC3FTjf0LgrHSeRhFAAAEEEEAAAQQQQMAR0MX+Fq6vluoGb8DOOSGBjbqmVivzL3wI8Y4j+siPDxov3XwG/8abYb8E/xJ4IJyKQIYFCABmGJzmgifQu7RYLjhiknQv8n473f/mStGJdbNRllXWZWUuwmzcK20igAACCCCAAAIIIBBEAZ1WaIEJ/oVn6yVzr81muO9NLyyWVVu8UwaNHdhTzj98kmhig5+i9TDs148g1yKQfgF/3+Xp7x8tIJAXAqMH9JSzDp7g6avOp/u7l5eIzrGR6aJt6wTBteavfBQEEEAAAQQQQAABBBDILwGd82/R+hqpa2rz3XGt6w//WSKLNtR46hrau4dcdOQUKS0p9BxPdGdU/1IZYuqiIIBAbgsQAMzt50Pv8khgr7H95YQ9Rnp6XNfcZv2lrb4584E4nStksZkrpLHF/z8aPDfFDgIIIIAAAggggAACCKRVYKXJ1EvFH/NDJjPg7teXy9srtnr629eMYvrlUVOkj3n1U4b37SEj+5X5qYJrEUAgQwIEADMETTNdQ+DY3UbIl0wg0F3WmYU5NBNQ//KW6dLcav5yaP7Sp3OHUBBAAAEEEEAAAQQQQCD3BXShDl3cLxXlsffWyMuLNnmqKi0ulItM8G+wz6y9wb27i46EoiCAQH4IEADMj+dEL/NEQFfN+pGZQHf0AO9fwT5cs10efHtVVu6iwWQhflZZm5VVibNywzSKAAIIIIAAAggggECeCjS1tsky82/3VJQXF2yQx95b66mqqFuB/NzMXz7GZ+BuQK8SGWfm/aMggED+CBAAzJFnVV1dLf/4xz/kggsukIMOOkgmTJggffr0kZKSEhk8eLAcfPDBcsMNN0hVVVXKevzcc8/JscceKyNHjpTu3btbr7qvxynJC/Qwf1H7+RGTpXePIk8lT3+0XuaZ1YGzUbbWtXSY8Dcb/aBNBBBAAAEEEEAAAQQQiCygw3WXmHm8W9r8jxx6c1mV3PP6Ck9DBWbv7EMnyLThfTzHE93pW1YsE8yKv5r8QEEAgfwRKDA/ZPz/dMmf+83Znr700ksyY8aMTvs3cOBAuf/++2XmzJmdnhvthPb2djnjjDNk9uzZ0U6RH/zgB3L77bdLt27piRGvWbNGRo0aZbW/evVqK/gYtTM59sa7K7eIDq3trCwy8+9d/cxC0bn47KJ/cfvV0dNk4pBy+1BGXycM7sXqXBkVpzEEEEAAAQQQQAABBOITWG3m/Vuz1f8Cgp+u2y7XPbdIWl2/h2gPvvflsTJj2pD4OhPlrHKT5DB1WG8pNL/XUPJHIJ9//84f5dzvaXqiO7l/3znZQw2Iffe735XbbrtNHn/8cZk/f768/vrr8tBDD8kJJ5wghYWFsnnzZjnmmGPkww8/TPoeLr30Uif4t9tuu8mDDz4ob731lvWq+1ruuusuueyyy5JugwtFpgztLd83/5N1F/2f8KyXKmRrfbP7cMa2dThBTWNLxtqjIQQQQAABBBBAAAEEEOhcYHtDi6zd5j/4t3xzndz8QkWH4N/xu4/0Hfzr2b3Q/I5TTvCv88fJGQjkpAAZgDnyWNra2qwAX6zuPPHEE9aQXT1Hh+pqkDDRUlFRIdOnT5fW1lbZc889Zd68eVJaWupUU19fbw1Bfuedd6SoqEgWLlxoDUd2TkjRRj7/BSLeDECb6q//XSFzPt1g71qvE00m3uUmE7C4MPMx+JKibrLjiN7SvajQ0yd2EEAAAQQQQAABBBBAIPMCLWbBvo/WbItrlFGs3i02i//d8PwiqTdzgLvL4VMHW9l/fobs9ijuJtPN0GH9XYKSfwL5/Pt3/mnnbo/57s2RZ6PZfZ2Vb3zjGzJ58mTrtFdffbWz0yO+f+utt1rBP33z97//vSf4p8fKysqs47qtQcJbbrlFNyk+BL6zz2jZcXhvTw1LNtVac3JkYwR+c2u7NbdINlYl9iCwgwACCCCAAAIIIIAAArLUjNKJZ4qhWFQaQLz2uYUdgn97j+kvp+831td8fRr002G/BP9iPQHeQyD3BQgA5v4z8vSwvPzzueMaGxNfFl6DTU8++aRV35QpU2Sfffbx1G3v6HE70KjnZyNIZfclCK86P8a5h02UQb26e27nP4s3yUsLN3mOZWqnprFVlm1Ozepimeoz7SCAAAIIIIAAAgggEDSBdWbYry7Y56e8tXyL3Pj8Ymkyf+h3l+kmCeGsQyaYed2Tn6+vuLDABP/KRRc6pCCAQH4LEADMo+e3ePFi+eCDD6weawAv0bJ8+XJZt26ddZmuNByr2O+vXbtWVqxYEetU3otDoLxHsVxwxCQpCRvy+9f5K0QXC8lGqaxpTsk8I9noO20igAACCCCAAAIIIJDvArVNrbLKLPzhp8ytqJRbX+4459/uO/SVX8yc4itrTxMZppjMv7KSIj9d5FoEEMgRAQKAOfIgonVD5+RbsmSJzJo1y5qbT4flajnvvPOiXRL1+IIFC5z3Ogsgut/XeQAp/gVGD+gpPzponKciXSH41peWSFVtk+d4pnZ0pbGtddlZkCRT90g7CCCAAAIIIIAAAgjkmkCrmfdvycYaM9oq+Z7N+WSD/GXu0g517Dt+gPxshkk+8DFfnyYNTjYLfvTqTvAv+SfElQjklgDfzbn1PKze3HvvvXL66adH7dnFF18sJ598ctT3o72hE3/aZeTIkfZmxFddkdguq1evtjfjfnW3Femi9evXRzoc+GP7jh8oK6rq5akPP8/E1BvWFb9uMSsD/+ro6b7+J50Mnv6D4zMz58iOxX2ktIS0/mQMuQYBBBBAAAEEEEAAgUQFdLXexhbvkN1469Apmv75/lp55N0vfr+zrz1syucLfvgZ9ltggn8Th5RLn9Jiu1peEUAgAAIEAPPoIe66665yxx13yF577ZVUr2tqapzrevXq5WxH2ujZs6dzuLY28bni3AFEpyI2LIET9xxlgoB1ZqWv7Y7I0so6ufv15XLmgeN8TdDrVJjARmtbyBqGvNOIPlIUNkQ5gWo4FQEEEEAAAQQQQAABBOIQ2FTdKJtrkxuFo8G/v7+1Sp7+qGNCxdd2HiYn7b2D798nxg/qJf17lsRxJ5yCAAL5JMAQ4Bx8Wrra78cff2x9vfXWW/Lggw/Ksccea83/d9JJJ8nTTz+dVK/dC4eUlMT+gd69e3enjYaGBmebDf8C+te4cw6dKEN6f2Gster8HS8s2Oi/gSRq0L8+VmysZcGXJOy4BAEEEEAAAQQQQACBeAXqm1utEUHxnu8+r91MHzT7teURg3+aZJCK4N+YgWUyqNz7e4q7D2wjgED+CpABmIPPrm/fvqJfdtGMv29961ty3333yamnnipf//rXZfbs2XLaaafZp8T12qNHD+e85ubYf3FqavpiTrrS0lLnung3Ohs2rEOA995773irC9x5OpfGBTMmy+VPfuJZretv81fIqH6lMm14n4zfsw5FXmmGJ48Z+EX2Z8Y7QYMIIIAAAggggAACCARUQAN4S8wf3XUe8ERLa3u7/OmVpTJ/aVWHS0/bb4zMnD60w/FEDwzv20OG9Un8d79E2+F8BBDIjgAZgNlxT6rVU045RU444QRpNz/8zz77bNmyZUtC9ZSXlzvndzast66uzjm3s+HCzomuDZ1jMNbXsGHDXGd3zc1R/cvkJwdP8Ny8/lvg1peXmCEBXwRgPSekeWf99kbRIQkUBBBAAAEEEEAAAQQQSK2ATgNU39yWcKXNre0y64WKDsE/navvxweNT0nwT7P+dNFCCgIIBFeAAGCePVvN/tOiAbo5c+Yk1Hv3wh+dLdLhzuBjPr+EmBM6ee+x/eXY3UZ4rqlpbJVZL1aI/o8+G0UnJK5ubMlG07SJAAIIIIAAAggggEAgBarMH/g3Vif+R34dMnzdnIXy/uptHpciM63Qzw6bJAdOGuQ5nsxO37JiGT+I4F8ydlyDQD4JEADMp6dl+jpo0Bc/4FeuXJlQ76dNm+acv2jRImc70ob7/alTp0Y6hWMpEvi/PUbK7jt8MeRbq9Ug3J2vLsvKnHyahbhkY40Zmpz4XydTREI1CCCAAAIIIIAAAggERqCxpU2WmX/fJ1pqzB/lr3l2oSxc/8VijlpH96JucuHMybKXSSbwW8p7FMkks+JvgaYTUhBAINACBADz7PGuXbvW6XGiQ3PHjh0rw4cPt66fO3euU0+kjXnz5lmHR4wYIWPGjIl0CsdSJNDN/M/2rEMmmPk2vpijUat+7bPN8twnG1LUSmLVNLeGZPGGmqTmJ0msJc5GAAEEEEAAAQQQQCC4Arpq72ebaqW1LbF5/7bWN8tVTy+QpZXewGFZSaFc8pWpsvNIbwJBMoKlpq7JQ8ul0GQTUhBAIPgCBADz7Bk/8sgjTo932mknZzueDf2rjj2EWDP83njjjYiX6XE7A1DP569BEZlSerCs5PNFQUqLCz313v/mSvl47XbPsUzt1DW1mX9w1GaqOdpBAAEEEEAAAQQQQCBQAhr80wCeTvGTSNlS1yxX/utTWbO1wXNZb5Otd/nR06yMPc8bSeyUmCzCKSb4V1xISCAJPi5BIC8F+G7Pkcd27733SmNj7MUXbrnlFnn22WetHms23wEHHODp/SuvvGIF6zRgF22F4PPOO08KCz8PMp1zzjnS0OD9n4ru63EtRUVFoudTMiMwwqz++5NDxnsaM/9mkN+ZRUGytTBHVW2zWRnY+1dHTwfZQQABBBBAAAEEEEAAgQ4CuuLvYjOtTmVNYvP+6QrB+u//8PkC+/cskV9/bbqMScFCHUWFBTJ1WLn0CEs+6HATHEAAgUAJEADMkcd5xRVXiA63PeOMM+Rvf/ubvP766/Lhhx/Ka6+9Jn/+859l//33l/PPP9/qbUlJidxxxx1OIC+RW5g0aZJceOGF1iXvvPOOfPnLX5aHHnpIdFtfdV+3teh5EydOtLb5T2YE9hzdX3ROQHepbWqVm82iIDp3SDbKum2N5h8gsYPT2egXbSKAAAIIIIAAAgggkIsCLW3tsmB9tWytS3xhvUffXW0FDt33NbR3D7nCBP+G9y11H05qW0f76px/OgKJggACXUuA7/ocet5btmyRO++80/qK1i1dyffuu++Www8/PNopnR7/7W9/K5s2bbLqef/99+Vb3/pWh2u+//3vy9VXX93hOAfSL6CrAq8wkwS/s3Kr09iqLfVyx7xlcs6hE7IyJFsXJdHJhvuWlTh9YgMBBBBAAAEEEEAAAQS8ArqQ3iKzaEd9c+J/vP9ozTZ58oN1ngoH9epuMv+mpeTf4WagmEw0wb8+pcWeNthBAIGuIUAGYI485+eff15uvvlmOe6442TnnXeWIUOGWENwy8vLZfz48XL88cfLPffcI4sXL5YZM2b46nW3bt1k9uzZ8swzz1hzAurCIJpVqK86558OM77rrrtEz6NkXkAXBfnJwRNkRNhf+OYvq5LH31+b+Q6ZFnUo8hIzeXF9c2Lzl2SlszSKAAIIIIAAAggggEAWBPTfyp+srU4q+KeLfvzxP5+Je6kQXZzj3MMmpiT4pxxjB/YUHUpMQQCBrilQYCYmdf+M6ZoK3HXGBdasWSOjRo2y2l29erVoZmO+lHdXbhFdJTfdZf22BrnsyU86/APihweMk0OnDE538xHr717cTXYc3kd00mAKAggggAACCCCAAAIIfC5Q3dgiizfUJLzar16t8wX+9tmF1rBht+cp+4yWr+w0zH0o6e2RZr7xUf3Lkr6eC/NbIJ9//85v+dzqPb/F59bzoDcIOALDTAbg2YeYIb/Okc83Zr+2TN5zDQ8Oezutu00t7dY/bPQfKRQEEEAAAQQQQAABBBAQ0VV7F66rTir4p346ykfnDHSX3XfoK0ftONR9KOntIb27E/xLWo8LEQiOAAHA4DxL7iSAArvt0E9O3W+M587+n73zAI+juvb4sbTqvVqSJRd1ycYUAyGAbbCNC72EkrwHD0IChBpKMCUJhEcIppcECAmBUFIeLQHjho0xJTbNBGyrS5asLlm2el2Jd844Mju7krUzuzuzq/2f7xOamb1tfmO0d86953/E9/Y4ZwYr56xiZpgkJalo7Taja/QJAiAAAiAAAiAAAiAAAl5FoIWT5ZXxvFzv+viuhg56Y3ud6p4SOEz3qoVZbtH+TogMVkJ/VR3gBARAwC8JwAHol48dN+1LBJbNTqEzD09TDXmQM4s9sL6UGjhM2Axr6x6kmrYeM7pGnyAAAiAAAiAAAiAAAiDgFQTq9vdSZWuPopetZ0AdfUP0WzvdP8nSe92iHIoKdT1RR3SYhbKTIt3iSNRzf6gDAiDgXQTgAPSu54HRgMCYBC46JoPm5ySqPpOdeL9ZW0wiGGyGNbT3UzOveMJAAARAAARAAARAAARAwJ8IiIz+7r09VLtP/2L8CLfxFDv/2nuHVOguODqD8lKiVNf0nESEBFIeZ/wNEI8iDARAAASYAByA+GcAAhoJhFgCNdZwvfgUzgx8xYJMOjw9RtXYXt6Jt2ptiWnZeWXi026SA1IFAicgAAIgAAIgAAIgAAIgYAAB0cIub+mmpg7XFsLf+qqBvq7vUI14Ls/1z7CL/FEVcPIkPDiQ8lOiyRKI130nkaEYCPgFAfxF8IvHjJt0J4FcXkkzIwuuJSCAfroklzITI1S3U7Ovlx55t4yGOCzYaJMc4jIB6h20Gt01+gMBEAABEAABEAABEAABQwlYeb5d3NRJIofjipVwG69+XqtqIjY8iK4+KZsCeOHfFYsKtdDstGhT3ldcGTfqggAIeJ4AHICeZ4weJhkBcf7l87b8QBO204cGBdKty/MpJTpURXUXZx17eksliw8bn53XOvwNlTR10aDVeAekCgJOQAAEQAAEQAAEQAAEQMBDBGSuK5l6O/tcW/ju6h+iJ9+rUCUNEZ+f6P7FhLmm+ydOxIJU7Pzz0D8BNAsCPk8ADkCff4S4ATMIRIRYKCtJvRPPqHHIxOC2FfkUbTdB2FrZRq9sqzFqGKp+BoZGDmQ/05v+TNUaTkAABEAABEAABEAABEDAewj0Dw3TTs7W2zMw7NKgRDvw6fcraV+PegfheUelUyE77lyxRM72K5p/ZmxScGXcqAsCIGAcATgAjWONniYZgYTIEMqIDzPlrqbyDsCVy/IoNEj9v/CanU20+usGU8bU1W+litZuU/pGpyAAAiAAAiAAAiAAAiDgCQID1mFl558seLtq7+xopC9r21XNSLjuOUdMU13TejI1OoSykyOR8EMrOJQHAT8joPYe+NnN43ZBwFUC6XHhlBQV7GozuupnJkXSjawJGGinE/LKJ3voo4q9utp0tZLoodS09bjaDOqDAAiAAAiAAAiAAAiAgOkEJOy3uLGL3OH8q2jpor99qtb9k4iea09m3T8XpIXS48JI3gskaSAMBEAABA5FAA7AQ9HBZyDgBIHMxEgSsV0zbG56LF25MNOh62dYD/DrOvXqokMhD11oaO+nlk7XsqJ5aGhoFgRAAARAAARAAARAAAScIiAJPyRZR9+ga2G/0ln3gJUe31ROwzZ63eKuE+dfbLj+zQQzEsI5IincqftBIRAAARCAAxD/BkDARQKyYieZgUPswnFdbNbp6vNzkuj7x05XlR9mLb5HN5bR7r3m7Mar4n7322mbqAaIExAAARAAARAAARAAARDwUgIyl5Ykd65q/sntie7fsx9U0l67zMFncdjvYdNidBGQzX5ZyRGUFmuOHJGuQaMSCICA6QTgADT9EWAAk4GAmZmBhd8Zc1Np+ZwUFcp+1ilZta6Emk3YjSeLm2XNXdTJWc5gIAACIAACIAACIAACIOArBEYU518nib61O2xDUTN9Vr1f1VR+ShR9b1666pqzJxItLJsPkqNCna2CciAAAiCgEIADEP8QQMBNBMKDLZTD4rtmyG+I5sfFx82g4zLjVXfT0TdE968toU7+bbRJQuBSZeXUPZMno8eP/kAABEAABEAABEAABPyLgOzWK2Otvs4+98xfJRrn5W01KoiRIRYl9FdPtl6pk8/ZguMj9IcNqwaDExAAAb8iAAegXz1u3KynCcTxl/F0k3Q4AtgJePVJ2VTIkwJba+IdgA+sL6H+Idf1S2zbdebYOizhE52m9O3M+FAGBEAABEAABEAABEAABISAOP8qWrpZxsY9C+f7ewdZ96+MrLIqbmNXn5RFCZEhNlecOwwKnEKFnDE4hhOHwEAABEBADwE4APVQQx0QOAQB0eJIitL+pX6IJp3+KCgwgG5emuvghKxs7aHfba4gCWkw2gat31BRYydJFjUYCIAACIAACIAACIAACHgjAZkv2+v06R1nU0c/3f3WLpbiGVA1cTrL9hw5PU51zZkT0RqfnRZDsnsQBgIgAAJ6CcABqJcc6oHAIQhkJkZQdJg5X9ASirxyeT4lRqpDAz6v2U+vfKIOQTjELbj1owHWIyxmJ6BkU4OBAAiAAAiAAAiAAAiAgDcRqOZQ3dYutbNO7/gk7Peut3dRi117IhV04TEZmpsNCw5k5180yW8YCIAACLhCAA5AV+ihLgiMQ2A0M3CoSZmBRRfktuUFFBGiniis2dlEG3Y1jTNqz17uHRxWsqlJVjUYCIAACIAACIAACIAACHgDgdp9vdTIO/bcYbsaOuh/Vxc56G9PjQ6hGxbnkCVA2+u37PgT51+IRT2nd8dY0QYIgID/EdD2F8j/+OCOQUA3AQnHzeMMXxbW6zDDpsWF0U1LcsleYPiFrdX05R51JjKjxifZ1CQ7sGiswEAABEAABEAABEAABEDATAL17X1Ut7/PLUP4ZHebknyvz053e2ZCON19xmzNun/BlgBO+BFF8k4BAwEQAAF3EMBfE3dQRBsgMA4BMzMDy5AKWSvkivmZqtGJ7+2J98qppq1Hdd2ok/beIUVg2aj+0A8IgAAIgAAIgAAIgAAI2BMQnb49bb32l3Wdbyxupsc3ljsk/JDkfL84vZBiw9XSPBN1wrn9KJtDhuH8m4gUPgcBENBCAA5ALbRQFgR0EJAv/Bm88meWLchNonOPnKbqvp81+R5YX0r7egZV1406EYFl0UeBgQAIgAAIgAAIgAAIgIDRBFq6+t0yF5Wolje219FzH+0m+/iWY2fFK7rcsiFAq6VzJA+y/WqlhvIgAAITEYADcCJC+BwE3EAgNSaMRPvDLPvevHQ6IStB1b04/x5cX0L9dmEKqkIePJFVV9FcgYEACIAACIAACIAACICAUQTaugeoijP+umoj7Px74V/V9OoXdQ5NLc5PphsW5ZCE8Wq12PAgSo8zb/OA1vGiPAiAgO8Q0P4XyXfuDSMFAa8iMIszA5u1kjeF4wiuWJBFeVOjVEyqOezhSQ4HHjEpMYdorogjEAYCIAACIAACIAACIAACnibQ3juoSNG4KkdtHR6h326uoA1FzQ5DPveoaXT5ibNIkgJqNXEYSugvDARAAAQ8QQAOQE9QRZsgMAYBccLlTI0kszIDy4TipqW5lBIdqhrd9j3t9NK2GtU1I08kFLi1a8DILtEXCIAACIAACIAACICAnxHo6h/iZHTd5Oq6d9/gsCKls7WyTUVQ3H2XHj+Tzp+XQTLv12pSRd4VoPunlRzKgwAIOEsADkBnSaEcCLiBgHyh56dEm5YZODo0iG5dnkeRIWotknW7mmjdzkY33KG+Jipbu2m/SXqE+kaMWiAAAiAAAiAAAiAAAr5CQJx2pU1dNOyi96+zb4h+vaaIdtR3qG49kHf7Xbcom5bNTlFd13KSER9OMleHgQAIgICnCMAB6CmyaBcExiEQFhxIuclRvDI4TgEPXxY9wptPySWLXVjCi7wL8Iua/R7ufezmJQyjrLmLOnllFgYCIAACIAACIAACIAAC7iIwaB2h4qZOGhq2T9OhrQeJWLn77V1UaacfGMJRNrcuy6PvZiVqa9CmdFxEEE2LDbO5gkMQAAEQcD8BOADdzxQtgsCEBGJY3HdmQsSE5TxVID81mq5cmKVqXpxwogdoVnZeWZCVldmeAatqXDgBARAAARAAARAAARAAAT0EZMefzC8Hhkb0VD9YRxLX3fXWTmq0066OCrXQL04vpLnpsQfLaj0QmZ6sJOj+aeWG8iAAAtoJwAGonRlqgIBbCKTEhJqaGfjE7ESS7MC2NsArpA9wZmDJjmaGWXlltoRXaCVMAwYCIAACIAACIAACIAACegl8w6vbEmHS7eLisrTxq9W7aH+vOlIlMTKY7j5jtkvOO+j+6X26qAcCIKCHAByAeqihDgi4iYCZmYHlFs49chrNZ0egrbXz5OaB9aWmOeEGrd9QUWMn9Q/BCWj7XHAMAiAAAiAAAiAAAiDgPAEJ1ZV5rSsmzr/71hRzhIp6XpoeF0a/OnMOpbkYtjsdun+uPB7UBQEQ0EgADkCNwFAcBNxJQDKE5XK2L9EFNMOk/x8vyKSC1ChV93s4zOEJDgd2VShZ1aiGE9FqgRNQAzAUBQEQAAEQAAEQAAEQOEhAQnZFs88VE1mcVetKSCJkbE3m7nedPpviI4JtL2s+Ft0/Vx2ImjtFBRAAAb8mAAegXz9+3Lw3ELBwZuC8qVGmZQaWzMQ3LcmjVA5JtrV/17bTC/+qJgmfMMNEq6WYdwIOWNUrrmaMBX2CAAiAAAiAAAiAAAj4BoHmzn6q29/n0mDFgSg7/3rtZGmOzIilO04toEjW/nPFQoICKBu6f64gRF0QAAEdBOAA1AENVUDA3QTMzgwsk5iVy/NJhIxtbWNxM63Z0WR7ydDjfsUJ2EWyIxAGAiAAAiAAAiAAAiAAAocisK9n0OWEdo0dffRrdv7Zawcewc6/m07JpRCLa5E7iu5fciQv/uNV/FDPEp+BAAi4nwD+6rifKVoEAV0EJDOwaAKaZVOjQ+nmU/IoKHCKagivfFJDn1XvU10z8kQSgkg4MJyARlJHXyAAAiAAAiAAAiDgWwS6+oeonDX7XAlekbDhX79TTB19au3A2WnRdOOSXLc47WYkhPOie5BvwcVoQQAEJgUBOAAnxWPETUwWAuKEk+zAZlleShRdtTBL1b0EAP9ucwVVtnarrht5Ik5ACQceGsZOQCO5oy8QAAEQAAEQAAEQ8AUCMlcsbeqiEReUa2T34L3vFFEb/7Y10fy7ZWkeBVtcf3UW3cDUmDDb5nEMAiAAAoYRcP2vmGFDRUcg4B8EZvKqYEyYeauCx2cl0gVHZ6hgi/jxQ5wZeG+3a2LKqkY1nogGizgBrXACaiSH4iAAAiAAAiAAAiAweQlIlEhxkywU6/f+yY6/X68poha7xCESnSMyOaFBroX9Cn3R/ctKMi/aZ/L+C8CdgQAIOEsADkBnSaEcCBhEwOzMwHKbZx+RRgtzk1R33M4TowfYCdg7aFVdN/KkZ2CYSnh1F05AI6mjLxAAARAAARAAARDwTgLDvOVPdv5J8ji91t1vpd+w5l9De7+qiYy4MLp9RT6FB6s1slWFnDwJYIWdXCXpH16/nUSGYiAAAh4ggL9AHoCKJkHAVQIiCpzP4bj2enyututsfXFC/ujEWVSYGq2qIhnRnthUTjLZMsu6eJImTkAzx2DWvaNfEAABEAABEAABEACBAwS+YbG/Mtb8s0/WoYWPLGzfv66YaniOa2upLMkj2X7dpdU3nSN8IkNcdyTajhHHIAACIKCVAByAWomhPAgYREBCDXJ4pVAyhZlh4oS8kTOdpdlpEn5V10Ev/KuaBZbNdgJ2wgloxj8M9AkCIAACIAACIAACXkCgsrWH2nvVyTq0DKt/aJge5OgWacfWkqNC6E52/sWGB9te1n2cFAXdP93wUBEEQMCtBOAAdCtONAYC7iUgWoBmZgaWlcpbWfckKlS9YrmxuJnW7mxy781qbK2zz3pA7NnE3Ygah4ziIAACIAACIAACIAACbiAgUSmSsVeviW7gI++WKVEltm1Ikg5x/iVEhthe1n08NTqEdf8idddHRRAAARBwJwE4AN1JE22BgAcISGZgCUMwy6R/yXxmH4788rYa+rx6n1nDUvoVweZSDv0YgRPQ1OeAzkEABEAABEAABEDAKALNnf1Ut79Pd3fWkRF6nCVtdtR3qNqI5oV3cf4l89zXHTYtNowy2fkn0jowEAABEPAGAnAAesNTwBhAYAICM1g3JCHSPWEIE3Q15sciWvyThVmqzyQA+LebK6iqtVt13egTCf0ob+k2NSTZ6HtGfyAAAiAAAiAAAiDgjwTaewdp9151yK4WDrJo/Duev27fs19VTaJe7uCEH2nstHOHydxddP9gIAACIOBNBOAA9KangbGAwDgEZOUwm1cQo8PUobjjFPfI5e9mJdKFR2eo2h7g8IkHN5RSW7f+EAxVgzpP9vUMwgmokx2qgQAIgAAIgAAIgIAvEJCEHQcWffWNdoT1q3//QSVtq1JHsISx7vZt7PybkRChr2GbWrLZLys5wm2ORJumcQgCIAACLhOAA9BlhGgABIwhEBAwhfJ4J154cKAxHY7Ry1lHpNHC3CTVJ7IDbxULKMukzExr6x5kEWdzdyOaef/oGwRAAARAAARAAAQmK4Gh4RFF+9k6rC8JnSSve/7javqgfK8KUYglgFay3rU7dPp4qk4SNZMc5Z4QYtVAcQICIAACbiAAB6AbIKIJEDCKgGTmzU+NopAgc/7XlZ2IPzpxFhWmRqtuWYSYn2AtlWGTtfhauwappk1/WIjqpnACAiAAAiAAAiAAAiBgOgEJ2y1t6qL+oRHdY/nLp3tIktjZmuhbi851XkqU7WVdxxZuK5/nx5JEBAYCIAAC3krAHC+Ct9LAuEDABwiEWAKpICXaISmHUUMXJ+SNp+RyaIN6dfOrug564V/VpmvxNbT3U0O7fmFooziiHxAAARAAARAAARAAgYkJVLHmX1e//kiT1V830OqvG1UdBfJ2vZ8uyaU502JU1/WcBFumKIvjMZxEBAYCIAAC3kwADkBvfjoYGwiMQyCMw4BzebVSQg3MMBFKvnVZPkWFqjUJZWV17c4mM4ak6rOmrZf2mqxLqBoQTkAABEAABEAABEAABDQTqOdF3dYu/VrTH5S10iuf7FH1Kzp9152cTUdNj1Nd13MiUTmz02IogufGMBAAARDwdgJwAHr7E8L4QGAcAtGhQZTDOiMyiTHDpkaHKmETEj5hay9vq6HPq9XiyrafG3VcyZmBO1ifEAYCIAACIAACIAACIOB7BCTJ2x5e1NVrkulXkn7Y25ULsug7mQn2lzWfiy737LRoCuUkIjAQAAEQ8AUCcAD6wlPCGEFgHAKiM5KZ6HrGsnGan/CyCB3/ZGGWqpxIM/92cwVVmZyQQ+QIS5u7qGdAf8iI6sZwAgIgAAIgAAIgAAIgYAgBmb9V8GKuXivjOeDjG8vJXp76B8dOd0hop6cPiYIpZOefSPPAQAAEQMBXCMAB6CtPCuMEgXEIJPNOvPS4sHE+9fzl72Yl0oVHZ6g6GrCO0IMbSqnN5DBcSUpS0tTJotHDqvHhBARAAARAAARAAARAwDsJDFiHef7WpTu5nCSne2B9CQ1y5mBbO31uKp1xeJrtJV3HseFBVMAJP4JYFxsGAiAAAr5EAH+1fOlpYawgMA6BjPhwmhodMs6nnr981hFpDqup7Rx++5u1JdTRZ24Y7qBVnIBdNGQ3CfQ8FfQAAiAAAiAAAiAAAiCghYBk/C1r6qZBXkzWY6IBff+6Eo4AUS/+zs9JpO/z7j9XLTEymPI4AkaSiMBAAARAwNcIwAHoa08M4wWBcQjM4lBgCQk2w6awEOGPTpylZECz7V+Em3+9ppgzt5nrBOwbHKZSF1aSbe8JxyAAAiAAAiAAAiAAAp4hUMESMt065Vs6eb75m7XFJNqBtnZkRixdsSCTk+e55rSTxfbs5EgKgPPPFi+OQQAEfIgAHIA+9LAwVBA4FAFxwuXwpMQ+M++h6rjzMwuHQdx4Si6lxYaqmpUwjPvYCah3MqdqzIWTrn4rlbd00TffiEohDARAAARAAARAAARAwJsIyJyxrVvtvHN2fCL38uD6Umpo71dVkbnxDUtyyBLg2muvzG8zkyI5+Z5rTkTV4HACAiAAAgYTcO0vocGDRXcgAAKHJiArkvkpUSRZycywyBAL3bGigJKj1OHI1ZzB7X5eke0dNDchx/6eIara22MGGvQJAiAAAiAAAiAAAiAwDoHWrgGq2983zqeHvmxlmZdHN5Y5JA2ZFhtGty7LdzlRR0pMKM1IMC/p3qHvHp+CAAiAgPME4AB0nhVKgoBPEJCdePmpURRsMed/74TIEPr5aQUkGim2Vtnaw07AEpJwXDOtpXOAZIUZBgIgAAIgAAIgAAIgYD4BCd2t4tBfPTbCkR3PbKmkr+s6VNUTWBbn9hX5FMnZel2xZA77FZkdGAiAAAhMBgLmeAgmAzncAwh4MYEQSyBnJ4siS6A5YQpJUaHsBCx00CQsb+mmVSzMbHZWXllhbu5Uh4h48ePE0EAABEAABEAABEBgUhKQOWF5cxdx7g/NJrIuL2+roY8r21R1JSLldo5IkUVpVyyJI1oy4fxzBSHqggAIeBkBOAC97IFgOCDgLgLhwRbK43Bgs3SKp0azE/DUAooNC1LdUilP8kSjZcBq7k7A3RwKbC8SrRooTkAABEAABEAABEAABDxGYJi9fpKkbdCqw/vHo3rrqwZau7NJNb4QjoBZuTyPpsWFqa5rPZFIlqykCGj+aQWH8iAAAl5NAA5Ar348GBwIuEYgOjSIcqZG8eTFtXb01k5l7RXZCRhtF35R1NhJD28o4wnfiN6mXa4nuUBkxVnCTmAgAAIgAAIgAAIgAALGEZDde5KcrVenNMzm0hb622e1qgEH8oT3p0tyOVNvlOq61pN4Dh+WbL9I+KGVHMqDAAh4OwE4AL39CWF8IOAiAZnEmBm+ICuw4gSUcAxb21HfoQg2D7Fws1km4Say8mx2chKz7h/9ggAIgAAIgAAIgIAZBCRBnCRn02Of1+yjP3xY5VD1qpOy6IiMWIfrWi7ERfDiOZx/WpChLAiAgA8RgAPQhx4WhgoCegkkczhuuouhEHr7lnoZ8eF0JycGiQhRZyf+d207PbaxnCR7m1lmHf6Gihu7TA9JNuv+0S8IgAAIgAAIgAAIGEmgmmVYmjr0aTGXcBTJE5vKSSI5bO3i42bQidmJtpc0H8ewbE0u7x4MMEs/R/OIUQEEQAAEtBGAA1AbL5QGAZ8lIE64qZzJzCybmRBBd7Agc3iw2gm4fc9+evK9CrKOmOcElFBkcQKauRvRrOeCfkEABEAABEAABEDAKALvoU2bAABAAElEQVQ1bT3UqNP5V7uvlx7cUMrzNbX378zD0+jUw1JduoXosP9oZ8P55xJHVAYBEPBuAnAAevfzwehAwK0EZnEmswQWNTbLMpMiOStbPoUFqZ2An1bvo6c2V5KIQZtlfaxBU8JOQDN3I5p17+gXBEAABEAABEAABDxNYA+H/Ta069v5J4nbVq0rcdAMPCk3iS46JsOloUexVnV+SjQFwvnnEkdUBgEQ8H4CcAB6/zPCCEHAbQREzDibnXCyymmWiTDzyuX5JFnabG1rVRs9s6WSRkx0AnYPWKmENQHNdETaMsExCIAACIAACIAACEwGArJ7r769T9et9A8N00O886+NnYC2Nm9GHP1ofqZLyTpEozo/JQrOP1uwOAYBEJi0BNRv4JP2NnFjIAACowRE1ySPMwPbh+KOfm7E7zyeaN3KTsDgQPWfoI8q9tKzLOo8Yi/sYsSg/tNHV7+Vyjg7sJmOSANvF12BAAiAAAiAAAiAgEcJiPOvbr8+55/Mx0QqZjfrBtpa7tRIun5RjkuOO9Gmzk+NIovdfNS2HxyDAAiAwGQioH77nkx3hnsBARAYl4BMdGTCExJk3p+AwtRo+tmyPAoKnKIa55ayVvrTR7tNdQK29w5ReUs3C0ybF5KsgoITEAABEAABEAABEPBBAnX79Tv/5HZf3FZDohdta6JpffPSPAq2iyaxLTPRsSyEF/BcNAjOv4lQ4XMQAIFJRMC8t/9JBBG3AgK+SCDEwhMf1juxd8AZeS9zpsXQzafkkcVOc2VTSQv9+V/VpjrgRGumsrXbSBzoCwRAAARAAARAAAQmDQEJ+a3dp2/nn0BYu7OR1u9qUvGQkN2Vy/IpOjRIdV3LSSgvgMP5p4UYyoIACEwWAnAATpYnifsAAR0Ewnj1U8JxzRQ9Pjwjlm5ckuswhg1FzfQyr/qauQuvtWvQIeREB2ZUAQEQAAEQAAEQAAG/ItDAzj9J+qHXPucEcS9trVFVlwXjm0/JpdTYMNV1LScS/VKYFu3S7kEt/aEsCIAACHgTATgAvelpYCwgYAKBKF5BzUmOZAFlEzr/T5dHsYjzDYtZx8VuEGt2NtHfPqs11QnY1NHv0gTWPKroGQRAAARAAARAAASMJ9DY0Uc1Ljj/JALjt5sryF6I5aqFWSxhE637hiRkWCRoJAoGBgIgAAL+SAAOQH986rhnELAjEBcRTJlJEXZXjT09ZmY8Xbso28ER+dZXDfTa9jpjB2PXm4SwiIYNDARAAARAAARAAARAYHwCsnBavVf/nKm1a4AeXF9KA9YRVSfnz0unE7ITVde0nIjkjTj/QoPg/NPCDWVBAAQmFwE4ACfX88TdgIBuAslRoTQ9IVx3fXdUPC4zga45iZ2Ado29sb2e3jDZCSgaNjKphYEACIAACIAACIAACDgSkHmSfbZex1LjX+kdtNID60uoo29IVWhhbhKdc+Q01TUtJxZ2/snOQZG+gYEACICAPxOAA9Cfnz7uHQTsCExjTZWUmFC7q8aeyurulRziYe8EfPWLOpLdgGaaTGpbuuAENPMZoG8QAAEQAAEQAAHvI9Dc6ZrzzzoyQo9uLOeIC3XSkNms1/ejE2dxhIj9zNA5BpJnLndqFEnyEBgIgAAI+DsBOAD9/V8A7h8E7AjM5F2AiZHBdleNPZWV3svnz3Lo9K+f7qE1Oxodrht5oaq1h9q6B4zsEn2BAAiAAAiAAAiAgNcSaGHnn8yP9JokfPvTR7tpZ32HqglZmJZEcZZAfa+s4jMU519MmP6MwaoB4QQEQAAEfJyAvr+mPn7TGD4IgMD4BGSFNSsp0vTJ0uL8qXTZ8TMdBvoSZwbeUNTkcN2oCzxHpYqWbmrvHTSqS/QDAiAAAiAAAiAAAl5JQCIjKl1w/slN/ZMjPDaXtqruL5qddiuX51GECzv3ZD4rOtcwEAABEACBAwTgAMS/BBAAAQcCARwvkTs1kidd5mqlLJ2dQhcfN8NhfM9/XE3vlbQ4XDfqwgg7Acuau6mzX61RY1T/6AcEQAAEQAAEQAAEzCYgERGu7PyT8f+rci/9/bNa1a1Iwo6fLc2jJNan1muzEiO4foje6qgHAiAAApOSAByAk/Kx4qZAwHUCEm6RlxJFwRZz/0ycelgqff/Y6Q439McPq2hLmXq12KGQBy8MsxewtKmLugesHuwFTYMACIAACIAACICA9xGQ+Y9EREhkhF6TedQzWypV1UXp79qTcyg7OVJ1XctJRrz5mtZaxouyIAACIGAUAXPf7I26S/QDAiCgi0CIJZDy2QkYKArKJtqZh6fR+fPSVSOQ+ebvP6ikjyv2qq4beWId/oZKGjupb3DYyG7RFwiAAAiAAAiAAAiYRmDQOqIsgkpEhF6TjMEPbSilIZ5L2doPvjOdjp0Vb3tJ03FabCilx4VrqoPCIAACIOAvBOAA9JcnjfsEAZ0ERHslh1dhdSZf09mrY7Vzj0qnc46cpvpAVp2fer+CPqlqU1038kQmrkXsBBywwgloJHf0BQIgAAIgAAIgYDyBEfb6lTV3kTgB9VoXS6isWlfiEEWxpGAqncaRH3otOTqEZiRE6K2OeiAAAiAw6QnAAehFj/jzzz+ne+65h5YuXUrp6ekUEhJCkZGRlJubS5dddhl99NFHbhnt3Xffzc6cKU79vP/++27pE434NgERUJ7B2YHNNtkFeMZc9cRQVp+ffK+CPq/eZ9rwZBJc3NjFq9j6J8OmDR4dgwAIgAAIgAAIgICTBKr2dlNXv375E5kzPbyhjJo4c7CtHZERS5dy8jd5R9FjCZHBlMm6fzAQAAEQAIHxCVjG/wifGElgwYIF9OGHHzp0OTg4SOXl5crPCy+8QJdccgn94Q9/oOBgZLRygIULHiWQGhNG/UMjJCEbZplMCkUP0Mpev7U7v80EPMxbAR/bVE43n5JLR06PM2V4EgYsWjYFqdGmh0ybAgCdggAIgAAIgAAITGoC9e191No1qPseRT/5yffKqZR3ENqaLDJfvyhH9/wpNjyIsjnjr17noe1YcAwCIAACk5kAHIBe8nQbGhqUkaSlsdbZ+efT/Pnzafr06TQ8PExbt26lhx9+mOrr6+nFF1+koaEh+stf/uKWke/YseOQ7cyaNeuQn+ND/yIwkydoEuq6v8e87LcyuZPMwOIEfLeo+eADkEnloxvL6BbOGjc3PfbgdSMPZEVcwmJENxGTUCPJoy8QAAEQAAEQAAFPEtjXM0h72np1dzHCi7XPsnbz5zX7VW3Ec5TJrcvyKSw4UHXd2ZOoUAvlTo2iAJP1qp0dL8qBAAiAgJkE4AA0k75N3/n5+XTffffReeedR4GB6i/A4447ji6++GI64YQTqKysjP7617/SVVddRbJr0FWbM2eOq02gvh8REKdWTnIU7WrooJ4B8zTvZBwSJiJOv/dKWg4+AdHjE0HplcvzaXZazMHrRh609w5RZWs3Z6+LMrJb9AUCIAACIAACIAACHiHQO3gg46/exr9h599L22rog3J14rawoED62bI8EiegHosI8Y5kdXrGjjogAAIgYAYBaACaQX2MPlevXk0XXHCBg/NvtGhiYqKyC3D0/LXXXhs9xG8QMJSAZATO4x1uwRZz/3wEsBPw8hNn0YKcRNX9ixPwwfWlVNLUqbpu5ImEx1Tv7TGyS/QFAiAAAiAAAiAAAm4nIPrGJSxxIouueu2NL+tpnY10i7QTFDiFbmHn30ydSTtCgwI44iKaLIHmzkf1MkE9EAABEDCDAP5imkFdZ58nn3zywZqVlZUHj3EAAkYTCLF4x4qrOAGvXJBFJ2QlqBAMsMD0A+tKqaJFrTGjKuThk0bWSqzbrz9UxsPDQ/MgAAIgAAIgAAIgcEgCsnNPpE0GWANar4nj77Uv6lTVJVr3p4tzqZB1k/WYLEKL5rLZi9F6xo46IAACIGAmATgAzaSvse+BgYGDNezDhA9+gAMQMIhARIiFw4FFcNmgDsfpRjRffnJSNn1nVryqRN/QMP1mbQntNnEnXu2+Pmqxy3KnGiROQAAEQAAEQAAEQMBLCVTxHKqzT3/G3w/LW+nPW6tVdyfTRpm3HTVDX9I2C+8cLEiNolAOH4aBAAiAAAhoIwAHoDZeppbesmXLwf4LCgoOHrtysHTpUkpOTlayCsvvk046ie6//37av18t0OtKH6g7eQnEsWaLZG4z2yQs+dpF2TTPbjLZy5l571tTTDVt5oXjyuRZhLNhIAACIAACIAACIOArBBo7ZBHz280HWsf9ec0+emaLY8SSaDifmK2Wb3G2bdk5KDI04cGQsXeWGcqBAAiAgC0BOABtaXjx8cjIiOKYGx2i6AW6w959911qbW1VMgvLb3Ey3n777ZSZmUn//Oc/dXdRV1dHh/ppbGzU3TYqeheB1JgwSokJNX1QloAAumFxDh2erk7+0T1gVZyA9fv7TBkjR89QOYfPdPSZlznZlBtHpyAAAiAAAiAAAj5JoIMTmtW4kPG3iJPFPbGpnOxlA8+fl05LZ6foYiIRJ9kceRIdGqSrPiqBAAiAAAgQYfnER/4VPProo/Tpp58qoz333HNp3rx5Lo38sMMOo7PPPpuOPfZYSktLUxyApaWl9Morr9CGDRuovb1dyUj89ttv04oVKzT3lZGRobkOKvgugZm8C3DAOkz7e8x1cgWxEPRNp+RxEpAS2tnwbRKQzn4r3bumiH55eiGJw9JokwmwaOiI1o2ETsNAAARAAARAAARAwBsJ9HH0RBlrKMsCph6rbO2mBzeUkiRls7VTD0ulc46cZntJ07FEnCREhmiqg8IgAAIgAAJqAlNY3FX911n9Oc68gIDsyluyZAlZrVYlXHfHjh3Kb71DE+debGzsuNV///vf01VXXaV8Ls5BSTgSGqpth9cUDcJwtbW1lJ6ePu548IFvEJDscLt4xbdnYNj0Afez/t+qdSVK1jrbwcRzyPJd7ARMjtb279m2DVeOgy1TaHZaDHRrXIGIuiAAAiAAAiAAAh4hYOWMv7KAKk5APSbRFne/vYsk+sLWFuYmcdK2TNaNFgVA7ZYWG8qSMxHaK6IGCIDAQQISnTe6SQfv3wex+N0BQoC9/JHv2rWLzjnnHMX5J064V1991SXnn9zuoZx/8vmVV15Jl19+uRxSQ0MDvf7668qxlv/IH5VD/YzuZtTSJsp6NwHR4RNdFm/IyCbC0Lcuy1eSlNhSEy2+e98ppr3d+jVtbNvTejxo/YaKGztpkLMUw0AABEAABEAABEDAWwjInpDylm7dzr/Wrn66b22xg/Pv2Jnx9OP5+p1/iZGiNw3nn7f8O8E4QAAEfJsAHIBe/Px2795NkqRDEnJI1t+//e1vtGDBAkNGLE7AUbNNPjJ6baLfsqPvUD+pqakTNYHPfZBAiCWQ8tkJKM5Asy0sOJBWLs+nWYnqSWMrO//ufafItMQc/UMjvDOxk2SVHQYCIAACIAACIAAC3kBANP/aWftPj7X3DrLeconD3GpOWrSSpE3vvDA6zEJZSZF6hoQ6IAACIAACYxCAA3AMKN5wSXbeSdiv/Jbt8n/605/orLPOMmxohYWFB/uqr68/eIwDEJiIgGjc5bBIs84oj4ma1/S5jOWOFQU0PV6dqbiZs9r9mjUBZcJqhkmYdClrAo7Yq2ObMRj0CQIgAAIgAAIg4NcEWjr7qbGjXxeDHg73vX9tCTVxG7YmCTtuXppHos+sx8J5ITdvahQFeMGisp7xow4IgAAIeCMBfX+RvfFOJtGY9u7dS6eccgpVVVUpd/Xkk0/SJZdcYugd6tXoMHSQ6MxrCcSx1p63OAEjQy1056kFNC1WnfyjoZ1DVdYUU2e/vtVuV+F39lmVUBvIsLpKEvVBAARAAARAAAT0EpCMv7v39uiqLprLD3DitZp9var6GXFhtJKlWESSRY+JnEx+ahRZdDoP9fSJOiAAAiDgDwTgADzEU5YMuBdffLGSBffqq6+m7du3H6K0ez7q6OigZcuWUVFRkdLg/fffT9dcc417GtfQymj/UkUSgcBAQCsBydSmrNyaHw1M0WFB9PPTCjgDsDr5Ry2LVYsT0F6sWuu96i0vmoR6J916+0Q9EAABEAABEAABEBAC4vwTWRI9AQkiZfLYxjIqa+5WwUyOCqHbOPpCFmD1mCVwiiInI7IyMBAAARAAAfcS8FsH4ObNm5VkGtOnTyfJimtvv/jFL+jss8+mv/zlL7RhwwaSzLjHHXccvfTSS/ZF3Xbe29tLp5122kFH45133kkrV650W/taGpL7HbWFCxeOHuI3CGgiIDsB81OivUITMDY8mJ2AnAGYJ6a2Jpo397Node+gOmOdbRlPHks4cq3dyrkn+0PbIAACIAACIAACINDRN3RAjuQb7SxGOGHIU+9X0ld1HarKseFBdAdHXcTz/E+PiXxMbnIUiYQLDARAAARAwP0E/NYBuGbNGpJQ22OOOcYhK+7XX39N9913H0lonvxI1lz5bbValQy51dXVbn8Sg4ODSrbfjz/+WGn7hhtuoHvvvVdzPy+88IKiGSghvHfffbdD/R07dlBFRYXDddsLzz77LP3xj39ULqWkpCjjsv0cxyCghUAMTwYlO7BeAWgtfU1UViak4gSUjHK2VtnaQ6vWlejOfGfblp7jOt6J2GynnaOnHdQBARAAARAAARAAgYkIiPxJaVMXDevZ+seNv7KthrZWtam6iQgJVHSXp0aroy1UhSY4kYQfMm+EgQAIgAAIeIaA3zoAP/roI8VRJok27O3pp59WHH5xcXH0xRdfUFtbG3366acUHx9PAwMD9Mwzz9hXcfn8+9//vrLTUBpatGgRXX755bRz585xf8rKynT1KfeTn5+vaAw+8sgj9O677yo7DuX+XnzxRSX8eDQDsGQeFmdgRIQ6i6qujlHJrwnEcAhugaLlYn48cBLvABQnoP3qtISwPLihhAasw6Y8KwkFbuMMxTAQAAEQAAEQAAEQ8BSBLnb+lTTqd/6t3dlIa3Y2qYYXwpp9ovmXYZd0TVVogpPpCeEkczQYCIAACICA5wj47f7qxsZGhers2bMd6K5evVpxDl577bV05JFHKp8fffTRJOf33HMPbdy40aGOqxfeeOONg0289957NHfu3IPnYx3MmDGD9O5EHB4eVu7hUPeRkJBAzz33HJ1xxhljdY9rIKCZQFRoEBWmRlNxYycNDeuIN9Hc4/gVZHVaEoPcs7qIJARm1Ip5Qvz4xnK6aWkuWQKMXR/hTcZU0dKtCF6LwxQGAiAAAiAAAiAAAu4kIJrHJS7s/Pt09z56aWuNakiBHHV00ym5lMMZe/VaCms02ydr09sW6oEACIAACIxPwNg33PHHYfgnra2tSp8S3mtrlZWVVF9fr1w655xzbD+i+fPnK+dSxlft1FNPVRx7P/rRj2jevHmUnp5OYWFhFBoaqiT7WLFiBT3++ONKBuKzzjrLV28T4/ZSAqLpUpgWTcEW83cCpnFWYHECRtmJVH9Z205Ps67NiM6wGFfQS5dlzV3UwxN0GAiAAAiAAAiAAAi4i4A4/2QR1qpzEVbmJ7/dXE72S7hXLMikuenq9yktY5aIjJm8+w8GAiAAAiDgeQJ+uwNQNP3EJOuurX344YfKaUxMDB1xxBG2H5HsihOTZB3uttHxuNrupZdeSvIzniUnJ9MPf/hD5We8MrgOAp4kEB7MTsDUGCriSeigdcSTXU3YtoSqiBPwf3knYM/gt6G//6pso/DgQPrhCbOU3cATNuTGAjIxl4x8s9NiKDQo0I0toykQAAEQAAEQAAF/JCALiyUuOP8aO/rowfWlDhEc589LpwW5SbqRyiJsTnKk4XMt3QNGRRAAARDwcQJ+uwNQkluIFRcXqx7h+vXrlfMTTjhBdV1Oenp6lGuiDQgDARDQTyCMnWuzeSdgSJD5f4JmJETQrcvzSfRrbG1jcQv9/fNa20uGHQ9av1FW6c12kBp2w+gIBEAABEAABEDAIwR6Bw/s/NMrv9LJUimSKE12ENrayXnJdM6R02wvaTqWuaAkiQsIMD8qRNPAURgEQAAEfJiA+o3Xh29E69CPO+44JdGHJPwY3dFXVVVF//znP5VVqFNOOcWhydHEG6POQ4cCuAACIOA0AdndJk7AUC9wAuaybs2NS3IdMhX/898N9PZXDU7fkzsL9g+NuJShz51jQVsgAAIgAAIgAAK+R8BV558kRntwQyk1d6qTlB2eHkM/PHGm7p17IgWTz86/oEC/fRX1vX9MGDEIgMCkIOC3f3VFA0/s66+/pjlz5tD3vvc9Eqdgf3+/oon3gx/8wOEBf/DBB8q13Nxch89wAQRAQDuBEIs4AWOUcFvttd1b4/CMWLru5GyezKrb/cune2hTSbP6okFnstpeymLd7pIIMGjY6AYEQAAEQAAEQMBkAn0sbSKafxJVoMdEC/m371UoCcps64te3w2L9SdLC+Qdf3kpsgAMmRNbrjgGARAAASMI+K0DcNGiRXTDDTcoL9aSTffNN9+kvXv3KswffPBBSkxMVPEXx+Do7sAFCxaoPsMJCICAfgLBHHoriUEiQsyfCH4nM4F+fGKmw8089+Fu2sq6gGaYZCmW7MAwEAABEAABEAABEHCGQP/Q8H+0lvU5/2Th8cVtNfR5zX5Vd4mRwfSzZfkk4bt6TBZZRfMvkpPCwUAABEAABIwn4Nd/fR999FFavHgxvfrqq9TU1ESpqal0ySWXkDgH7e2tt96i6OhokuQgZ5xxhv3HOAcBEHCBgISAFKZGc/KLLurqV2vMuNCsrqon5ydzQhArvfLJnoP1Zfr8u/crlAnvEbxT0Gjb2z3IYTI9NDMxwuiu0R8IgAAIgAAIgIAPERDn364G1xKtrdnRROt3NanuWpKj3crOP8naq9cyeR4T50J9vf2iHgiAAAiAwAECU3iFR9/SEAiCgAsE6urqKCMjQ2mhtraW0tPTXWgNVScLgWEON5EMuJ195joBheffP6ulf/y7XoU2mB2Vd3DWYBGtNsOmc9jNtNgwM7pGnyAAAiAAAiAAAl5OYHTn3wDrCOu1bVVt9PimclV1C4ft3r4inyM2YlTXtZykx4VRRny4liooCwIg4EYCeP92I0wfbspvQ4B9+Jlh6CAwaQmILkw+68JEh5m/OfmCo9NpaeFUFevB4RF6YH0JVbcdyAiu+tCAkz1tvdTS1W9AT+gCBEAABEAABEDAlwhIwo4i1vxzxfkni7BPccSDvV21MMsl519SVAicf/ZQcQ4CIAACJhCAA9AOutVqpdbWVuVHjmEgAALGEhh1AkaFmusEnMJCNf9z/Ew6IVutB9rLotq/WVtCje19xoL5T29VrT20v2fQlL7RKQiAAAiAAAiAgPcRGLSOUBGH/bri/Gvgec3DG8poaFgdHHbRMRkOcyEtBGLDgygrCRImWpihLAiAAAh4igAcgEy2qKiIrr/+eiosLKTQ0FBKSUlRfuS4oKCArrvuOtq5c6enngHaBQEQsCNwwAkYRWY7AQPYCXjVwkw6arpa96+TE3Pct7aY2roH7Ebu+VMRbShrFq3EIc93hh5AAARAAARAAAS8moDIp5SyhnK/C2G/7b2DtGpdCXUPqDc/LClIpjMPT9N9/5LgLXdqFMmiKgwEQAAEQMB8An7tABwZGaGbb76ZDj/8cPrd735HJSUlJNdEFlF+5Li0tJSeeuopOvLII+nGG29Urpn/2DACEJj8BCyst5fPWntmZ4qzBATQDYtzqSBVrfsniTnuW1PMeoXGO+J4rv+fyf7w5P+HgDsEARAAARAAARAYk4C8r8iioL3jbszC41wU3cCHNpSyxIh6UVMWPy89fpZu511IkMzjokkWdWEgAAIgAALeQcDcGDuTGfzgBz9QMgCP5kGZPXs2HXvssTR16gHdr+bmZvrss8+U3X/Dw8P0xBNPUENDA/397383eeToHgT8g4DiBGTHWzFr2vQMmOfsCrYE0C1L8+jed4pp995v9f8aOvrpfl4x//lpBRQebOyfUwnRkazJc9KiSTjBQAAEQAAEQAAE/ItAFc9J2nv1L0TK7sEn36ugSpYXsbVZnK33ukU5up13lsApVMDOP5k/wUAABEAABLyHgN9mAf7b3/5G4gCULelz586lZ599lo455pgxn4w4Aa+66ir68ssvlfKvvPIKXXTRRWOWxUXnCCALkXOcUOoAgSFOviHaNqK/Z6Z1ctjtr97eRQ3t6kQcslPx9hUFpkx0JWFKYWq07hV6M3mibxAAARAAARAAAX0Eavf1Ut1+/XrEsgHiuY9206aSFtUAkiJD6J6zZlNseLDqurMnsuGvgBcno0ODnK2CciAAAgYQwPu3AZB9oAu/XZYRh59Ybm4uffTRR+M6/6SMOAY/+OADysvLU0KDf//738tlGAiAgEEEgniHWwE7ucKCAw3qcexuZDJ7Bzv6EiPVk2LZiffoRhHOHhm7ogevdvZZSXYAwEAABEAABEAABPyDQEtnv8vOv1c+2ePg/BPNvpUr8nU7/4R+dnIknH/+8c8QdwkCIOCDBPzWAfjVV18pO2ZWrlxJERETZ6aSMlJWTOrCQAAEjCUgYSSy081sJ2ACr4zfcWoBxYSpV7b/XdtOj5nkBGzpHKB6k7ISG/uvAL2BAAiAAAiAgH8T2N8z6PLC36tf1NE7OxpVIIM4bFfkTqbFhqmuazmZmRhOMk+CgQAIgAAIeCcBv3UADg4OKk9Ewn+dtdGyQ0P6tTac7QvlQAAEHAmIE1CScYSysLSZlhoTxiG/+RRhtyNx+x5xApabshNwT1sv7eOXAhgIgAAIgAAIgMDkJCDJPspbujkiSf/9vfllPcmPrUmS3mtOzlaSdthe13KcGhNKMj+CgQAIgAAIeC8Bc9+iTeQyY8YMpfeOjg6nR9HZ2amUHa3rdEUUBAEQcBuBEEugEg4s2eXMtBkJEXTr8nwHZ+T2PfsVJ6DVhHDgCn4pcCUToJk80TcIgAAIgAAIgMD4BCRbb2lTJ0niDr32zteN9H+f16qqS47eq0/Kpu/MSlBd13KSwNIoMzlxCAwEQAAEQMC7CZj7Bm0im/POO0/R83v99dedHsVrr72mhA2fc845TtdBQRAAAfcTCA0KVMKBzc4ulzv1QPIP+x2JihNwUzkZ7QSUlwJ5ORiwmpssxf1PHC2CAAiAAAiAgP8SEI3h4sZOGrTqd/5tKGqilz+pcYD44/mZdGJ2osN1Zy9EhVooOynS2eIoBwIgAAIgYCIBv3UA3nTTTZSZmUmS0OP//u//JnwE4vyTsrNmzaJbbrllwvIoAAIg4FkC4gSczVnmvMEJeNvyAoedgF/U7KfHTXACystBKSclcWWHgGefHFoHARAAARAAARBwlsCBxb0u6h/Sn2hsc2kLPf9xtUOXlx0/k07OT3a47uwF0WXOT4miAEn9CwMBEAABEPB6An7rAIyJiaGNGzfSUUcdRd///vfp7LPPpn/84x9UX19PovFntVqVY7kmO/4uvPBCpeymTZtI6sJAAATMJ/DtTkBzJ555PPldyeHAIaxRaGufm+QE7BkYZo2gLmWXs+14cAwCIAACIAACIOA7BL5hsT/5Pu/qt+oe9McVe+kPH1Q51P+v70ynpbNTHK47e0GkWESX2RKonvs4Wx/lQAAEQAAEjCcwhb9Y9O8lN368busxMDDwYFuCYIqo3x7CnCkjbYjjEDYxgbq6OsrIyFAK1tbWUnp6+sSVUAIExiHQO2ilooZOTr5h7p+zEg6/vX9tCYfgqlfpj5kZR9cvziFLgLGTZBHkhibPOP9ocBkEQAAEQAAEvJxAVWs3NXcO6B7lJ7vb6AmORrCXDTx/Xjqde5T+uXewZQpHYcRw9MO371O6B4mKIAAChhDA+7chmL2+E2PfRr0Ihzj0Rn9kWKPH4/12pozUhYEACBhPIDzYoiQGCQo8tCPf0yPLT4mm28bYCfhZ9X5lAm4dUTsGPT2exo5+fnHo93Q3aB8EQAAEQAAEQMDNBOr297rk/BM94iffq3Bw/p11RBqdc+Q03aO18FxL5jtw/ulGiIogAAIgYBoBi2k9m9zxXXfdZfII0D0IgIA7CUSEHHACyi48V0SyXR1Tfmq0Eg68ap16J6A4AZ/cVEHXLc42dCfg7r09SmhybHiwq7eG+iAAAiAAAiAAAgYQaOnqp9p9fbp72lHfQY9tLHPQAz51TgpdeHTGhJFP43UcyFp/ovkncy4YCIAACICA7xHw2xBg33tUk2vE2II8uZ6nN91N3+AwFSmZ8ozdbWfPQLL12TsBpcyxs+LpukXGOgFltV4SpshOSRgIgAAIgAAIgID3EmjvHaQSTualN7BI5h8iRzLImYNtbUnBVPrhCTN1O/8kz4doHmNB0ZYqjkHAdwjg/dt3npUnR+q3IcCehIq2QQAEzCMgGenE2RXK4tRmWgHvBLx1jHDgT3fvo99ySI6R4cBW1kaUl4khu5cBM/mgbxAAARAAARAAATWBngErlTV363b+lTV30QPrHZ1/C3OT6DIXnH8ilZ6dHAnnn/px4QwEQAAEfI6AuW/IPocLAwYBEPAFAqJLI+LU4ewMNNMKxQm4LM8hO/AnJjgBB4ZGqJSdgCP2SuBmAkLfIAACIAACIAACCgFZpJPFumGd39Mi+SGRB/38fW9rx2cl0BXzMylggoSHtnXsjzOTIighMsT+Ms5BAARAAAR8jADiwf7zwCR77/bt22nHjh20b98+5Wp8fDzNmTOHjjrqKAoKCvKxR4vhgoB/Ewi2BFAh7wQUp1dXv3nZuQvZESlOwFXrSlXhOOIEJN4JeN2iHBJNHSNMOFRyRsGcqVFGdIc+QAAEQAAEQAAEnCRQ1drDGsZq552TVWnPvl66b00x9bIMiq0dMzOOfnJSFgW4MM+YmRhOyVGhts3iGARAAARAwEcJ+L0DsKenh/73f/+XnnvuuYOOP/tnGRcXR5dffjn9/Oc/p6govDjb88E5CHgrgaDAACU7sCQG6ewz2Qm4PI8eGMMJOGVzOV17snFOwL3dg7wjsZemJ4R762PDuEAABEAABEDArwg0dfTTvp5BXffc0N5Hv2bnXzeHD9vaERmxdD0vMloC9Ad8pceFUWpMmG2zOAYBEAABEPBhAvq/EXz4pkeHXlpaquzwe/DBB6mtrY31Nr4Z80d2BD700EN02GGHkdSBgQAI+A4B2V1XkBJNcRHm7uKVkOSf8U7AYHZK2tq2qn309JZKGtGr9m3bmJPH9fyyUMu7BWAgAAIgAAIgAALmEhDdv5q2Hl2DEKfhb9YW8yLnkKr+HI6AuHFJLlns5hyqQhOcpMaEUkY8FgsnwISPQQAEQMCnCKjfRH1q6K4NtqOjgxYvXkx79uxRnH4S6iuOwC1btlBJSYnyI8ejjj9xDkrZJUuWkNSFgQAI+A4BCX3J47DXxMhgUwc9Z9rYTsCPK/bSi1trlL9FRg2wbn8f7WmDE9Ao3ugHBEAABEAABOwJiN5feUs3LwLafzLxee+gVdH8k539tpbPmXpvXsoLjiyFoteSokJoZmKE3uqoBwIgAAIg4KUE9H8zeOkNOTusVatWUUNDg1JcQoC/+uoruvnmm2n+/PmUm5ur/MjxTTfdRP/+97/p3nvvVcpKHakLAwEQ8C0CU1j8WjLYJUebK2I96gQMClTr/q3f1URvfllvKFTZCah314GhA0VnIAACIAACIDAJCUjijj473T5nblMShjy8oUzR/rMtn8XJOiTaQJKh6bX4iGCSdmAgAAIgAAKTj4DfOgDffPNNEofABRdcQHfeeadyPN7jlXJ33HEHXXjhhcoOHakLAwEQ8D0C8v9yVlIkpcWaK2YtTsCbT8lzSP7x6hd19G5Rk6FgG9r7qZpfQGAgAAIgAAIgAALGEWjtGiD50WoiGfL0+5VU1NipqpoSHcpJx/IpPFi/xHtMWBDl8GKpzJdgIAACIAACk4+A3zoAa2pqlKd56aWXOv1UR8uO1nW6IgqCAAh4FYEZCREkwtZm2uEszn01Z+azn2I//3E1ba1sM3RojSw+LrsQYCAAAiAAAiAAAp4n0D80rPt795VtNbS1Sj1PEMfd7SvyKZp/67WoUAvlcfiwKxmD9faNeiAAAiAAAsYQ8FsH4Gg23+TkZKdJj5aNjIx0ug4KggAIeCcBEbaemWiuuPXxWYl06fEzVYBEBuh371fQ13XtquuePpEMhFWt3Z7uBu2DAAiAAAiAgF8TGBHdv+ZuEv0/rbb66wZas1MdKRAaFEArl+ezxIn+6Ibw4EDF+SeJ02AgAAIgAAKTl4DfOgAlo69YeXm50093tOxoXacroiAIgIBXEkiNCVN0bsyMdFk6O4XOO2qaio+8FDzybhlVsDC4kdbcOUCV7ASUpEcwEAABEAABEAAB9xPYs6+Xujnzr1aThGGvfLJHVS2QJzCS7XeWCwk7xIFYkBpNQS5kDFYNCicgAAIgAAJeS8BvHYBXXnml8pL72GOP0cjIyIQPSMo8+uijiibGFVdcMWF5FAABEPANArJiLslBzHQCnndUOi0tnKoCNmAdUbL71XO2XiOtRXEC9sAJaCR09AUCIAACIOAXBPb3DJLIbmi1nfUd9PSWSodqV7GUyNz0WIfrzl6QTMHi/HMlY7CzfaEcCIAACICA+QT81gF4/vnn02WXXUbbtm2js88+m5qa1NvpbR9Nc3MznXvuufTJJ5+Q6ABKMhAYCIDA5CGQGBlCuVOjTHMCitj2/3Ao8HezElRQZYfAfWuLaW+3dpFwVUMaT0SUHDsBNUJDcRAAARAAARA4BIEB67Dy3XqIImN+VN3Wo0QF2IcM/+DY6XRiduKYdZy5GGyZQoXs/HMlY7Az/aAMCIAACICA9xDQnybKe+7hkCN58cUXx/184cKFtHPnTlq9ejVlZmbS0qVL6ZhjjiHR+pMXcnH8ffbZZ7RhwwYaGBhQPpM60uYll1wybrv4AARAwPcIxEcEUyaH0FS2mpMMI4D/5ly9MIt62On3dV3HQYD7eLfAb9YU011nzqboUP3i3gcbdPKgtWuQdwF2/2d3JDSBnMSGYiAAAiAAAiDgQECkNUTWY2hYm8RGS2c/rVpbQn2cNMTWls9JodPnptpe0nRsCZxC+SnRFMbafzAQAAEQAAH/ITCFv5C0fRP5GJuAgADFmTfRsAXDeCnv7T+Tclardu2OicbgT5/X1dVRRkaGcsu1tbWUnp7uT7ePe/ViAvXtfbSnrde0EUpmwF+zw89e/y8rKYLuPLXQ8Ml6QmQw5Sgh0nACmvaPAh2DAAiAAAj4NIG6/b1Uu0+bpEdn/xDd/dYuh5Dh78yKp+sX55AsHOoxSfRRkBpFUQYuKuoZJ+qAAAi4lwDev93L01db84sQYHHgTfQjD3C8MmN95qsPHOMGARA4NIFpsWGUFqs/k96hW5/4UwnFuXVZHsk4bE12Jj7ybinvHphYs9S2nqvHbd2DVMbZCuXvIwwEQAAEQAAEQEAbAXHk1WnU85Vw4YfWlzo4/8Rxd/VJ2bqdf5LkNy8Fzj9tTxClQQAEQGDyEJj0IcC7d++ePE8LdwICIGAIgRkJEWTlTLySEMMMk1X521fk091v72L9v8GDQ9jZ0Em/21xB1y/ilX+ZxRtkEoYsTkDZCWhkvwbdHroBARAAARAAAY8QkEW7cmURzfnmRevviU3lVM4hw7aWER9ON5+Spzthh2wYFL3jmDDj5ERsx49jEAABEAAB8wlMegfgjBkzzKeMEYAACPgcAdEDtLJWjzi/zLAETkxy+4oCxQnY1f+t5MAnu/fRnz7eTZefOGtc2QJPjFdxArZ0UW5yFJyAngCMNkEABEAABCYdgSrevT9odX7nvuy2l+/47XvaVSwSWKf4tuX5FBGi79VNnH+yiBfH7cBAAARAAAT8l4BfhAD77+PFnYMACOglIFqfMlmODtM32dbbr229NA4Dlgl/aJD6T/WmkhZ69Ys626KGHO/vGdKVwdCQwaETEAABEAABEPAiAk0d/ZoXEV/fXk/v8Xe8rUWEBNJtHBUgycr0WibrCMvCIgwEQAAEQMC/CajfKv2bBe4eBEAABFQEJNxVsuRF6lxxVzWm8yQzKVIJ+bHYhfy++WU9rd3ZqLNV/dUkJFnEzGEgAAIgAAIgAAJjE+gZsFJNW8/YH45zdVNJM72+Xb24F8TZem9ZmkfpceHj1Jr48iyOaEiOMk/beOIRogQIgAAIgIBRBOAANIo0+gEBEPBJApItL59Ft8OCA00b/5xpMXTtomwO+VUP4cWtNfRRxV71RQPOJJOhWaHRBtweugABEAABEAAB3QREw0/0+/iX0/ZVbTs995Fat1y+8q87OUdZiHS6IbuCGfFhlBID558dFpyCAAiAgN8SgAPQbx89bhwEQMBZAkGBATwBj9ItvO1sP4cq951ZCYrun32ZZ96vpK/r1FpB9mU8cV7BLze9g99qE3qiD7QJAiAAAiAAAr5EYERx/nVR3+Cw08OWBbXfvV9BLP+nsktPmEnHzIpXXdNyMo1lRFzZOailL5QFARAAARDwDQJwAPrGc8IoQQAETCYQGhRIhanRJOE4Ztni/Kl04TEZqu6H+Y3h0Y1lVNWqzhaoKuSBE9nhUNLURZLhEAYCIAACIAAC/k7Ayt+HRY2dJHq5zpo4DH+7uZxsk31J3bOPSKOlhSnONuNQTnb9TU/QHzbs0CAugAAIgAAITAoCcABOiseImwABEDCCgIQB5/FOQAkLNsvOOjyNVsxRvxT0D43QqvWl1NzZb+iwBrjfsuYu3rVgt23B0FGgMxAAARAAARAwl8CAdZh2NXQ6OPImGtUbX9ZRcWOXqtjRM+LogqPVi32qAhOcJEWFkOj+wUAABEAABEDAngAcgPZEcA4CIAAChyAQFRpEeVOjyCwfoGQn/u/jZtB3MxNUo+zsG6LfrC2mDv5tpHX2WWn3Xm1C50aOD32BAAiAAAiAgCcJiBzGzvpOlsVwPuxXxlPU0EFvcEIvW0uMDKYrF2Sx5q++hcYErp/FGX9hIAACIAACIDAWATgAx6KCayAAAiBwCAIx4UGUnRzpkJTjEFXc+lEAvxj85KQsmp0WrWq3uXOAVq0r0aQ9pGpA54n0a/TuQ51DRTUQAAEQAAEQcBuBzv4hZeffoFWbHIYs2j25Wa37JwuL1y3KochQi67xxUUEUY4yN9HnPNTVKSqBAAiAAAj4FAE4AH3qcWGwIAAC3kIgIdLcEBtJTHLTKbk0I16t8SO78R5jTUDRIjLSpF+jdx8aeX/oCwRAAARAAARsCUjyjmIO+7UOa5PBGGHZjKe3VFJ7r3rHvoT95nKEgR6LYqdhbnKU7p2DevpEHRAAARAAAd8jAAeg7z0zjBgEQMBLCEyNDqWM+DDTRhMebKGVK/IpiZ2RtvZ1fQc980EVyUuGUSZdlbMeYP+QthAoo8aHfkAABEAABEDAXQRk17to4HIOD832zteN9O/adlW9udNi6AzW+NVj4axPnM/6xAFmaZPoGTTqgAAIgAAImELAbx2As2bNoqysLKqoqHAa/J49eygzM1Op53QlFAQBEJjUBNLjwik9zjwnYFx4MN3OTkBZ/be1jyv20l8/3WN7yePHQ7wLopQzA0uGYBgIgAAIgAAITEYCtft6qaq1hxNgab87WSj7+2e1qoqxYUGKrIfIe2i1YEsA5adGkYWjAmAgAAIgAAIgMBEBv/22qKmpoerqahocHJyI0cHPh4aGlDpSDwYCIAACowQyOAw3k0W3dczdR5tw6XdqbBjduiyPQvhFwNZW8y6DNTsabS95/FhE0Ctauj3eDzoAARAAARAAASMJSMb7qtZuqtvfp6vbngErPfleBQ3beA7F5XfNydkUy4t5Ws0SOIUK2PkXYgnUWhXlQQAEQAAE/JSA+m3RTyHgtkEABEDAVQISDizi22ZF4GSz9s9Pl+Q49P/Sthr6V+VeV29PU33RRZIdEjAQAAEQAAEQmAwERnhne1lzNye8GtB1O+I8fPbDKmrtVtc/+8hpNIfDf7WazDXyOOxXpEBgIAACIAACIOAsATgAnSXF5To6OpTS4eFq0X0NTaAoCIDAJCYgiUHyU6Ip0CQv4BEZcXTFgiwHwk+9X0k7WRfQSJMdEnvtXnSM7B99gQAIgAAIgIA7CEhSraLGTpLFLb32bnEzfbp7n6q66Padd1S66pozJxJtkMPJQqJDg5wpjjIgAAIgAAIgcJAAHIAHUUx88PLLLyuFZsyYMXFhlAABEPBLAjHhQVSYFk3BFu1aPu4AtjA3iS46JkPVlGjyPfJuGUmmXiNNNJK6OeQJBgIgAAIgAAK+SGDAOky7ONNvV7/+77Lqth56mXfj21pkiIWu5dBfPQuGmYkRFB+hPWTYtn8cgwAIgAAI+CcBv9k3vmjRojGf8GWXXUYRERFjfjZ6cWBggKqqqqilpYU1vqbQ0qVLRz/CbxAAARBwICAT+9lpMcqOgYGhEYfPPX3hTM4kuL93iNbvajrYVR9n5121roR+deZsknBlI0wcj5IU5DAObxKhchgIgAAIgAAI+AqBPta0lZ1/g1b93+P9/N37xKZykiRZtvaThVkkUQNaLSM+jJIN+g7XOjaUBwEQAAEQ8H4CfuMAfP/99xXnnWhwjJocf/bZZ6OnTv2WLMC33367U2VRCARAwH8JhAYF0hx2ApY0dVLPwLChIGSh4pLjZlB77yB9YhNy1NE3RPevLaG72QkYw1kHjTB5cSrjrIeFqdEUYFJotBH3iT5AAARAAAQmDwFJ2FHMzj97x53WO/zTR7upsaNfVe3UOSl01Iw41TVnTqZGh1B6HGSInGGFMiAAAiAAAmMT8BsH4IIFCxQH4CiGLVu2KOfz5s075A5AeZEODQ2l1NRUOv744+miiy46ZPnR9vEbBEAABGTXmzi+SngXnCvhQ3pIirPt6pOyud8SZQfDaBtNnf304PoS+vlphSROSiNM7r2Kw4+zOUkKDARAAARAAAS8mcAQa/6V8sKVq86/LWWt9GGFOgmXhO9+/9jpmm8/ITKYZnFdGAiAAAiAAAi4QsBvHICyA9DWAgIOhKO98MILVFhYaPsRjkEABEDAbQQsgQecgOUt3S4JiOsZkDggb16aS796u4j22GTlrWRtvsc2ltEtS/NIxmeEtXYNUAiPJyMeuxeM4I0+QAAEQAAEtBOQ6KByzvbrqnxHfXsfPf/xbtUAwnjR7frFOZq/d6PDLJSdFKnayKBqGCcgAAIgAAIg4CQBY978nByMkcUuueQSkp+4OO1b8I0cJ/oCARDwfQKyGy93aiQlRWnX+3H17sODLbRyeT4l8u4BW/uqroMe5sQgrmgb2bbnzLFkBq7b3+tMUZQBARAAARAAAcMJ1O7rI5HLcMXke1V0/wbstAN/PD9TswZveHAg5XHGX0houPJEUBcEQAAEQGCUgN86AGXn3/PPP6+E9o7CwG8QAAEQ8BQBkROQENhpsWGe6mLcdiVb4G0rCkiSk9jav2vblcQgInRulMnLVQPvjICBAAiAAAiAgDcR2NczSLJzz1V7aVu1ate9tLc4P5m+m5WgqemQoADKT43SvGNQUycoDAIgAAIg4FcE/NYB6FdPGTcLAiDgNQSmJ4TTzETjw2DF8XjrsjwlDNcWhmQ4vG9tMXWz4LlRVtPWS012ouhG9Y1+QAAEQAAEQMCegCyEVbBUh6u2raqNNha3qJrJiAujS747U3VtopOgwClUkBLN39nGaPVONB58DgIgAAIgMDkI+K0DcMeOHSQZfXNycqi+vn7CpyllsrOzKSsri8rKyiYsjwIgAAIgMB6B1JgwZTcgbwo01HI4jOiOUwtIQopsTV567l1d5HLYk22bEx3v5qQgLZyQBAYCIAACIAACZhIYHvlGSfohv12xipYu+sOHVaomRPv2hsW5JJq8zlogy4bkpURRmN13tbP1UQ4EQAAEQAAExiPg/LfReC346PWXX36ZqqurFafetGnTJrwLKZObm6vUkbowEAABEHCFgOgB5vMEn+f5hlouOwElA3BUqDocuIaThNzz9i5q6x4wbDySGViSg8BAAARAAARAwCwCla3d5KoUxqe799E9vJDWayepcenxM2ka7wB01mRhMIflQqJCg5ytgnIgAAIgAAIg4DQBv3UAbtmyRcmmdeaZZzoN66yzziLJDrZp0yan66AgCIAACIxHIDY8mJODRPHfovFKeOb6rMQIuuv02RQXrn7BaOCwXMkY3GzQzjz+c0ry4rXXQKejZ4iiVRAAARAAAV8kIJq0bd2Duocu7wVrdjTSYxvLaGhYvYPwxOxEWpibpKntTP5+jmPdXhgIgAAIgAAIeIKA3zoAR8N4586d6zTXOXPmKGVLS0udroOCIAACIHAoAjLRz0qKPFQRj3wmOxLuOmM2JdtlJm5lZ9yveCdgPWfsNcLECSghyCK+DgMBEAABEAABowh09A45JOvQ0vcIhwy/8K9qemlbDaldf0Rzp8XQ5SfOUjYbONumRAYkR4c6WxzlQAAEQAAEQEAzAb91AHZ3HxD6jYx0/sV7tGxnZ6dm0KgAAiAAAuMRkEm/7Moz2qbyi8YvTy+ktBj1C8d+fin61epdJDp9Rpg4Acubu6i9F05AI3ijDxAAARDwdwID1mEqZ80++f7RY/1Dw/Twu2W0oajZofrJecn0s+V5FBqk1tt1KGhzQfT+zJgH2AwBhyAAAiAAAn5AwG8dgHFxccrjbWpqcvoxj5aNiopyug4KggAIgIAzBFLYCZcR77xOkDNtOlMmITKEfsk7AWfEqzMTd/Vb6d53iqiMHXNGmGivlzZ1kezIgIEACIAACICApwjIzr3y5m6HkF1n+9vPi1Wi97d9z36HKhcek0E/nj+LLAHOv2KJFnDu1EiS5B8wEAABEAABEPAkAee/nTw5ChPaluy/YuvWrXO697Vr1yplJRMwDARAAATcTSA9LpxS7XbjubuPsdqLCQuin/NOQBEetzURM79vTTHtrO+wveyxY8UJyA7Hzn44AT0GGQ2DAAiAgJ8TqG7rIVnk0mO1nDDrl//c6bBD3sLOu2tPzqazj5imKexXxiA7/8KD1Ym59IwNdUAABEAABEBgIgJ+6wBctmyZktDj2WefpeLi4ok40a5du+gPf/iD8qW+fPnyCcujAAiAAAjoITCTXwQkJNhoiwyx0O0rCqgwNVrV9YB1hB5YX0Lbaxx3OqgKuulkmL2AJY1d/HIGJ6CbkKIZEAABEACB/xBo4SRXzZ36ss/LYtjdrJG71y5pSERIIN15agGdwEk/tFpSVDB0/7RCQ3kQAAEQAAHdBPzWAfiTn/yEIiIiqL+/nxYtWkSrV68eF+Jbb71FS5Ysob6+PgoLC6Nrrrlm3LL4AARAAARcJZCVFEEJkcZnARQNopXL8+nIjFjVLUhmw0dY62hrZZvquqdOFCcghwN3D+jboeGpcaFdEAABEAAB3yUg3yl6tW23lLXS/WtLSHbG25ok0rrnzDmUb7d4ZltmvOMDun/qnffjlcV1EAABEAABEHAHAb/db56YmEjPPPMMXXzxxdTS0kJnnXUWZWZm0oknnkipqakK28bGRvrwww9p9+7dym7BKVOm0NNPP01Tp051B3u0AQIgAAJjEpC/NdmcGdg6zJp4fcbuhAu2BNBNp+TSbzdX0Ce79x0c3zArpT+5uZxEOP0kFjj3tFnZ6VjS2EkF/FIVwbsTYSAAAiAAAiCgl8DQ8IiiaStSE1rsG/7ue317Hf/UO1TLZtmMW5bmkchoaDWR+xPZDej+aSWH8iAAAiAAAq4Q8Ou3qv/6r/+ikZERkt2Avb29VFlZSVVVVSqe8sUvJrsFxfn33//936rPcQICIAACniAQwG8HeSlRVMxOML1aRXrHZQkMoOsW5VDoh1Ukux5GTf4c/v6DKuofJ2EXyQAAQABJREFUGqHlc1JGL3vst+w8lPufnRZDslMCBgIgAAIgAAJaCchcXpJ+DPB3lxazstPwWf7O+7Bir0O1Y2fG0zWs+SeLZnpM5D6wuKWHHOqAAAiAAAi4QkDft5YrPXpZXdkBWFFRQbfddhsddthhyuhkoiA/sgtn7ty5dOeddypl4PzzsoeH4YDAJCcgOwPy2QkYboLzS/q+YkEmLS103PH8563V9IGNY9CTj0GcgEWNHex0VIddebJPtA0CIAACIDB5CNTu69O8m17ChX/DIb9jOf9OOyyVbliSo9v5l8gSH1OjQycPYNwJCIAACICAzxCYwo4ujZvhfebedA3UarXSvn0Hwt7i4+PJYjFuk+Tnn39Oa9asoY8++oiKioqotbWVgoKCKC0tjU444QS6/PLLlRBlXTc2TqW//vWv9Pzzz9PXX39N7e3tSnjz/PnzFZ3D7373u+PUcv1yXV0dZWRkKA3V1tZSenq6642iBRCYpAQGORHHrgZxgmnbveAOHPIV8bfPaumtrxpUzQXyAsnKFfl02LQY1XVPnYQGBdAc7iuIdyfCQAAEQAAEQMAZAvt6BqmUNWW1WGtXP61aV0r17X2qavy1R5d+dyYtna1/B7x8l81Nj0Xor4osTkAABIwggPdvIyh7fx9wAHrJM1qwYIGiNzjRcC655BIlG3FwsGsJAiShyfe+9z3F4ThWnwEBAfTLX/6S7rrrrrE+dvka/gC5jBAN+BkB2QG3q6GTxBlohr35ZT393+e1qq7DggLprjMKaUZChOq6p04kU3FhWjRenDwFGO2CAAiAwCQi0McJO3by4ployjpr7b2DdOc/dpI4Dm0thEN9r2dpjKNmxNle1nQsun+ykIXQX03YUBgEQMBNBPD+7SaQPt4MtlJ4yQNsaDiwu0Z2+91www302muv0aeffkpbt26lRx55hKZNm6aM9MUXX6RLL73U5VH/8Ic/POj8O/nkk+kf//iH0t9zzz1HWVlZijbi3XffTc8++6zLfaEBEAAB1wmEsrOtIDWKd8DxG4QJds6R0+hUO92/PnZKrlpXQm3dA4aMSEKyypq7FIkGQzpEJyAAAiAAAj5JQLLJy/eFFueflXXBH99U7uD8i+UkH788vdAl559AlMUyOP988p8TBg0CIAACk4YAdgB6yaM8/fTTSXb3nXfeeRQY6Ch2v3fvXiUMuKysTBnxli1bSHYN6rH33nuPFi9erFQ944wz6M0331T1KX3NmzeP9uzZQ7GxsUpilLg4/SueY40RKxBjUcE1EJiYgDjBingnoLzcGG0jHA78BL8c2WYHljGkx4XR3WfMNuzFJikqmLKTo4y+ffQHAiAAAiDgIwQqWrqotUu9i2+iob+8rYbe2dGoKibfbyuX51NiZIjqutaTBNb9y52K7y2t3FAeBEDAfQTw/u0+lr7cEnYA8tPbvHmzsuvupJNOojlz5ig74DIzM2m8H9kh525bvXo1XXDBBSpHnG0fiYmJ9PDDDx+8JDsE9dpDDz2kVBV9w6eeesqhT+lr1apVShnRBfzjH/+otyvUAwEQcDMBCYPN45cICSUy2gJYAOnqk7KV/m37rtvfR4+8W0ZDnDHRCJOXuuq9PUZ0hT5AAARAAAR8jEBzZ79m59+2qjYH519CRDD94rRCl51/ovuXyVl/YSAAAiAAAiBgNgHjMlyYfadj9N/S0kIXXXQRyW46sfHyoUg2YNvP5NwMk1DdUausrBw91PS7q6uLNm3apNRZsmTJuMk3zj33XIqOjqbOzk5lh+DPfvYzTf2gMAiAgOcIxIQHUQ47AQ+Ew3qun7FaDmYdpFuW5tFdb++khvb+g0WKGjvpmS2VdM3J2eyc9PzfyMaOfiUDY1ps2MEx4AAEQAAEQMC/Ccguea0LRPW8iCXfX7Zm4VW2ny7JpWgO/3XFZLFOvq8tSGDlCkbUBQEQAAEQcBMBv90BODQ0RCtWrFCcf+LcO/zww+m0005TsIqD7+KLL1bOU1NTFeefXJOw2P/5n/9RQnXdxF9TMwMD3+psjRUm7Exjn332GQ0OHgiJWLhw4bhVJMnIcccdp3wudYQXDARAwHsIxPPOhOzkSDLA1+Zw05GhFrqNQ6JEF8nW/lXZRn/njMFGWU1bL+/y+PbvolH9oh8QAAEQAAHvIyC70GVhTItCRu+glXewl9KAXYKtS4+fqXzHunqX0xPCSXbuw0AABEAABEDAGwj4rQPwhRdeoC+//FJ5Bs8//zxt376d7r///oPP5M9//jO9/fbbVF9fT2+88QaJI7CoqIhEq0/Km2GjOxWl74KCAl1DkHsYtfz8/NHDMX+Pfm61Wqm8vHzMMrgIAiBgHgHRJDIrrCgpKpRuZSegZEa0tbe+aqANu5psL3n0uLK1myRrIwwEQAAEQMC/CVS0dNPAkPNSFLIB4PcfVFED7yi3tZNyk2hRfrLtJV3HslCXGoNd6rrgoRIIgAAIgIBHCKjf3DzShXc2+vrrrysDW758ubKr71CjPPvss5WdgrIrTjLwmuEMG+HMZLYOStEL1GMi/jlq6enpo4dj/s7IyDh4vbZW264e6edQP42NapHlgx3hAARAQBOB5OhQmpkYrqmOuwrPYk0jCZGy1yN8YWs1fV69z13dHLIdfn/jHR/d9P/s3Qd8nXW9+PFvs/do04w2aZqmM7QsoWUIBYQyBBmiggMBERH9C17EcbkC3oteuHARrqgXLYp6GSoqQzZl76EtlO6RNulI0rTZO+n/933KSZ/n5OzVc3I+v9er5Jm/53neJyQ53/P7fb+dfYxS9gnFTgQQQGAcCzTs7jEfBgX3e+Dv7++Qtzc7f1fp77VLjq0xo+vDS2WRafL+1U4m7984/pbj0RBAAIGEFEjaAOCKFSusX+5f/OIXPb5w9px/eoAW/rjqqquku7tb7rzzTo/nRHPjT3/6U3n77betS2h+Pp2OHErTHICulpeX51r0+DU3d/8fLl1dXR6P8bZRg4e+/i1cuNDbqWxHAIEgBXSEQdXEAzPK4NCqIrnsuBmOO9ag3M+e3yDrzVSsWDStiLx2Z6f0DgzH4nJcAwEEEEAgjgR0FPi2tt6g7mjltnZ54J2tjnN0qu63T55l5Zd17AhyRWOHs0yKDvL+BQnH4QgggAACURdI2gDg7t37PvGrqakZRdYRfq7W09PjWhz9+olPfMJafvbZZ0e3xWJBp/5+//vfty5VWloqv/zlL0O+bF/f/mkO9uf11GFmZubo5t7e4P6wGj2RBQQQiIlAZXGOTD1ABTFOnFMqnz58quM5B0wuplufWSs72mPzs2NweK+s3tlh8jgRBHS8EKwggAAC41hAf+br1F/94CnQ1trVbz6kWu84R8f7aRErTW8RTtPg3wwz8i8/y5kjN5w+ORcBBBBAAIFICSRtVloNfmluO3sQTKveuprm/ps9e7Zr1fqalbXvjwLdF6v24Ycfyrnnnmvdq17/z3/+s2gQMNTmegY931UMxFtf9qIj2dnBjS7yN2VYpwAzCtCbPNsRCE1Ak42PmHdBWiE31u3Th1fKrq4BeWldy+ilO/uG5Jan1siPPjVfCt0KhoweFMEFzf20ZkenHDSlgJEXEXSlKwQQQCAeBUbM6O/1JgWEfgAUaNNCIXcsWy8d5veTvZ3/sUrREe3htPTUCVbF31j8vgvnPjkXAQQQQCB5BZI2ADht2jRZs2aNNDU1jb76ZWVlkp+fLzrd9a233hoTAFy5cqV1bLh5QUYv6Gdh8+bNsmTJEtmzZ49o1d8HH3xQjj/+eD9n+d6tz+dq/qb16nRnV/M3Xdh1nOurv/yCruP4igACkRWYbvIXDZsgYHNHbKvj6s/Fy46rkT1mKtb7je2jD9Vk7uPWp9fID8+sMwVDUke3R2uhx0wDXmumHs8rL5AU9+SE0boo/SKAAAIIxFxgi8n7px80BdP+8OYWa8Sg/ZzDTODvnMOco9jt+wNZzs5Ilbnl+ZKVHv3fc4HcD8cggAACCCDgSSBppwAffvjhloerErALRwNsmv9P8/zZR8C1tbXJLbfcYuUNrKurcx0eta/bt2+Xk08+WfSrvrH+zW9+I2effXbY17MH5uwFQTx1bB/FZy8I4ulYtiGAQPwIaGXgkrz9KQ1idWdpKSly9Sdmy3QzEtHeNrZ0y/8s2yCaqy8WraPXVC63poTF5nqxeCaugQACCCCwX2CXmca7M8jR7i+bEerPrtr/wb/2VpqfKVeaqb8pOnc3xFaUky7zzchzgn8hAnIaAggggEDMBJI2AKj5/DTQ9/jjjzuwr7jiCmtdA4MHH3ywXHvttXLllVfKggULZN26dda+iy66yHFOpFd27dolp5xyimzatMnq+mc/+5lE6pr24KWOgPTVXPvT0tJk1qxZvg5lHwIIxJGAfmgw0yQgn5gb+yCgjoL47mlzxwQg/7F1j9z7er31czcWVLu7B2Tzrv2jmGNxTa6BAAIIIBB9gZ6BIdlkPlgKptW3dsvSV/f9Xe06LyM1Rf7llNmixT9CbRWFWdbIPwp+hCrIeQgggAACsRRI2gDgOeecIzoNWEfBbdy4cdT8k5/8pFx66aXWm9T169fL7bffLnfffbe48v7plNyvf/3ro8dHeqG9vV1OPfVUWbVqldX1zTffLN/4xjcidpkjjzxyNO+hFhfx1jQ/4Jtvvmnt1nPS00lm7M2K7QjEo4AGAbUKoY5MiHUrzsmQ7582T3JNMNDenlvdJA8v327fFNVlnX7cYKaI0RBAAAEExoeAjiRfZ/L+BTOivKt/SH767LoxuQI1bUX1pNyQYHTAoBb70LQb+vuWhgACCCCAQCIIJG0AsKioSOrr62XLli1SW1vreK2WLl0qv/71r2XRokWSm5srWg1XRwDeeuut8thjj5m8UtFh08rDGoD8xz/+Yd3PddddJ9/73vcc9xbuiuYAdFUzfu6556wAqKc+//rXv0pHR4e1S4uQ0BBAIPEENAfenLJ8KcgOfXRDqE89tThbvrNkjqS55eH707sN8tiK2AUBG/f0ilZ8pCGAAAIIJL7AppYu6TW5XgNtWhjr5y9skOZO5++BJXVlctysyYF24zhOi33MqyiQsoLwKgY7OmUFAQQQQACBGAhEJ5IVgxuP9iW+8pWvyBtvvGEFwTQwt2LFCrnmmmtEp8NGo+mIOw20vfbaa1b3V111ldx0001BX+ree++1PonUTyNvvPFGj+d/5zvfsbZrFWQdXTg87PxDSqcguwKPGii97LLLPPbDRgQQiH8BDQLONQUx8rOi87PLl8Bc8wbpyhNmjjnk/re3xjQIqDkIdcoYDQEEEEAgcQV2tPda1eaDeYK//XObLG9oc5yio+O/dFS1Y1ugK5rmYv7UwphUtg/0njgOAQQQQACBQAVi/44w0DtLsuMuvPBCeeaZZ6ynPumkk0QDkK6qw54oMjIyxlQp9nScp23a/wUXXGBVFX700UetfINXX321TJkyRT744AP58Y9/LFu3brVO1cInxcXFnrphGwIIJIhAqhUEzJdVOzqku98Z8I/2IxxdO0naegfk929scVxKg4DazjpkimN7NFZ0qtjanZ2ywLxpI09TNITpEwEEEIiuQGffoGxpDS6lw/KGPfKX9xodN1aQnS5XfWJWSL8LCs25s8vyQjrXcROsIIAAAgggcIAECAAeIHj3y+qUW1d7/vnnrQIkrnVPX6urq60pzJ72BbJNqwrrFN8nnnhCXnjhBeuf/Tyd5vzDH/5QLr/8cvtmlhFAIEEFNPClIwE1CBjM9KlIPO7p8ytkZETk/94aGwTUOr2fikEQsG9wxKoMPLc8n3xNkXhR6QMBBBCIkcDg8IiV98/M5g24NXf0yV1m6q/9FM1IcdVJM2VSXmbA/bgOLDfFPrTCPfn+XCJ8RQABBBBIRAECgOZV0ymwjzzyiGhOPB0Bt3v3buu1nDhxosyfP19OPvlkOfvss6M2/fdAfONkZ2dbFZDvv/9+0WnDOsW5ra1NyspMTpTjjpNvfvObcvTRRx+IW+OaCCAQJYGMtBSTtyhfPtzeIf0mIBbL9smDK6zLuQcBH/hoJGAsgoBtPYOy1RQFCTXpeyy9uBYCCCCAgOwrymeKfgwMBf47S4/96XPrxox4v3DhNKmbUhgUq9b3qDGFPsj3FxQbByOAAAIIxKnAhL2mxem9xeS2dAqsBrtcVX71oi4S+6d8FRUVctddd4lWD6aFL6DVl6uqqqyOGhoapLKyMvxO6QEBBAIS6B8altU7OmM+ElBv7okPdsgf3nSOBNTt+sYsFkFAvdZMk/9pcn7wI0D0XBoCCCCAQOwENu/qlp3tfQFfUP+G/+WLG+WVDbsc5yysmShXm6m/9r/tHQd4WEkzxT5ml+ZLYU66h71sQgABBBJLgPffifV6Retuk7oIyJ133mkV3tDgnyvoN336dDnqqKOsf7qsTfdt375dPv3pT8sdd9xhbeM/CCCAQKIKZKalykFTDkxhkDMWVHhMvq4jAR+NUXVgrSLZ3U9RkET9/uW+EUAgOQSazDTeYIJ/qvL0h01jgn9TirLkiuNrgwr+ZaanyHwzWpDgX3J8r/GUCCCAQLIIJG0A8K233rKq+mpwLz8/X7TYRVNTk2zcuFFef/11658u6zbdV1hYaAUCr732WtFzaQgggEAiC6SbnIDzTJVeTWoe6+YzCLh8W9Rvx9QEkbVNnaJ5pWgIIIAAAvEn0N47KDr6L5i2xuS4/T+3EebZ6anyL6fMEa3eG2jTXIFzyvKDOifQvjkOAQQQQACBAymQtAHA22+/3SSlH7ECexrw08BeSUnJmNdCt+k+PUaDgHqOnktDAAEEEl3AVR24JC8j5o/iNQj4ToM8GoMgoOZAXGeCgK7R3zEH4IIIIIAAAh4F+gaHZb3189njbo8bd3cPyE+XrZdht8xGXz+hVqYWZXs8x9vGmsm5kptJmnRvPmxHAAEEEEhcgaQNAL7yyivWVIDvfe97UldX5/cVnDdvnuix+mbx5Zdf9ns8ByCAAAKJIJBihjpoTryygtjnxDvQQcCO3iGpb+1JhJeJe0QAAQSSQmDIjMxes1NHaAeeolxHc2vRjw4zatDezjl0qhw5faJ9k99l/V1Ymp/l9zgOQAABBBBAIBEFkjYAuGfPHuv1OvHEEwN+3VzHarVcGgIIIDBeBDQp+ozJeVJZHNwoiUg8v68g4CMxGAmo+aWaOwNPMB+JZ6YPBBBAAIGxAvoh+/rmrqALVP3u9XrZYM6zt0MqC+UzHwuuwFyeGfU3fVKuvRuWEUAAAQQQGFcCSRsA1Kq+obZwzg31mpyHAAIIRFugamKO1JTkmtHR0b6Ss39vQcAHzXTgWAQBN7d0S2efc+SI8w5ZQwABBBCItsAWMyK7rSe4n8XPr2mWZeafvZWaKu/fPGmW6Aj3QFu6qfg7qywvqHMC7ZvjEEAAAQQQiBeBpA0AnnzyydZr8NJLLwX8Wrz44ovWsSeddFLA53AgAgggkEgC5YVZ1pTgAxEEvOjo6jFUsQgCalGQdU1dMjBEUZAxLwAbEEAAgRgIaMXfHWZEdjBtQ3On/Pa1zY5TMtNSTNGP2aKj+QJt+vtuVmm+ZJmCITQEEEAAAQTGs0DSBgCvueYayc7OlptvvlnWrVvn9zXWY7QacG5urlUUxO8JHIAAAggkqEBJXqbMLc8XLRISy3b6/Ao5UEFADf5pUZARjQbSEEAAAQRiJhBKxd+2HlP047n1MuT2M/vy42dIdZDTeDX9RWFOesyelwshgAACCCBwoASSNgA4Z84ceeihhyz3o446Su644w7ZvXv3mNdBcwXeeeedcswxx1j7/vSnP4meS0MAAQTGs0BRTobMq8gXnRYVy+YrCPjoiu1RvZXOviHZ3Nod1WvQOQIIIIDAfoFQKv4OjYzInabir1b+tTdNJ3FMbYl9k9/l4tx0k/82x+9xHIAAAggggMB4EJhgEu4m5XAH1zTebdu2yfr1662KwJoIv6amRkpLS631pqYm2bx5s1X5V1/smTNnytSpU72+7nr+smXLvO5nx36BxsZGqaqqsjY0NDRIZWVwiZr398QSAghEU6B3YFhW7eiI+fTYp1bukN+9sWXMo11y7HRZUlc+ZnskN8yYnGuqIlMFMpKm9IUAAgi4C2jF35XbO4Iv+vFGvTy1cqeju7qKAvnXM+YFNXI9Kz1FFkwtlLTUpB0P4TBkBQEExrcA77/H9+sb6NMFniAj0B4T5DjN56cBO1fTOKj+27hxo/XPtd3+dcOGDaL/3GOm2o9us/dnP49lBBBAIFEFsjNS5aApBbJmZ2fQb9LCeebTzHRgbe5BwN++Vi+ZaamyePbkcLr3ee7mXd2iz12QxZQwn1DsRAABBEIU0L+bQ6n4+8r6ljHBv0m5GXLVJ2YFFfzTDBezy/IJ/oX4+nEaAggggEBiCiRtAPD4448nYJeY37PcNQIIxFhAE6NbQcAdndLVPxSzq2sQUNM7/eFN50jAu1/eKFkm0fuiGZOici/mfamsN/kA55uRIRpspCGAAAIIRFYglIq/9SZFw9JXNjtuRNNUfNsU/SjIDu4Dmxoz0js3iEIhjouyggACCCCAQIIKJG0A0FXRN0FfN24bAQQQiKlAupkiVWeNBOyQjt7YBQE1p5PmiPrze42jz6sBup+9sEEyTBDwsGnFo9sjuTAwtFdWm4CnFkOhMmQkZekLAQSSXaA5hIq/nX2Dcvsz62TATBu2t0uPrZHayXn2TX6XywoypTSfNA9+oTgAAQQQQGDcCZD0Yty9pDwQAgggEB0BrQo8t7xA8rNi+9nRuYdNlTMP3jcl2PVkw2Zo4E+fWyertre7NkX8q+Y//DCE/FQRvxE6RAABBMaJgFb83WTSLATTtDr7Xc9vkJaufsdpJ88rkxPmlDq2+VvJM6P+pgdZJdhfn+xHAAEEEEAgUQQIACbKK8V9IoAAAnEgsC8ImG+mTsVuaqzmV/38wmmib/bsbXB4r9z6zFrZ0Nxp3xzR5YGhERMEbJfuGE59jugD0BkCCCAQJwKhVPzVW//juw3y/jbnhz2zy/Lky0dXB/VkOl14ljkvRRMA0hBAAAEEEEhCAQKASfii88gIIIBAOAJaMXGeqbiYYwplxKppEFArAB83s8Rxyb7BEbn5qTWyxeSGilbTQKNWQu4wU9BoCCCAAALBC2jF37WmmJT+PA2mvbW5VR5dsd1xSpHJ93fVJ2YHVcBD6/7NKiWlgwOSFQQQQACBpBOI7TyuOOUdGRmRVatWyaZNm6Szs1OGh4f93ulFF13k9xgOQAABBMargOYEnFuRb6bgdpgcfc6cTNF65hTzDu5ri2ul34zKe7t+9+hluvuH5SdPrpEbzqyTKUXZo9sjuTBk3rSuNs86x+QELMrJiGTX9IUAAgiMewGtrt5j0ioE0xr39Mj/vrTRcUqq+T1w9cmzZaKp/BtMqyzOlsKc4AqFBNM/xyKAAAIIIJAIAkkdAOzp6ZGbbrpJli5dKq2trQG/XjoShQBgwFwciAAC41RAK+TqSEDNk6dTZWPRdAryN0+aKf9tpv6uaNw/JazD5JX68ROr5caz6mRylJK7a0XiNWYEy8zSPCnJy4zF43INBBBAIOEFNO/frq6BoJ5DR1zf+vTaMR8wXXRMtfVBTDCdFeemS2VxTjCncCwCCCCAAALjUiBppwB3dXXJ4sWL5ZZbbpFdu3bJXlNWMph/4/K7gYdCAAEEghTQCrl1JgiYkRa7nEo6+vDbp8y2KvTab3d394AVBNzTE9wbTXsf/pa1AvGG5i5pMlUsaQgggAACvgX0b+v6IIt+DJrpwj99dp00dzqLfiyePVlOccsF6/vqYqq4p8jMIKsE++uT/QgggAACCCSqQNKOANSRf++99571uh111FFy+eWXyyGHHCJFRUUmOXDSxkUT9fuY+0YAgQMokG1yAepIQJ0OHGx+p1BvW0cfXnvqHPmJGfW3sWV//r+mjn758eOr5XozErAgKzrTvTQIuMlcc8gMCZwapSnHobpwHgIIIBBPAvozOZipvxowXPrKJmu0tf05aifnyqXH1ojOwgm0aa2P2WX5QeUKDLRvjkMAAQQQQCARBZI2APjQQw9Zf0ScccYZ8sgjjxD0S8TvXu4ZAQTiRiAnI83kBNwXBBzWubIxaHrN7582T/798VXSsLtn9Irb2nrlZpMT8N8+Oc8UKoner7mtrT0mZ+xemTaJqWWj+CwggAACHwnoSD7N4xdM04IfL6/f5ThF8/1ds2SOGWke3Af0tSZdQ25m9H4HOG6SFQQQQAABBBJAILjfpAnwQIHe4rZt26xDv/WtbxH8CxSN4xBAAAEfAnnmjZYWBtE8fbFqeVlp8q+nz5XygizHJTXh/H89pfmjgks67+gkgBUNNm5q6bJSSARwOIcggAACSSPQuKc3qFHhb2/eLQ++0+DwyTRBPx3tXRxk8SUdnU2uVgclKwgggAACCEjSBgBLS0utl7+kpIRvAwQQQACBCAnotNvZZXkSwxigVZX3OjParyTPWRVybVOn3G7ySEW7QIlOcdO8gDp1jYYAAgggINLdPxRUrlT9IOXnL2xw0OlHSVr0afqkXMd2fyta9KNqYnQqwvu7NvsRQAABBBCIZ4GkDQAuXLjQel3Wrl0bz68P94YAAggknECRGakxy+RdCiJVU9jPqCM9rjujToqynXn/PtjWLj97fr3J1xfdKsVa4VIrBI/EaPpz2GB0gAACCERRoL6123woEtgFWrv65VZT2X3ATBm2t88vmiZHVE+0b/K7rDlptehHMLkC/XbKAQgggAACCIwTgaQNAH7729+2XsK77rqLURvj5JuZx0AAgfgR0JxNtdabsNjdU3lhlvzrGfNEpyLb27tb9shvXt0c9Z/1bT2DsmpHhwy5vYm13wvLCCCAwHgX0IBeR+9QQI+paRo0+Kc/P+3txDml8skFFfZNfpfTUidY1eHTTKV4GgIIIIAAAgiMFUja35DHHHOM3HLLLfL666/LBRdcIG1tbWN12IIAAgggELLA5PxMmVES3NStkC/20YlVE3Pk+yYnYHZ6qqOrF9a2yGPv73Bsi8ZKZ9+QFQTU5Pc0BBBAINkEdBT0FltRJl/PP2KGCOq03y2moJK91ZmCUpceOz2oUXw64nx2ab5kuf3st/fLMgIIIIAAAsku4BwmkWQa3/nOd6S2tla++tWvSlVVlZxyyikye/ZsycnxX9Hx+uuvTzItHhcBBBAIXqDUFOcYNm/y6nc53+AF31PgZ+jIw++eNkf+84k1jillD7y9VcoKMmVRzaTAOwvhyO7+YdEiJLPNNGgaAgggkEwCWhipfzCwD0AeND+TdYS2vWlBp2+fPFuCHcVXbaqxF+Y4U0DY+2UZAQQQQAABBESSOgDY3Nwsf/vb36S9vd3kbRqRRx55JODvCQKAAVNxIAIIJLlARWG2DJtRIQ27e2MmMbe8QL5x4kz56XPrHNfU0SaTcjNlZmmeY3ukV1pNTsBduf1UoYw0LP0hgEDcCuh03u0mABhIe2Ft85hR2bmZqfJdU/FXq7sH03S0uf6eoSGAAAIIIICAb4GknQLc2toqxx9/vNx3330yPDxs5YbSCo6B/vPNyl4EEEAAAbtAZXGOVBbH9g3awpqJcuHCafbbkMHhvXKbyTfV0tnv2B6NlXozCjDaFYijcd/0iQACCIQi0GCm/gZSB2nV9na555XNjkukmjm8OvKvoii43xP5JlgY61QTjhtnBQEEEEAAgQQSSNoA4E9+8hNZt26dFfA7//zz5fnnnxcNCmowUEcD+vuXQK8xt4oAAgjEhYDm55tmpmnFsp11cIVoMnl7a+8dlFufXiM9A4ElqbefG8yyBhu1EiYNAQQQGO8C+nNVq6H7azvae83I7PVWagj7sZd+vEYOmlJo3+R3OSMtxUq1kJJiEgDSEEAAAQQQQMCvQNIGAB999FErufCXvvQl+dOf/iQnnHCCFBcXB5Vw2K8uByCAAAIIOASmmtEd00tiFwScYEaVXPrx6TJ/SoHjPhr29Mr/LDNvQgMZruI4M7gVayqwqYhJQwABBMargM6e2RLAhx1d/UPmw5e1ol/t7UzzQc1Jc50f1Nj3e1rWmN+c8nzRICANAQQQQAABBAITSNrfmtu2bbOELr300sCkOAoBBBBAICICmqtpxuTYVQdOS0mRq83UMg0+2tuKxna59/V6ayS4fXukl5kKHGlR+kMAgXgSaDYpFbT4ka82ZGbX3GFysu5o73Mc9rHqYrnwSGeqBscBXlZmmGJPeZnB5Qr00hWbEUAAAQQQSBqBpA0AlpSUWC9yfj5VGpPmu50HRQCBuBEoM5Uea0tzzajr2NxSrnmjqMnlC9ySyz+3ukmeXLkzqjfBVOCo8tI5AggcQIGh4RFT4Ml3lXcdIXjva/Xy4fYOx51Wm7QQ3zTFmoKdwjulKEu08AcNAQQQQAABBIITSNoA4HHHHWdJrVy5MjgxjkYAAQQQiIhAaX6WVY03VkHAUhN0vGbJHElPdUYd/+/NLfLult0ReSZvnTAV2JsM2xFAIJEFNJ2Cfsjhq+mHLMvWNDsOKcpOl2vNhzJZ6amO7f5WinLSZZoJHNIQQAABBBBAIHiBpA0AXnPNNZKeni633Xab9PU5pyMEz8gZCCCAAAKhCJTkZe5L4u6MyYXSVUDnzC7Ll68vrnUcq29d73p+g2w2VXuj2ZgKHE1d+kYAgVgLaCGlpg7ff0Ov3NYu+iGLvemHMN8xwb9J5ud/MC07I1VmleaRrzsYNI5FAAEEEEDAJpC0AcDDDz9cli5dalUCXrJkifXV5sIiAggggECMBCbmZljJ3GNVyPHo2hL57BFVjqfrHxqxKgO3RrFgB1OBHeSsIIBAggvU7+oxOVS9P4ROD/7ta5vF/ZArT5gptSaHXzAtzQQN55gPcNJSk/atSzBcHIsAAggggIBHgaTNnusq/lFXVyevvvqq6NeDDz5YZs+eLTk5vqcWaFXJe+65xyMoGxFAAAEEghcoysmQueUFsrapM+qVefXuzjl0iuxs75WX1+8avdk9PYNy6zNr5cazDgp6WtpoJ34WrKnAuf2iIx9pCCCAQKIK6Icl7b2DPm//qQ93yna3oh+f+VilHDVjks/z3Hdqmggd+acjAGkIIIAAAgggELrABJOY1/2DudB7S6AzU0xVSA3kuZoy2Ndd292/uo4bHvZd7cz9PNadAo2NjVJVtW8ETkNDg1RWVjoPYA0BBJJSoKNvUNbu7JQhPzmlIoGjo1N+8uRqWb2j09Hd4dOK5JpT5gSdmN7RiY8Vnf52cGWRZKQxksUHE7sQQCBOBUZG9sryxjbpHxzxeodtPQPyL39aIb2D+/9enlGSK/9xznxJsf397bUD245pk3LGVHG37WYRAQQQQCAAAd5/B4CUBIck7QjAadOmBRTwS4LvAR4RAQQQiBuBgqx0MxIwX9bEIAioU8n+5eQ58sNHVspOWx6rf2xtkz+8tUW+fPT0qLi4pgJrPkIaAgggkGgC283oaV/BP32eB99pcAT/dNvFx0wPOvhXWpBJ8E/xaAgggAACCERAIGkDgPX19RHgowsEEEAAgUgL5JsgYF1FgRmZ1+G3umS4187LSpPvnjZHrn/kQ+nqHxrt7ilTtbLCVA1eclD56LZILjAVOJKa9IUAArES6B8alu1tvgt/bGjukpfWtThu6fhZJTIryA89Ck2lYB01SEMAAQQQQACByAgw/ygyjvSCAAIIIBBBgdzMNKmbUmCmye5P1RDB7h1dVRRmmym/syXNrQrJvW/Uyz+37nEcG8kVqgJHUpO+EEAgFgJbW3t85mkdMSl1fvdGveNWstJT5IKF0xzb/K1ovr/ZZVT89efEfgQQQAABBIIRIAAYjBbHIoAAAgjETCAnwwQBKwpjkitvrhlxePnxMxzPphly/+f59fLBtnbH9kituKYCR6o/+kEAAQSiKaBFP3Z1Dfi8xCumsJKOALS38w6rlGJT6CnQpnlSNRUEFX8DFeM4BBBAAAEEAhMgABiYE0chgAACCBwAAR0FcpAZCZhpRpBEux03a7Kcd/hUx2X6TJL7W55cIy+sbXZsj9SKNRXYVNOkIYAAAvEsoFN/NzQ7Cya532/PwJA88PZWx+aKwiw5fX7gqRR0IPYcE/zLSqfirwOSFQQQQAABBCIgEP13VBG4SbpAAAEEEEheAX0jqDkBYxEEPP/wSjm2dpIDe9gMBfzVy5vkjyapvVaCj3RjKnCkRekPAQQiKTBsqv6u29klA0O+f/797Z/bREcJ2tuXjqoOaiRfbWmeaB5YGgIIIIAAAghEXmDcBwBTU1NF/6WlOeuduLaH8tW9r8i/LPSIAAIIIGAXiFUQcMKECfK1xbWyqGai/fLW8sPLt8ldL2wwhUlGxuwLZwNTgcPR41wEEIi2wMaWLkeRJE/X297WK0+a4kn2dlhVkRw2rdi+yedy1cRsKcnL9HkMOxFAAAEEEEAgdIFxHwDU0Rquf3Ym17ZQv9r7YhkBBBBAIPoCsQoCpqemyLc+MUs+dciUMQ/1+sZW+ckTq6WzzznKZcyBQW5gKnCQYByOAAIxEWjY3SP688lX07+lf/9GvaM4SKqZy/ulo6t9nebYNzk/UyqLcxzbWEEAAQQQQACByAo4h8VFtu+46O2GG27weB/etns8mI0IIIAAAnEh4AoCrtrRIf0mP1+0WooZCXihqVpZat6U/ua1zWJmwI22NTs75fpHPpTvnTZXyk1+q0g1nQpcmJ0uGoCkIYAAAgdaoNXkJ23c0+v3Nv65tU1WNDqLJX1yQYVohfVAWkF2mtROzg3kUI5BAAEEEEAAgTAEJphP7Wxva8LoiVMRCEKgsbFRqqqqrDMaGhqksrIyiLM5FAEEkl2gb3BYoh0EdBkvb2iTO5etEy0IYm/5WWnynSVzZHZZvn1zWMuT8jIi2l9YN8PJCCCQtALd/UPy4fYOx6g+TxiaEuG7D70vOzv6RncX5aTL7Z85VLSIk7+mx8w3hZ6o+OtPiv0IIIBAeAK8/w7Pb7yczTCD8fJK8hwIIIBAEgm4RgLGojDIoSaP1Y1nHSQTczMcwp19Q3LT46vkzU2tju3hrOhUu53t+99Ih9MX5yKAAAKhCAwMjcjapk6/wT/t+8kPdjiCf7rt82b0dCDBv/TUCTLXVPwl+KdqNAQQQAABBKIvQAAw+sZcAQEEEEAgCgKxDAJWT8qV/zh7vlRPcuao0gIedy5bL4+u2B6xCsH1rd3S3hPZHINR4KdLBBAYhwIjWvHXBP8CSbGwu3tA/moq/9rbLFPF99iZJfZNHpdNikCZbYJ/+nOchgACCCCAAAKxESAAGBtnroIAAgggEAWBWAYBdQTgDWceJDoi0L098PZWuefVzQGNmHE/131dE3Osb+40U46H3XexjgACCERVYJPJRaqjmwNpD5qfe/1mtKCrmZiefPmY6aI5VP21WhMoLMhK93cY+xFAAAEEEEAgggIEACOISVcIIIAAArEXiGUQUKe1ad6/k+eVjnnQZWua5b+eXiM9A4G9eR7TgW2Djixca4qNDJn8WjQEEEAgFgI72nulpbM/oEvpKMFXNuxyHHvCnMmmmEeeY5unlaqJ2VKSl+lpF9sQQAABBBBAIIoCBACjiEvXCCCAAAKxEXAFATPSov9rLdXMXbv02Br5wqJpYx7ufVMJ80ePrRKtnhlu6xkYlg0tXRGbWhzu/XA+AgiMX4G2ngHZ0toT0AOOmGHK975e7zg220zl/dyRY38mOg4yK5NNZfXKYmcqBfdjWEcAAQQQQACB6AhE/51SdO6bXhFAAAEEEHAIaBDwIFNNMhZBwAlmituZB0+Rqz8xSzSRvb1t3d0jP3xkpWguv3Dbnu5BadjdG243nI8AAgh4Feg1Hzasb9YPG7we4tjx0toW2WymCtvb+R+rlMJs31N6C7LTZEZJrv00lhFAAAEEEEAghgIEAGOIzaUQQAABBKIrEMsgoD7JohmT5N8+WSf5WWmOB9tjinhoheBAp9M5TnZb2dYW+LQ8t1NZRQABBHwKaJqBNTs7TLqBwKJ/3f1D8uA7Wx19Ti3KliUHlTm2ua9o+oTZZfmSotU/aAgggAACCCBwQAQIAB4Qdi6KAAIIIBAtgVgHAfVNrVYIrijMcjxSd/+w/OLFDaJVNcNtm8xU4M4+KgOH68j5CCCwX2CvGfK3rqnLFBwKPNfoX/7RKB1uRUIuOrpa0lK8v6XQmiAzTdGP9FTvx+y/K5YQQAABBBBAIFoC/CaOliz9IoAAAggcMIFYBwHLCrLk3z81X+aW5zueeY0p5PHIiu2ObaGsaAxR36j3D1EZOBQ/zkEAgbEC9SbnX3tv4B8sNO7pkWc+bHJ0dER1sRxcObYyuv2gUpP3Ly/TOUravp9lBBBAAAEEEIiNAAHA2DhzFQQQQACBGAvsDwLGZspZnpkGfO2pc0x1ywzHkz70XoNsaO50bAtlZWBoRNbt7IrIiMJQrs85CCAwfgSaO/pkZ3tfwA+kowV/98YWGbYlCtT8p188qtpnH2nmmKqJFP3wicROBBBAAAEEYiSQtAHA3//+96L/Ojo6Aqbu6uqyztHzaAgggAAC8S+gQcA55QWilXtj0XIy0uSbJ84SnfLmajp672fPbxBNtB9u6zL5tzbt6gq3G85HAIEkFtBRf5vcinj443h3yx5Zua3dcdgnF0wRHf3sq1WZir9M/fUlxD4EEEAAAQRiJ5C0AcCLL75YLrnkEmlsbAxYu6mpSfS8Sy+9NOBzOBABBBBA4MAK6NSzOZp83haUi+YdzTHTgM89bKrjEs2d/Wb0TL1jW6grLZ0DooVBaAgggECwAjqSeH1TZ8AVf7V/TT3wf29ucVxqYm6GnH3oFMc295UcU/ijrCDTfTPrCCCAAAIIIHCABJI2ABiOt06DoCGAAAIIJI5AYU66lYTePjIvmnd/3mGVMsskvbe3l9a1yBsbW+2bQl5u2N0je7oHQj6fExFAIDkF9MODwQAr/qqQ/s1798ubRD/EsLfPL5wmOsLaV5tekmtGQ8fokxdfN8I+BBBAAAEEELAECAAG8Y0wPLxv+lZaGomMg2DjUAQQQCAuBCblZcr0SbkxuRedcvyNE2dKttsb5KWvbpJdXc430qHckH4Otb65S3oGhkI5nXMQQCAJBXT0n+b+C6Y9snz7mA8udET1MbWTfHYzyeRCLcxO93kMOxFAAAEEEEAgtgIEAIPwXrt2rXX0xIkTgziLQxFAAAEE4kWgvDBLKouzY3I7mhvrkmOnO67VY/IA/vyFDREp5DFskguuNVWGB4dHHNdgBQEEEPAksN2M/tOcpIG2d+t3yx/fbXAcrh9qfPX4GT5H9ukHINMo/OFwYwUBBBBAAIF4EEiaoWwvv/yyR+933nlHdu3a5XGfa2N/f79s3LhRbrvtNusPnkMPPdS1i68IIIAAAgkmoBUpNWjW1BH+SDx/j/7xmSWyvKFNXrdN/V1jgnaPrtgu57jlCfTXl6f9fYOmMrDJ51VXUeDzDbmnc9mGAALJI6Cj/5qCGP231aQZuMt8WGFvOpn3/500U6YW+f4QpcJ80OJverC9X5YRQAABBBBAIDYCSRMAPOGEE8a8OdK8JsEU9NDjNZfJ1772tdi8OlwFAQQQQCAqAjUmN5Xmwdod5Tx6+jvj0mNrrCDdrq79Ofseeq9R5k8ttPIShvuAHb1DUt/aI/pMNAQQQMCTwI72wEf/dfQNym1PrzXFP5yjiz+/aJocNq3YU/ej2zLTU/wGCEcPZgEBBBBAAAEEYiqQVFOANYDn+udSdq0H8rWyslJ+/vOfyznnnOM6na8IIIAAAgkooIE5LdJRkB39z8FyTRVizQdoz4U/bH4f3fXCeukb3JdbNlzCne19QY3uCfd6nI8AAokjEMyI5yEzOvqO59ZJi1uu0uNmlcgnF1T4fWjNs5oSq5Lrfu+GAxBAAAEEEEDALhD9dz72qx3A5RdeeGH06hrsO+mkk6zRfPfcc4/U1NSM7nNf0DeJWVlZUlFRIVVVVe67WUcAAQQQSFABfZOqyexX7eiQ7v7IBOK8UcwtL5BzD50qf/3nttFDdAryva/XyxWLa0e3hbOweVe3pJln0mInNAQQQMAlsKOtTzRnqL+mfx/rz6TVOzodh+qHJZd93HfePz1Bi35MzM1wnMsKAggggAACCMSPQNIEABcvXuxRfeHChVJXV+dxHxsRQAABBMa3QFpqimhwbuX2duk3+fSi2c47vFI+2NZuVe91XeeldS1yaFWRHDXDd0VN1/G+vpr37lbf+sEVb8J9SbEPgeQR0NF/OwPM/ffs6iZZtqbZgaM/S759ymzJSPM9aUhHOJOGwEHHCgIIIIAAAnEn4Pu3edzdbuRuaPPmzbJp0yaZPXt25DqlJwQQQACBhBPQN7ZaRCM91byDjWLTypg6FTjL5Miyt6WvbJJWt+l29v3BLFtBQFMUpK1nf77BYM7nWAQQGF8CgY7+W2k+nPidGf1nb/oz8RoT/CvO8T+qr9xUPc/OSLWfzjICCCCAAAIIxJmA811InN1cNG9Hg3/V1dWSlhb8IMgrr7wymrdG3wgggAACMRbQipVzTRBQg3TRbGXmTfIlx9Q4LtE9MCw/f3GDjAQwRc9xopcV7WatqTTc3jPo5Qg2I4BAMggEOvpPqwPfuWy9uP8I+rpJTzBjcp5fqoy0CVJZ7LsysN9OOAABBBBAAAEEoi6QtAFALeTx3nvvBQ18+eWXy9133x30eZyAAAIIIBDfAnmmWIfmBIxyDFA0mf7Rtc4pv5pz69H3t0cMyAoCmpGA7b0EASOGSkcIJJiAFgfyl/uvZ2BIbjUVf7v6hxxPd47JWXp0bYljm7eVquIc0XQKNAQQQAABBBCIb4Gk/W3d2dkpZ5xxhqxduzbgV+iyyy6TpUuXBnw8ByKAAAIIJJZAYU661JqE99FsmqPvK8fWSEmec1rdQ+82ysaWrohdWt/460jAzj6CgBFDpSMEEkRAq/n6y/2no45//sIG2dbW63iqI6qL5TNHVDq2eVvRD05KzchmGgIIIIAAAgjEv0DSBgBnzpwpLS0tcsopp0hDQ4PfV+riiy+W3/72t9ZxF1xwgd/jOQABBBBAIDEFSkwV3Wgns881b5q/ccJMU41+v9GwSeB31/MbpG8wchWJNQi4xgQB3Uf37L8qSwggMB4FdpjRf0PDJh+Aj/bHdxvkH1vbHEdUmam8V5qfTSn2H06OI5wr00tynBtYQwABBBBAAIG4FUjaAOCzzz4rU6dOlcbGRisI2NzsrHrmesX2mjdkX/rSl+T3v/+96PIXv/hF+cMf/uDazVcEEEAAgXEoUF6YJdMmRfeNreYc1Gl29qYjdtwT8dv3h7KsQYDVOzqk222KXyh9cQ4CCMS/QCCj/17dsEseXeFMO6Cj+b6zZE7AxTwm52dKflZ6/INwhwgggAACCCBgCSRtAFALgDzzzDMyadIkWb9+vZx22mnS0dHh+LYYGRmRL3zhC3LfffdZ27/85S/L7373O0lJSVo2hw8rCCCAwHgWmFqULXPL801uK9swvQg/8HmHT5WZblOOX1zXIm9uao3olVxBQM33RUMAgfEt4G/034bmLvnVyxsdCKlmxN+3TcXfQKfz6s/FaROj+yGJ4wZZQQABBBBAAIGwBZI6kjVv3jx54oknJDc3V1asWCFnnnmm9PX1WajDw8Ny4YUXyoMPPmitX3rppfKb3/zGTNeK3hvBsF9NOkAAAQQQiKhAcW6GzJ9SKDkZqRHt19VZmvlA6ZsnzpSsdOev4/99aaNsaO50HRaRr4MfjQTsNVWHaQggMD4F/I3+2909IP/97FrRnwf2dvGx06XOjEoOtOkHJBlpzp9bgZ7LcQgggAACCCBwYASS/jf3kUceKQ8//LBkZGTIa6+9Jp/+9Kelt7dXPvvZz8qf//xn61X56le/ahX/iHbwT6ch//3vf5frr79eTj/9dCkpKbECjnpdzUEYqXbjjTeO9qt9+/r34osvRuqy9IMAAggkpEC2Cf7Nn1ook9yKdkTqYcpMAv2Lj6lxdNc/NCI3P7VGGnb3OLaHuzIwtFdWmenAkcwzGO49cT4CCEROQNMIeMv9N2B+rtxugn9tPc7CQEvqyuTkeWUB34T+TKwwaRJoCCCAAAIIIJBYAmmJdbvRuduTTjpJHnjgAfnMZz4jTz31lNTU1FgFQvRqV1xxhfziF7+IzoXdei0rC/yPL7dTWUUAAQQQiKJAasoEmV2WL40ZPdK4p9fkhI3sxY6fVSIfbm+XV9bvGu24u39Y/vPJ1fKjTx0kk/Mj92ZbgwAaBNTRPlnp0RnZOPoQLCCAQMwEtOjPTlP8w1tb+somU2m827Fbfw586ehqxzZ/K9NNftRofyju7x7YjwACCCCAAALBCxAA/MjsnHPOkV//+tfyla98RVwFQb7xjW/Iz372s+BVI3DGtGnTZO7cuVaewgh057WLDz74wOs+3aHBUBoCCCCAwD6ByuIc0UT5600OLW+jbEKx0jfTlx83Qzp6B2VFY/toF3vMSJ2fPLFGbjirTopyMka3h7vQPzhiFQapm1IgmWkEAcP15HwE4kFAR/+5T+113dcH28wHDKbwh72VFWTK1SfPEk1FEGibaNIiRPJnUaDX5TgEEEAAAQQQCF9g3AcAt27dGrCSjgT81re+JXfeeaecf/75cu2114q38zVAF+mmU391SrL+09GA9fX1UQ/AzZ8/P9KPQX8IIIDAuBbQN78LzJTgtTs7pSeC+fTSUlOsJPw/eWK1rGvqGjXUN/U6Hfj6M+tMLsLI/drus4KAndZIQHJ5jXKzgEBCCujovx1tvR7vfcQMWb7/rS2Ofdlm9O81p8wJqoqvGQgt1VGuju64SVYQQAABBBBAIKICkXsnEdHbilxnoYxg05EYf/nLX6x/nu5E9w8NRb6S4o9+9CNPl2MbAggggECcCejUWc0LuKmlS3Z1DUTs7nQ03rWnzpV///sqR/6/La09cuvTa+X7p8+N6Ig9LQiyWqcDm5GA6SYASUMAgcQUaPIx+u/1ja1Sb36G2NsXFk2TqiCr+OrxpA2wK7KMAAIIIIBAYgmM+7/295pPPaPxL7FeZu4WAQQQQCDSApoXcJbJC6gjYsznQhFrOsX4BybQV5qf6ehzjRlxeOdz62VoZMSxPdwVHcWooxn1dyUNAQQST8Aa/dfuefTf4PCI/PEd52wYreB7wpzSoB50cn6GTDHn0RBAAAEEEEAgcQXG/QjA3/72t4n76nDnCCCAAAJxL6BvinPN1Nz1zZ1e828F+xDFZprxv54xT2589ENpM3kBXe2fDW3yvy9tkitPqJWUCEYdO/uGZJuZPqg5DmkIIJBYAjr6Tyt8e2rPfNg0ZpTyBQurRD/ACLTlZ6XJjJK8QA/nOAQQQAABBBCIU4FxHwD88pe/HKf08XFbS5YskeXLl0tbW5sUFRVJXV2dnHbaafK1r31NiouL4+MmuQsEEEAgzgUKc9KtKcHrmjpFq/dGopUVZMkPTBDw3x/7ULptuQZfM4n8dZTgl03lzkhW4tTqxhp4zDV90xBAIDEERjT3n5fRf139Q/K35Y2OB5lbni8fmxb433eaH3RWWZ6kBBEwdFyQFQQQQAABBBCIG4FxPwU4bqTj9EaeffZZaWlpkcHBQevrSy+9JD/4wQ9kxowZ8sgjj4R8142NjeLr344dO0LumxMRQACBeBSw8gJOKRSdKhepNs3k3PruaXMlwy0/39Mf7pS//nNbpC5j9aMzgDeY6sYaUKAhgEBiCDR1eh/998jybWM+kNDcf4F+cKAxv9km+Eel8MT4XuAuEUAAAQQQ8CfAx/z+hMbp/gULFsg555wjCxculClTplgBwLVr1wpG/p4AAEAASURBVMp9990nzzzzjDUi8NOf/rQ89thjcvrppwetUFVVFfQ5nIAAAggkuoCOkplZmm+m13XLzva+iDzObJNn8NunzJbbTBGQYVuevofea7RGAp56UHlErqOdaD7Ahj09Jq9hbsT6pCMEEIiOgAbrt3up/NvS2S/6QYG9LaqZaP18sm/ztTxjcl5QVYJ99cU+BBBAAAEEEDjwAgQAD/xrEPM7uPrqq+XGG28cc91FixbJRRddJHfffbdcccUVMjw8LJdddpls3LhRsrKyxhzPBgQQQAABzwLTTWGQbjP9TnPrRaIdWlUkV55YK3c9v0Hs4/Pufb3eCgIeO7MkEpex+thhApdFZipwYXZ6xPqkIwQQiLxAswnyecv99+d3Gxw5SVNNztALjpwW8E1ooZDJboWIAj6ZAxFAAAEEEEAgLgWSPgA4NDQkjz/+uLzyyiuyadMm6ezstAJfvl4tnTqxbNkyX4fE9T7N9eeraf6/d955R+655x7Zvn27/OUvf5EvfOELvk4Zs6+hoWHMNvsGnQKsow9pCCCAwHgU0N8Tmjfrg8Z2x5vwcJ71mNoSK6j4m9fqHd388sWNkpORKocFkdfL0YHbig4y3NjSJQdPLZQ0t6nHboeyigACB0hAR/9p4R5PbfOubnnV5Aq1t5PryqS8MLAPc4tz06VqIhV/7X4sI4AAAgggMB4EkjoAqPnuLr74Ytm6devoa7nXNr1qdONHC/qGTvcHmjvF/fxEWtcgoAYAtalTsAHAysrKRHpc7hUBBBCIuIDmzZpZmidrdnaa3x2R6f6UunJrVOGfzfRfV9NpwXc8t94UDJkrc8sLXJvD+to/OCL1rT3W/YfVEScjgEBUBFq6dPTfiMe+H3h7q2OkcHZ6qpx32FSPx7pvzDYfJsw0U3+T4W9d92dnHQEEEEAAgfEukLQBQK18q9VuBwYGrKCeTnGdNWuWVQk3JYXaKFoN2NW2bYtsonlXv3xFAAEExruATqXVqXRaYTdS7VzzRl6rez65cn9+r4HhEbnV5Ai8/sy6iOXv0xxiE3MzrH+Runf6QQCB8AV09J+3nynvN7bJB9vaHRf51CFTpCCAKf1pqRPMhwj5jPx16LGCAAIIIIDA+BFI2gCg5sDr7++XzMxMuf322+WSSy4hz53t+5pPfm0YLCKAAAJhCFQWZ1sBu7aewTB62X+q/nz+4lHV0mXyC75im+anBTz+88k1cuNZBwU81W9/r56XNpmpwHmZRZKRxgdjnoXYikDsBba393oc/TdiRgPf/9b+WS16Z8U56XL6Av+FgsyPFZltChhpNXMaAggggAACCIxPgaT9i/7VV1+1pjdcd9118vWvf53gn9v396pVq0a3aJVgGgIIIIBAaAIasNOpwJEMoqWYPi9fPEMOd8v71947KP/x+Cpp6ohMBeLB4b2i+cRoCCAQHwL9Q8Om8q/n/79fMx8IbNnd47jRzxxRJZqOwF+rNoWLCk2wkIYAAggggAAC41cgaQOAfX37/njSacC0sQJaCdjVFi9e7FrkKwIIIIBACALpppjGbFMUREfZRKqlmXQVV31iljVlz97n7u4B+Y+/Ry4IqP01d3oOONivyzICCERfYKvJzTlspgC7N80H+Md3nAXYdPTx4lmT3Q8ds15akCkVhRT9GAPDBgQQQAABBMaZQNIGAKdPn269lIODkZmSFS/fF/fee681slFHnOg0Z/f2wQcfyIYNG9w3O9Z/9atfydKlS61t5eXlcu655zr2s4IAAgggELxAfla6TJ+UG/yJPs7QUYXXnjpHakqc/baaoN1NZiRgc4RGAtbv6pG+wWEfd8IuBBCItkBH36Ds6hrweJmnP9wp+v+9vV24cJqkpPj+1CE/K01qIvxzyX4PLCOAAAIIIIBA/AgkbQ7Ac845R1avXi0vv/yyHH300XHxiui0ZHtwbteuXaP3pds1uGdvWsE42Pbee+/JZZddJieeeKKcfvrpsmDBApk0aZIMDQ3JmjVr5L777pNnnnnG6jY1NVU0GJib63xjGew1OR4BBBBAYJ9AeWGWqeLr/U18KE45GWnyr6fPk5ueWCVbzOggV9NAgU4H1sIgk/OzXJtD+qojjjaafIB1FQVUBw1JkJMQCE9gr8nvV+9lOr7+THl4ubNgm/6/elhVkc+L6gcIs8vy/QYJfXbCTgQQQAABBBBIGIGkDQBeddVVVkDttttuk8997nPiGhF4IF85HXX3u9/9zuMtvPbaa6L/7C2UAKCePzw8LM8995z1z96ffVmDgvfcc4+cddZZ9s0sI4AAAgiEKTBjcp50D7RLrynaEamWZ0bxXHfGPPnxE6vHBgH/vlp+aAUBM8O6XEfvkOxo75MppqoxDQEEYivQbKpyd/d7/pnx8PLtokWA7O3zi6b5DNanmpGBWvE3krlJ7ddnGQEEEEAAAQTiTyBppwBPnjxZnnjiCcnOzpZFixbJr3/9a2lvb4+/VyjCd3TGGWdYgT0dBfixj31MKisrLYOsrCzRYh86KvDOO++UTZs2ydlnnx3hq9MdAggggIC+8dZ8gPo1kk2nGGsQsHpijqPblq5+KyfgLvM13NZgCgz0DAyF2w3nI4BAEAJDwyOi/+95ajrN/xkz/dfejq6dJLXmgwZfrXZyruRmJu04AF807EMAAQQQQGDcCkwwUwrGZhIet4879sHq6+utAKBOt9W8eSUlJZKT43zz5H6WHrdx40b3zawHIdDY2ChVVVXWGQ0NDVYgMojTORQBBBBIeIEWM6JnQ3NXxJ9D84Td9PjqMQGD0vxMazrwpLzwRgLmZqbKgqmFPkcXRfyh6BCBJBbQqb86+tZTu+v59fLaxtbRXfrBwn9/5hApK/A+7V+Lg1S5fVAw2gELCCCAAALjUoD33+PyZQ36oZL6o7+//OUv8pWvfEU6OztF46D6r7m52S+iBgBpCCCAAAIIhCMw2QTkNHdXU0f4I/Ps91FgRgL+mxkJqEVAGvb0ju7SKYT/bqoDa07AcIKAOg2x0fRLAGGUlgUEoiagI253einms9kEBu3BP72JJXVlPoN/hdnp/L8btVeLjhFAAAEEEIhvgaQNAL7xxhtywQUXWPnw9CWqrq6Wgw8+WIqKikwy5KSdGR3f363cHQIIIDDOBLQqsAbUuvojO622wLzJv+6TdVYQUIN1rqZBQC0M8kOzL5wg4La2XinKSReddkxDAIHoCWgFbk9zdfRD6/ve2uK4cE5Gqpx72FTHNvuKZh1wrxhu388yAggggAACCIxvgaQNAN50001W8K+wsNCqfKu58WgIIIAAAgjEUiDFvCOfZfIBrtzWLoPDkc3IoSN9/s0E+v7DjPrTgJ2r6YhDnSKshUEm5ma4Ngf1VQMSOn354MqiiOcyDOpGOBiBcSywu3tA2nsHPT7hisZ2+XB7h2Pf2YdM8RmU1wI+2SZISEMAAQQQQACB5BRI2qFu7777rpW/6Ec/+pEQ/EvOb36eGgEEEIgHgaz0VL8J+0O9z31BwHky1a1yr04p1MCgBhhCbX2DI6bicHeop3MeAgj4EBgZ2ev1/y/dd//bWx1nazD/tPkVjm32lcz0lDE/B+z7WUYAAQQQQACB8S+QtAHAnp591dQ+/vGPj/9XmSdEAAEEEIhrgWLz5l0T80ejFeVkmJGA82RKkbMogAYBNU9gOEFAHU3Y1hN6EDEaz0ufCIwHgR3m/08Nsntqr2xoGVPk57NHVElGmvc/62tMugEdcUxDAAEEEEAAgeQV8P6Xwjg3qampsZ7QFQgc54/L4yGAAAIIxLmABgB1xF40mgYBNe+fexBQK4v+2AQB94QRxNNCBDoiiYYAApER6B8alm223J32XgeGRuRP7zbaN1lFPY6bWeLYZl/R0YH6IQMNAQQQQAABBJJbIGkDgOedd55V9ffpp59O7u8Anh4BBBBAIC4EtMK85gOMVo6ufSMB66Si0DkScLsJAupIwFBH8ukope3t+3MMxgUmN4FAAgs07O6RYS9B9SdX7hgzavfzC6d5Hd2Xakb9VU/KSWANbh0BBBBAAAEEIiWQtAHAa665RmbNmiV33HGHaD5AGgIIIIAAAgdaID01ReoqCiQ3MzqJ+out6cAegoBtJiegCQJ2eCk44M9FRyv1DQ77O4z9CCDgR6Czb1BaOj1Pq+8w+x5Zvt3Rw/wpBXJIZaFjm31lqhlZrHlGaQgggAACCCCAQNIGAPPz82XZsmUyf/58Of744+W6666T999/X/r6+viuQAABBBBA4IAJaB4vDQLmZ6VF5R50OqBWBy4vcBsJaIKAP39hQ0jTeXWw0pbWfbl1o3LTdIpAkgjU7/L+/9HD/9wmvW6B9s8vqraK2nniyTEVf6e4jfj1dBzbEEAAAQQQQCA5BJI2AJiamirV1dXy9ttvW0G/m2++WQ477DDJzc0V3efrX1padN6UJce3HE+JAAIIIOBPIM2MBJxngoAF2dH5faNBwB+eOTYI+P62dnl4+TZ/t+dxvxYT2RNGVWGPnbIRgSQSaDaFP7r6hzw+cZPZ98yqJse+Y03ev5qSXMc2+8p0s09TC9AQQAABBBBAAAEVSNoA4N69e60cgPpVm309kGXrJP6DAAIIIIBAlAQ0d9e88gKTvD86hUH2jQScJ8U5zv4feq9RVppAYCitvpWCIKG4cQ4CQ8Mj0rDH++i/P77b4MgLmGZ+PnzuiEqvcJPzM6NWVMjrRdmBAAIIIIAAAnEtEJ2hBXH9yPtu7oYbbkiAu+QWEUAAAQSSWSDFvMmfU5YvG5q7ZFeX57xg4fhMysuU/3fSLCv/30efh4l+LHaXmQr8n+ctMMHB4CqHakGQbW29VlXScO6LcxFINgH9/2ZgaN+H0u7PvrGlS97Y2OrYvOSgcpmc75zG7zogLXWCTJtI4Q+XB18RQAABBBBAYJ8AAUC+ExBAAAEEEIhjAZ3CN7M0z1T57Jbmjv6I36lONf7cEVXy4DsNo323m2IgP3t+vVx3Rp3oSMRg2nYTyNDRRxQeCEaNY5NZoHdgWHaYatyems5Kuf+trY5duSa337mHTnVss69o8E9zidIQQAABBBBAAAG7AH8d2DVYRgABBBBAIA4FNAhYOzlPKqKU0P+sQ6bIoVVFjidfvaNTdDpwsE0LguhUYBoCCAQmoP+/uEbgup+xvKFNVu3ocGw+2wT/8rwUCdLiQaUmAE9DAAEEEEAAAQTcBQgAuouwjgACCCCAQJwKaFL/yuLsiN9digkwXnlCrUwyxUHsTQuCLG/YY98U0PKe7kEKggQkxUHJLqCFc9p6Bj0yjJho+v1vO0f/leRlyKlm+q+npvU+KPzhSYZtCCCAAAIIIKACBAD5PkAAAQQQQCCBBKrM9L7qSZHP75WflS5XfWKWpGoUwdZ+/sJGae0KfurxZgqC2BRZRGCsgAb4fI2WfWl9izTu6XWc+FkzXd/b9N6ygizJy0za7D4OJ1YQQAABBBBAYKwAAcCxJmxBAAEEEEAgrgWmFGXLjMm54harC/ueZ5mCI59fNM3RT1f/kPyPyQc4NDLi2O5vpf+jgiD+jmM/AskqsKOjT7RwjqfWPzQsfzaVf+2t2gT/j51ZYt80upyRNkGqojA6ePQCLCCAAAIIIIBAwgsQAEz4l5AHQAABBBBIRgEd7aN5ASMdBDx9frkcUV3sIF3X1CV/tBUJcez0saIFQfoGh30cwS4EklNAA3zb3Eb32SWeXLlT9rhNDdbgvE7X99SmTcyVtFT+rPdkwzYEEEAAAQQQ2CfAXwp8JyCAAAIIIJCgAlptd7YZtRdkoV6fT6sFR65YXDumkMDf398h79bv9nmu+04KgriLsI7APoGtrT0yrP+DeGgdpgr3o8u3O/YsmFooB1c6C/W4DijMTrcqb7vW+YoAAggggAACCHgSIADoSYVtCCCAAAIIJIjARFO4Y255gaRGMAqYa/KIaT7ANLc+//eljdJspi0G07QgyG5T6ICGAAL7BNpNgG9Xl/f/J/72z23Saxs5q2P+LlzonJrvstT/RWtMcSAaAggggAACCCDgT4AAoD8h9iOAAAIIIBDnAoU56TK3Ij+iQcAZZnrxl46udjx598Cw3LlsvQwOe85b5jjYtqKFDrTgAQ2BZBfYu9cU/tjV7ZVhZ3ufPLuqybH/4ybvn7cgn+YDzc5IdRzPCgIIIIAAAggg4EmAAKAnFbYhgAACCCCQYAIFporvPBMETEv1nCMslMc5ZV6ZHD1jkuPUTSZ4cd9bWx3b/K1QEMSfEPuTRWCnGUHbYwLp3tof390qwyZI6Go6CvczpvKvp5aZniJTTQCQhgACCCCAAAIIBCJAADAQJY5BAAEEEEAgAQTyrSBgQcSCgJoP8KvHzZCKwizH0z/94U55c1OrY5u/FQqC+BNi/3gXGBgakUYfhT82NHeZ/6+ceTZPM0V5NNenp1YzKVdS3KbpezqObQgggAACCCCAgAoQAOT7AAEEEEAAgXEkkGfy982rKJD0CI0E1OmFmg/Qvb9fvbxJdrT3BiynM4A3+5j6GHBHHIhAggps3d0tQ8P7R/fZH0OnBt//9hb7JsnNTJWzD53q2OZa0dyfxeYfDQEEEEAAAQQQCFSAAGCgUhyHAAIIIIBAgghEOghYbUYaXXJMjePptUjBnc+tFx3VFGhr6xmU1q7+QA/nOATGjUBH36C0dHov/PHPrW2yeken43nPMcE//X/Zvemgv+pJOe6bWUcAAQQQQAABBHwKEAD0ycNOBBBAAAEEElNAK/nWTYncSMAT5kyW42aVODC27O6R379R79jmb0XPGaYgiD8m9o8jAX+FP/T/h/vf3up44pK8DFlSV+7Y5lopK8iSrHQKf7g8+IoAAggggAACgQkQAAzMiaMQQAABBBBIOIGcjH1BwIy08AuDaD7AS4+tGVN0YNmaZnllfUvANlZBEB950ALuiAMRSBABLfzR3e+98MdL61pkW5tzOv1nTeGPjLSxf6anmuF/WvmXhgACCCCAAAIIBCsw9i+LYHvgeAQQQAABBBCIWwErCFhRaIIJ4QcBddTR1SfPkky3wMQ9r24OKh+g5g7s9VEJNW4xuTEEghTwV/ijz0ylf+i9BkevOr332JnO0bauA8rN6D9PgUHXfr4igAACCCCAAALeBAgAepNhOwIIIIAAAuNEQAt51FlBwPB/7VcW58hXPu7MB9hv8gDe/dImGQlwaq8eVt/aPU50eQwEvAtsNVPevRX+0LOeXLlT9pjcmPb2hUXVkmJG3Lo3Hf1XUeSsyO1+DOsIIIAAAggggIA3gfDfCXjrme0IIIAAAgggEDcCGgQ8yOQEjMTooeNmTZYT55Q6nm1tU6c89eFOxzZfK1oQpMlMjaQhMF4F9hX+8F70pr13UB5bsd3x+AdXFsqCqYWOba6VisIsU42bP91dHnxFAAEEEEAAgeAE+CsiOC+ORgABBBBAIGEFdAqvBgEz08P/9X/R0dVSmp/psPjjOw1BTQXevKtbNAhCQ2C8Cfgr/KHP+9d/NIpW03Y1HfP3+YXTXKuOr2mpZvSfCQDSEEAAAQQQQACBUAXCfwcQ6pU5DwEEEEAAAQRiLqBBwLqK8IOA2s/Xjp/huP+B4eCmAu81U4HXm5GDmgeNhsB4Emjq6PdZ+GNne58sW93seOSPmyrb1ZNyHdtcKxr8S2P0n4uDrwgggAACCCAQggABwBDQOAUBBBBAAIFEFnAFAbPCHAlYN6VQltSVOSiCnQo8OLxX1pkg4HCA+QMdF2MFgTgUGDSB8IY9PT7v7MF3tsqwRsA/aulmhJ9W/vXUdF9FIZV/PdmwDQEEEEAAAQQCFyAAGLgVRyKAAAIIIDBuBKwgoJkOHG4Q8EIzZTHcqcDd/cOysaVr3NjyIMkt4K/wx4bmTnlr824H0mkHlUtJnnNKveuAKUXZogVAaAgggAACCCCAQDgCBADD0eNcBBBAAAEEElggM81MBzZBQC0QEmqzpgIvrnWcHuxUYD25tWtAGkzFVBoCiSzQ2TcozWb6r7emlbLvfb3esTs3M1U+dehUxzbXSkbaBCkvIPefy4OvCCCAAAIIIBC6AAHA0O04EwEEEEAAgYQX0CDgvIp8Ux049BFGmlMw3KnACtm4p9cEAr0HTxIemwcY1wL7Cn/4DmI/t6bJjHbtdjice2il5GWmOba5VqYW5UgKo/9cHHxFAAEEEEAAgTAECACGgcepCCCAAAIIjAcBDQLOLsuXcOIMkZgKrJYaHOnuHxoPrDxDkgk0d/ZLl4/v3T09A/Lg2w0OlSmmuMeSg5x5NF0HaLVu9+n1rn18RQABBBBAAAEEghUgABisGMcjgAACCCAwDgXys9KltjQv5CeL1FRgLQaihUS0kAINgUQRsAp/+JnC/oc3tkivW8XrSz9eI+leqvtWmtx/jP5LlO8A7hMBBBBAAIH4FyAAGP+vEXeIAAIIIIBATAS0CEFlcejVRnUq8KmmmIG9BVsVWM/tHxyxKgPrlEoaAokgoIU/tKK1t7aioU3e2NTq2H3crBI5yFTS9tS0OM/kfM9FQTwdzzYEEEAAAQQQQMCfAAFAf0LsRwABBBBAIIkEqibmmGqkGSE/8QVHVo2ZtvjgO1tlR3tvUH129A7J5l3OXGlBdcDBCMRIQKf9tpjpv97awNCI/Oa1zY7dmvPvi4uqHdvsK1NNIH7ChNDzctr7YhkBBBBAAAEEEFABAoB8HyCAAAIIIICAQ6B2cp7XogSOAz2seJoKrCOj7n5pk2gF1GBak6mm2tTRF8wpHItATAV0lOpmk7fS12DVv/2zUTQ/oL19fuE0KchOt28aXdaq3JPNaFwaAggggAACCCAQSQECgJHUpC8EEEAAAQTGgYDmHZtTrpWBQ/szIVJTgZVSRwG29w6OA1UeYTwKtJiq1b4KfzTu6ZHH3t/hePQ5puDO4jmTHdvsKzoNn9F/dhGWEUAAAQQQQCASAqH9ZR+JK9MHAggggAACCMStgAb/5pogYGqIpYEjNRVYR1atN0VB+tyKJ8QtHDeWVAK+pv6OmG/ee17dLFrYxtVSzbTer5jCHylepvfmZqaaKfiM/nN58RUBBBBAAAEEIidAADBylvSEAAIIIIDAuBLINXnKZoZYGTiSU4F1CvE6EwS0B1LGFTQPk5ACWvm3s2/I672/vK5F1uzsdOz/5MEVonk2vbXKYu/7vJ3DdgQQQAABBBBAIBABAoCBKHEMAggggAACSSowMTdDpk0KLSgRyanA3f3DsrGlK0lfBR47HgX2dA94zf3X0Tco97211XHbpaaq73mHT3Vss69oYRD9/42GAAIIIIAAAghEQ4AAYDRU6RMBBBBAAIFxJDC1KFsm54cWmNCpwGUFzimNoVQFVs7WrgFp2N0zjmR5lEQW2N0z4PX27zfBP/fcgJccO10y01K9nlM1MdvrPnYggAACCCCAAALhChAADFeQ8xFAAAEEEEgCgRkleZKflRb0k1pTgY+vdZwXalVg7aRxT68JBDorqjo6ZwWBGAgMmem/7T2ei9Os2tEhL5npv/a2qGaiHFpVbN/kWNb/t4pyQguyOzpiBQEEEEAAAQQQ8CJAANALDJsRQAABBBBAYL+AqzJwZnrwfzrMqyiQUw8q39+ZWVprcvo99eFOx7ZAVza2dEv/0HCgh3McAhEXaDOVqW21PUb717yA97y6aXRdF7LTU+Wio6c7trmv+MoL6H4s6wgggAACCCCAQCgCwf8VH8pVOAcBBBBAAAEEEl4gPTX0ysCRnAqsxUAadvcmvCcPkLgCu03+P0/tsRXbZXtbn2PXZ4+o8pnbrzA7XfQfDQEEEEAAAQQQiKYAAcBo6tI3AggggAAC40wgJyNNZpnKwBMmBPdg3qYC/+LFjTIwNBJcZ+bols7+MTnWgu6EExAIQWDEBKDbPEz/3dneJw8v3+bocUZJriypK3Nsc1+pJPefOwnrCCCAAAIIIBAFAQKAUUClSwQQQAABBMazQLGpVFodQmVgnQp8mttU4A3NXfLT59aJ5lQLtm1p7Q72FI5HIGyBdjP9V0eh2tvevXvlN69tFs1v6WoaJL/suBmi0+e9taKcdCnIYvSfNx+2I4AAAggggEDkBAgARs6SnhBAAAEEEEgagYrCbCl1q+4byMN/zkNV4OUNbXLXCxvGBFX89dfROyTepmL6O5f9CIQq0Oph+u8bm1rlg23tji4172WNGQHoq5H7z5cO+xBAAAEEEEAgkgIEACOpSV8IIIAAAggkkYBObyzIDq4ysE4F/vbJsyU3I9Uh9dbm3fLrVzaZwgr7R1A5DvCyoqMAdfQVDYFYCOj3WluPM/9fd/+Q/P6NLY7LTzSjZD/7sSrHNvcVPSYvM7j/f9z7YB0BBBBAAAEEEAhUgABgoFIchwACCCCAAAIOgQlmjuOs0nzJSPM+xdFxwkcr1ZNy5funz5XMNOefIS+ta5E/mEBKMAG9vsER2dnhLLrg6ZpsQyASAjrq1D7NV/t88J0G0WnB9vZlU/U32y3Ibd+vs4KnTcyxb2IZAQQQQAABBBCIqoDzL++oXorOEUAAAQQQQGC8CWSYIN7MyflBFwWZaQKH1546R9JTncHDpz7cKX96tzEopsY9vSYoE3wOwaAuwsEIGIHdbqP/NjR3yrLVTQ6bw6cVyZHTix3b3FemFmf7DBC6H886AggggAACCCAQrgABwHAFOR8BBBBAAIEkFyg0hQymFmUHrXDQlEK5+hOzJdWtpLBWUn3UrZqqr86HTOGFbSYISEMg2gL2nJNaCGTpK5vFPgFdR7VefEyNCYg7A9v2+8oxIwND+f/F3gfLCCCAAAIIIIBAsAIEAIMV43gEEEAAAQQQGCNQaUY0BZsPUDs5vLpYvnFi7ZgRhA+YaZXPrNo55jreNug04L7BYW+72Y5A2AKdfYMyMLR/pOmTK3fIlt09jn7PO7xSJudnOrbZVzQuWFua5zNAaD+eZQQQQAABBBBAIFICBAAjJUk/CCCAAAIIJLGAjniaaQIb7lN6AyE5urZEvnrcjDGH/va1enllfcuY7Z42aB2QLa3OYIyn49iGQKgCe7r35/lr7eqXh95zTlXXir5nLCj32X1FYRaFP3wKsRMBBBBAAAEEoiVAADBasvSLAAIIIIBAkglkpqVaQcBQHvvEOaXypaOqx5z6y5c2ytumQnAgTadnuhdjCOQ8jkEgEIHW7v7RwzRXZb9tNKDuuOzjNZKW4v1P66z0FKkspvDHKCILCCCAAAIIIBBTAe9/pcT0NrgYAggggAACCIwHgaKcDBPkCD4foD77GQsq5DMfq3Qw6Mi+/3l+vaxoaHNs97aypbU7qCrC3vphOwJ2gZ6BITPFfN/0X61S/eamVvtu0QD27LJ8xzb3lRkleZKq5X9pCCCAAAIIIIDAARAgAHgA0LkkAggggAAC41lAA4D5WWkhPeK5h02VMw+ucJyrxRZuf3adrNnR4djuaaW7f1hazPRMGgKRFLAX/9jY0i27ugYc3Z96UJlj3X2ltCBTtFgODQEEEEAAAQQQOFACBAAPlDzXRQABBBBAYJwKaD7AWWWh5QPUcz+/cJqcPK/UoTMwPCL/9fRa2djS5djuaaVhd69o0JCGQKQE7AHAtzY7R/+VF2TJNJP/z1vLSJsg1T72ezuP7QgggAACCCCAQCQFCABGUpO+EEAAAQQQQMASCCcfoAYBLzm2Ro6dWeLQ7DVVfm9+co00uFVedRxkVrRS6/a2XvfNrCMQkoBWl9aRpdo8Tf89asYkn1V9p0/KlbRU/uQOCZ+TEEAAAQQQQCBiAvw1EjFKOkIAAQQQQAABu4DmA5xaFFo+wBQTBLxi8Qw5orrY3qV09Q/JT55cLU0dfY7t7is72vtMkYZ9QRv3fawjEIyAffSfp+m/R82Y6LW7ibkZMikv0+t+diCAAAIIIIAAArESIAAYK2mugwACCCCAQBIKVE0MPR+gVlT9fyfNkvlTCx1ybT2DctPjq6TVR64/nQKsU4FpCIQrYA8Auhf/qCj0Pv03LXWC1JTkhnt5zkcAAQQQQAABBCIiQAAwIox0ggACCCCAAAKeBMLJB6j9ZaSlyDWnzDYVVvMc3WsRhv82hUGGRvZVZnXs/GilpbPfGjHoaR/bEAhEQKeT66hTbTr91z3/36Ia79N/Ne+ffv/SEEAAAQQQQACBeBDgr5J4eBW4BwQQQAABBMaxgOYDrJ3sDOAF87hZ6any3VPnyvRJzkILm3d1y6PLt/vsaktrt8/97ETAl8CengET+Nt3hBagca/+6236b2F2upSa4iA0BBBAAAEEEEAgXgQIAMbLK8F9IIAAAgggMI4Fik0utClFoQdEcjPT5AenzxOtuGpvf/3nNvEV5OvoHRL7FE77uSwj4E/A/r3z5qbdjsO9Tf9NmSAyYzJTfx1YrCCAAAIIIIDAARcgAHjAXwJuAAEEEEAAgeQQmGamROZnpYX8sAVmVNU3T5opGmBxNc3198sXN8rQsPepwBog1OmbNASCEdDvqfbeQeuUYKb/Vpnvcx21SkMAAQQQQAABBOJJgABgPL0a3AsCCCCAAALjWCDcfIBKo1OJP3XIVIfSlt098rfl2xzb7Ct9gyOy00/VYPvxLCOgAntMsRlX3DjQ6b95ZqSqjgykIYAAAggggAAC8SZAADDeXhHuBwEEEEAAgXEsEG4+QKU57/CpoqOs7O1hMxV4k8nR5q017umVQR+jBL2dx/bkFdD8f64WyPTfCR9N/dVANw0BBBBAAAEEEIg3AQKA8faKcD8IIIAAAgiMc4Fw8wGmp6bI1xfXSqot0GJmAssvX9roNcg3NLxXtpkgIA2BQARGzDdUmxkBqM3T9N+jZoyt/ju1KFs0VyUNAQQQQAABBBCIRwECgPH4qnBPCCCAAAIIjHMBzQdYnJse8lPWlOTKOYc5pwLrKL+//KPRa586DbhvcNjrfnYg4BJoM7n/NL+kNk/TfxfVTHQdan3NzkgVDQDSEEAAAQQQQACBeBUgABivrwz3hQACCCCAwDgW0GmSc8rypbQgM+SnPOewKVI9yTkV+NEV22VDc6fHPjWf21aTL5CGgD+B3d39o4e84Vb9d4rJ8acBbHvTqr8p9uo09p0sI4AAAggggAACcSBAADAOXgRuAQEEEEAAgWQU0CCgFvWoLA5t5FRaSopcecJMSbUFXjTIp1OBB4Y8VwXe3T0gvQOMAkzG77dAn1mn/GoBEG3W9N9NrY5TF7lN/y03AcGCrNBHszo6ZwUBBBBAAAEEEIiSAAHAKMGG0m1zc7P8/e9/l+uvv15OP/10KSkpEX1zpP8uvvjiULr0e84DDzwgS5YskfLycsnKypLq6mr54he/KG+88YbfczkAAQQQQACBSAhoQQ8dQWV+3QXddCTW+YdXOs7b3tYnf36vwbHNtaIBwh3t5AJ0efB1rEBH75BozkhtG5q7pNUEje1N8/+5WkZaypjRgK59fEUAAQQQQAABBOJJgEzFcfRqlJWVxexuent75fzzz5cnnnjCcc2tW7fKfffdJxoY1EDkDTfc4NjPCgIIIIAAAtEQKCvIEi3usb6pUz5KvRbwZc46ZIq8U79bNu3qHj3n8fd3yBHVE2VOef7oNtdCS2e/GXWYIxq8oSHgLtBqm/775ubdjt06/bfKNmJVR6/aR6A6DmYFAQQQQAABBBCIIwH+8o2jF8N+K9OmTbNG5tm3RXL50ksvHQ3+nXjiifLwww/L22+/Lffcc4/U1tbKyMiI3HjjjfKrX/0qkpelLwQQQAABBLwKTMzNkLopBSYQGNxQQA3AfP2EWkmzTwU2V/lfMxW4f2jsdF8NMDaZgiA0BNwF9k3/3Tfib8QMF33Lx/Rf/XbT71kaAggggAACCCCQCAIEAOPoVdIRd4899pjs3LlTtmzZInfffXdU7u7555+XBx980Or7rLPOkmeffVbOPvtsOfLII0UDg2+++aZoAFLb9773PdmzZ4+1zH8QQAABBBCItkC+yaV20JRCyUwP7k8UHdH3mSOqHLenVX8ffMfzVGANALqqvDpOYiWpBTr7h0z+yH3Tfzf6mf5bmJNujVpNajAeHgEEEEAAAQQSRiC4v64T5rES80Z/9KMfyZlnninRngp82223WUBpaWnyi1/8QlJTUx1gmnvwlltusba1tbXJ0qVLHftZQQABBBBAIJoC2RmpMt8EAXMznb+f/F3zzAUVMqs0z3HYUyt3yqodHY5tujJocrzpVGAaAnaBPbZ8f2+6jf5zn/5bkhd6BWv7NVlGAAEEEEAAAQRiIUAAMBbKcXSNzs5OWbZsmXVHJ598slRWOhOnu271vPPOk4KCAmv1b3/7m2szXxFAAAEEEIiJgObn05GAhdmBV1dNMXMyr1hcO2YK8d1mKnDf4NipwNtNMRCd8klDwCWgVaK1WdN/3fL/afEPLcymTaedF+cw/dfC4D8IIIAAAgggkBACBAAT4mWK3E2+8847MjCw74/bxYsXe+04IyNDjjrqKGu/njM4OOj1WHYggAACCCAQDQENssyryJfJ+YEHWqYUZcsFR+5LY+G6p2Yz0u/+t7e6Vke/9g+OjKnwOrqThaQT6DbTf/vM94Q2T9N/F9mq/07MTaf4R9J9h/DACCCAAAIIJLYAAcDEfv2CvvtVq1aNnjN37tzRZU8Lrv1DQ0Oyfv16T4ewDQEEEEAAgagK6IirmaX5MtUE9gJtp80vl7lu1X+fXdUkK/8/e/cBHkd1Lnz8Ve9dlm1Zxt3YxjYQY7DpJRBqaCEhjRZK6iV8cEm5geTmhiSEQOpNgRAIaSSXEJpJAiGEarAJEJtiwLjbcpNlq1fPd96xZzSz2pV2pV1pd/Z/nkfeqWfO+c1Yu3r3lM17+mVRv5vJQPqhpOkGp/WfVr9f999y/+y/VUV0/03Tx4RqI4AAAgggkLICBABT9tYNreCbNm1yT4zU/dc5YOLEvsHUN24MP4i6c2zoq15noJ/6+vrQU1hHAAEEEEAgosABVYUypbrIdMGMeIi7I9McdNWx0yTPdCP2pp8//a60dfV4N0mLafW1p51W7j6UNF1xAoBhu/9O6ev+q7NUl5sJQEgIIIAAAggggEAqCWSnUmEp6/AFdAxAJxUX+wdKd7Y7r0VFRc6itLS0uMvRLHiDh9EczzEIIIAAAggMJjCuLN8e32+1mZ117yBD9+mxHz78ALn7+XVutjtbuuS3L26QK46Z6m7ThXozFmAsYw36TmYlEAI6RmRb175xIvX5avBMBqIV1PH/nFRZlOuOBehs4xUBBBBAAAEEEEh2Af9X48leWso3bIGOjr6uTjrO30ApL6+ve0t7e/tAh7IPAQQQQACBERGoMjOvzq4tjWr8tZPnjJU54/dNaOUU7h+rtsurG3c7q/ZrY2t3v5aBvgNYCbyAN+D3Yujsv6b7b11FXxd0fQZJCCCAAAIIIIBAqgkQAEy1OzbM8ubn57s5OJOBuBtCFjo7O90tBQV9H3zdjQMsaJfhgX6WLVs2wNnsQgABBBBAILJAaX6OGRdw4FbserZ2Bf7kcVMlP8f/ceeOZ9aITvjgTVsYC9DLkXbLjZ7Zf18Inf3X0/1XZ6emtWjaPR5UGAEEEEAAgUAI0AU4ELcx+kqUlJS4Bw/Wrbe1tdU9drDuwu6B+xcGG18w9HjWEUAAAQQQiEVAu2FOrCyQjbsGbqE+piRfPnrEJLnz2bVu9jrW2x9f2iiXHjXF3dbQ0ikHVBaKBnhI6SXQ2dMrzR37AsLa/dcZC9BR8Hb/rS4euPeEcw6vCCCAAAIIIIBAsgnwKTfZ7kiCy+MNzHknBAl3We/EH4zpF06IbQgggAACoylQV1EoY0oGD8icNKtG5k0o8xVVuwJ7Az06puDWPX3DZPgOZiXQAt7nINzsv3T/DfTtp3IIIIAAAgikjQABwLS51fsqOmfOHLfGq1atcpfDLTj7s7OzZcaMGeEOYRsCCCCAAAKjKjC1ulhK8gfu0JBhugJfeexUyc7sm0K4x0T8lqzY4iv7tuYO6R1sdhHfGawEQaDBTA6jKezsv2byD31+NBXkZklx3sDPmn0g/yCAAAIIIIAAAkkoQAAwCW9KIou0cOFCcSb/eOqppyJeSscHfOGFF+z9ek5OTk7EY9mBAAIIIIDAaAlkmqDezLElkhcyzl9oearNxA3HHzjGt/nvb26XpvZud1tPryXbTRCQlD4COvvvgN1/zfh/Tqoy3c5JCCCAAAIIIIBAqgoQAEzVOzfEcusYgCeddJJ99t///neJ1A34/vvvl6amJvu4c889d4hX4zQEEEAAAQQSL6Dj9s0aVzLozMDvP7jWTAzSV56u3r3y6Gv1fRvMUr3pBmxZpj8wKS0EdjT3TXgW2v13QnmBb/bfMSXM/psWDwWVRAABBBBAIKACBAADdmPvvvtuu6uKdlf52te+FrZ21113nb29p6dHPvOZz0hvb6/vuJ07d8oXvvAFe1t5eblcfvnlvv2sIIAAAgggkGwChblmuAozM/D+3pphi6cTghwzw98K8LHXt0mLZ0bgzu69snN/l9CwmbAxUAINntl/XwyZ/feIqZVu91/t+pufkxWoulMZBBBAAAEEEEgvAQYySaL7/eyzz8rq1avdEmkgzkm6XYN73nTJJZd4V6NePvHEE+XCCy+Ue++9Vx566CE5+eST5fOf/7zU1tbKypUr5aabbpINGzbY+d18881SUVERdd4ciAACCCCAwGgJVJgumjqT7/qGtohFONu0Anz67R3itPFrN11A//b6Vjn/PXXuOfV72s3kIrT2ckECuqCB3/aufV+Chp3919P9tzqKyWYCykS1EEAAAQQQQCAgAgQAk+hG/uIXv5Bf/epXYUv03HPPif5401ADgJrHL3/5S7uL76OPPipPPvmk/ePNOzMzU2644Qa58sorvZtZRgABBBBAIKkFak23TQ3qbW/q69rpLfB4s3/RtCpZ+m6Du/kvphvw6XPH25M86MbWzl7Z09YtZYWMf+siBXBhZ5Tdf7VVaSXj/wXwCaBKCCCAAAIIpJcAXYDT6367tS0oKJAlS5bIb3/7W7sFYE1NjT05yMSJE+UjH/mIaGvESF2I3UxYQAABBBBAIAkFplYXSWlB5O84zzlkgq/UGvB7/M1tvm1bTCtAUnAFdJzHhtZ9QeJws/96u//qLNN52XT/De7TQM0QQAABBBBID4EM8wHI6QWTHjWmlkkhoJOPaLBR08aNG6Wurq/rVVIUkEIggAACCKS0QLeZ4OO1zXukw4zpFy7d+thb8tL6RndXaUGO/PDCQ3yBnvl1ZVJkxn4jBU9AW3i+Ub9vsrO3tzXLVx963VfJ75w/Xyaa7uSapo0pkprSfN9+VhBAAAEEEEglAf7+TqW7lbiy0gIwcbbkjAACCCCAAAKjJJCTpTMDl0p2lmfaX09Zzj3U3wqwqb1b/rFqu+cInRGYVoA+kACt7Gjp6yK+dE1fd3Ctos7+6wT/dNZouv8G6MZTFQQQQAABBNJYgABgGt98qo4AAggggECQBQpysyLODDx1TLEcMrHcV/2H/71FtOWgk3Q24M6efZNEONt4TX2BvXstaWzrsiui3X+Xhcz+u8jM/uuk8sJcE0Tm47LjwSsCCCCAAAIIpK4An2hS995RcgQQQAABBBAYREADOJOrisIeFdoKsNF0C33KzBDsJB0kZeueDmeV14AIaPCvp3ffCDjhZv89wjP7b1VxbkBqTTUQQAABBBBAIN0FCACm+xNA/RFAAAEEEAi4wLiyfBlbmtevljPHlsic8aW+7Q+9ukV69va1AtxuZort8bQK9B3MSkoKaMtOJw3U/TfL9P+tMAFkEgIIIIAAAgggEAQBAoBBuIvUAQEEEEAAAQQGFJhiZgYuMxN9hKbQVoA6Ntxzq/vGhNOWYhoEJAVDQIO5u6Ps/ltZlCMaBCQhgAACCCCAAAJBECAAGIS7SB0QQAABBBBAYECBjIwMmTm2WHRcQG86qLbUHifQu+3BVzeLjhPnpHrTDdjS/sCklBfY1dolzq19Z1uL6Lo3+br/FvVvNeo9lmUEEEAAAQQQQCCVBAgAptLdoqwIIIAAAgggMGQBncxhRk2xmFigmzQwGNoKUAN+L67tawXY1bNXvLPGuiezkHIC3vs4UPffHDN7dHlh/xajKVdhCowAAggggAACCOwXIADIo4AAAggggAACaSNQlJctVUX+cd10NuDJVYU+gz+/YloBelr91e9mMhAfUAqu6IzOTe09dsm1heeLa/qCvLpx0dQqt1aV5hnR4DAJAQQQQAABBBAIigABwKDcSeqBAAIIIIAAAlEJ1FUUhmkFWOc7d2Nju7y8vtHd1tbV644d525kIaUEGjyTf7y5tUl2t3f7yr94Wl8AsLqE7r8+HFYQQAABBBBAIOUFCACm/C2kAggggAACCCAQi4COA1hd7A/wHDa5QiaUF/iy0VaA3rH/Nuxq8637DmYl6QV2mglenLT0XX/rv0mVhe79z83OlNJ8uv86VrwigAACCCCAQDAECAAG4z5SCwQQQAABBBCIQaCuokC8E7xmhhkLcM3OVlmxaY+ba2tnLzMCuxqptdDW1SN6/zT1mu6/y9bt8lVgkbf1X7G/i7jvQFYQQAABBBBAAIEUFSAAmKI3jmIjgAACCCCAwNAF8nOypKY035eBjgE3LmRbaCvAjaYVYHfvXt95rCS/gLf77+tb9khzx76xAJ2SL/aM/xfaOtQ5hlcEEEAAAQQQQCCVBQgApvLdo+wIIIAAAgggMGQB7fLrbQWYZVbef0itL7+3tjXLm1ub3W3dvZZoEJCUWgLe2X+fD+n+O21MkYzdH/jV7uE6UQwJAQQQQAABBBAImgABwKDdUeqDAAIIIIAAAlEJ6Fhv48r8rQCPmV5txgf0dwHVVoDetL2503Qn9bcg8+5nObkEmjq6pbN7X6vNHtN686XQ7r++1n/+e59cNaE0CCCAAAIIIIDA0AUIAA7djjMRQAABBBBAIMUFak0rQG3556TsrEw562B/K8DXNu+Rd0xLQCdZlshaMz4gKTUEvN1/dUzHVjOjszfR/derwTICCCCAAAIIBFWAAGBQ7yz1QgABBBBAAIFBBXJMwG98SCvA42fWSHmBfxbY0FaAOobcDtMSkJTcAjqLc4N39t81/tl/Z44tlqr9M0IXm66/OjYkCQEEEEAAAQQQCKIAAcAg3lXqhAACCCCAAAJRC2gAMDurrxWgdg0+c76/FeArG3f3a/W3YVerPaNs1BfiwBEX2N3WbSZtMU02TerqMd1/1/tn/108tdotU3UJ3X9dDBYQQAABBBBAIHACBAADd0upEAIIIIAAAgjEIqDdfkNbAZ40u0a0RZg3PfCqfyzArh5LNjUyIYjXKNmWG1r7Wmm+aoK4HfvHAtRyasj3iKmVdpEzzEpVUZ69zD8IIIAAAggggEAQBQgABvGuUicEEEAAAQQQiElgfFmB5HhaAWpX0DPmjfflsWztrn4zANfv6ZD2kDHlfCexMmoCvXst2dXa7V5/6Zqd7rIuzB5fKhWF+1r9lebniLb8JCGAAAIIIIAAAkEV4JNOUO8s9UIAAQQQQACBqAV0IhCdEMSbTjlorBTm+seEe/DfW7yHCBOC+DiSamVXa5fbRbuju1de2bDbV77F06rc9dCZn90dLCCAAAIIIIAAAgERIAAYkBtJNRBAAAEEEEBgeALjSvN9rcAKc7Pl1IPG+TJ9/t2dUr+n3bdtT3u3b6IJ305WRk3A2/335Q2N0mnGAHSSTvx8+JR93X91ubKI8f8cG14RQAABBBBAIJgCBACDeV+pFQIIIIAAAgjEKJBpIkETQloBnjp3nJkZtu/jkrb4+9Xz60zLv30TSziXWL+rTfaaLqek5BDo7t0rOgGIk5a+65/9d25tmWi3X03lphuwjgNJQgABBBBAAAEEgizAp50g313qhgACCCCAAAIxCdSU5EmeJ+BXYoJEJ88e68vj35v2yLOr/ePJdZrJJTbv9rcM9J3EyogKNLR02d2z9aJtXT3y703+7r+LPN1/af03oreGiyGAAAIIIIDAKAkQABwleC6LAAIIIIAAAsknoK0A6yr8YwGefcgE00psX2sxp8T3LF0vTabrrzdtMQFAHWuONPoCO1v6Zv/91/pG6e7ta52p4z0unNzX/bci5N6OfukpAQIIIIAAAgggEH8BAoDxNyVHBBBAAAEEEEhhgTHFeVLgmfyjKC9bLjtqiq9GLZ09cs/Sdb5t2gN4fUObbxsrIy+gQdjmjh73ws+HdP89uK5Mis091VRmgn90/3WpWEAAAQQQQACBAAsQAAzwzaVqCCCAAAIIIBC7QEZG/1aA2mLMmTTCyfE5E1h6xUwu4U068+zuti7vJpZHWMDb+q/FBAJXmi7b3rRoat/sv3T/9cqwjAACCCCAAAJBFiAAGOS7S90QQAABBBBAYEgC1aYVYKGnFaBmcumRk6UoZNudz66V9i5/t991phVg6CQhQyoEJw1JQMf/c9Lydbuk1zNhS05WhiyYVGHvNnFeqTQTgJAQQAABBBBAAIF0ECAAmA53mToigAACCCCAQMwCEysLfefobLEfXTTJt63BtPi7d/kG3zYNCNbv6fBtY2VkBFpN1+w2T0B26Rr/7L+HTqwwgd393X8L6P47MneFqyCAAAIIIIBAMggQAEyGu0AZEEAAAQQQQCDpBLR7qDNWnFO442eOkYNqS51V+/XxN7bJ29uafds2NbZLV89e3zZWEi/g7f67x0zS8tqWyN1/q8z9JSGAAAIIIIAAAukiQAAwXe409UQAAQQQQACBmAUmVvpnBNbxAa84ZqrkZvV9hNL5ZW9/eo2ZabYv4NdrZgTZsKs15utxwtAFtNv1Tk/332VrG0xX7L788rIz5dADyu0N2v23ggBgHw5LCCCAAAIIIBB4gb5Pr4GvKhVEAAEEEEAAAQRiE9BuvyX5+7qMOmeOLc2XCw6rc1bt18272+WBVzf7tu1o7pKmjm7fNlYSJ9BkJvzwtroM7f77ngMqJD8nyy5AaX6O5HiCuIkrFTkjgAACCCCAAALJIUAAMDnuA6VAAAEEEEAAgSQVCB0LUIt52tzxMqW6yFfiB1/dIht3tfm2rdvZyoQgPpHErXi7/+pszKvq/d2yF0/rm/23qpjuv4m7E+SMAAIIIIAAAskoQAAwGe8KZUIAAQQQQACBpBEoM5NF6I83ZWVmyJXHThXz4ibt9nv7M2tkr3l1Umtnr2xv7nRWeU2QgJpr0M9JL2r3X2fFvBaYln8H13m6/zL7r0eHRQQQQAABBBBIBwECgOlwl6kjAggggAACCAxLIHQsQM1sclWRnHVwrS/f1dtb5G9vbPVt01aB3vEBfTtZiYvAztZO6entC/ktfdc/++9hkyok14wBqEm7dDvLcbk4mSCAAAIIIIAAAikgQAAwBW4SRUQAAQQQQACB0RUoMWPG6azAoem8Q+tkfFm+b/Mflm+UHc0d7rZuE5ja1tS37u5gIW4CW/f0+WpX4HdMINabFnm7/xbleXexjAACCCCAAAIIpIUAAcC0uM1UEgEEEEAAAQSGKzC5ulCyszx9fk2G2pJMZwX2ps6evfKLZ9f6xv7b1tTpW/cez/LwBPa0dYt2tXbSC2v8rf+K8rJk/oQyZ3fYQK67kwUEEEAAAQQQQCCgAgQAA3pjqRYCCCCAAAIIxFcgLzur38QfeoXZ40vlpFk1vout2LRHnl29092ms9PuNoEqUvwF6pvafZk+H9L99/DJlSZwu+8jL91/fVSsIIAAAggggEAaCRAATKObTVURQAABBBBAYHgC1cV5Mqakf1fgjxxxgFQU+icKuWfpetnT3hf02+bpFjy8UnC2I9De1SuNrX3G2hV4rZl52ZsWTWX2X68HywgggAACCCCQngIEANPzvlPNU9LCAABAAElEQVRrBBBAAAEEEBiigE7+kZfj/whVmJstlx09xZdjS2eP3LN0nbtNWwB2dPd1VXV3sDBkga0hYyuGdv8tNRN+HFRL998hA3MiAggggAACCARGwP/pNTDVoiIIIIAAAggggEBiBLQ76fSaYsnwDwcoh02qlCOmVPouqt1RX97QaG+zzCS1O5o7fftZGbpAT+/efp5LQ8b/O3xKlWRl7rtR2v1Xu3GTEEAAAQQQQACBdBQgAJiOd506I4AAAggggMCwBErNrMATygv65XHJkZNFJ53wpjvNhCDaVVXTdtMN2NJIIGnYAttMMLV3b5/l5sZ22bCrzZfvYs/sv+FmcfYdzAoCCCCAAAIIIBBgAQKAAb65VA0BBBBAAAEEEidQV1EgxXnZvguUF+bKxxdN8m3b1dol9y7fYG/r6rFE10nDE9Agqo73501L1/RNuqLby82YjLPGlriHEAB0KVhAAAEEEEAAgTQUIACYhjedKiOAAAIIIIDA8AUyTB9g7QrsdDF1cjx2xhiZO6Fv3Dnd/vgb2+Strc32Idua6AbsWA31tcEEUXVmZSdpQDC0+69O/pG5v/uvBmrzc/wtM51zeUUAAQQQQAABBNJBgABgOtxl6ogAAggggAACCREoyM2SSVWFvrw1MHi5mRAkL7vvY5Z2VL39mXdFx63TmYGdLsG+E1mJWiC09Z92/d2y298icLFn9t/K4v4zN0d9MQ5EAAEEEEAAAQQCIND3yTQAlaEKCCCAAAIIIIDASAuMLc2XiqIc32V12wULJvq2aYDqmdX7uqnqWICkoQk0dXRLc0eP7+TQ1n/VJuA3w7TOdFJVEQFAx4JXBBBAAAEEEEhPAQKA6XnfqTUCCCCAAAIIxFFg2phiyc32Twt82txxMrW6yHeVJSvq7UlAdDbgvZ4JLHwHsTKgQGjrP7v7r5lt2Zu0+6+2xNSkk7LQ/derwzICCCCAAAIIpKMAAcB0vOvUGQEEEEAAAQTiKpCTlSkaBPQmHX/ugsPqvJtk8+52+fem3dLda4mOY0eKTaCju7ffJCprdraa2ZX94yr6uv/S+i82ZI5GAAEEEEAAgUAKEAAM5G2lUggggAACCCAw0gI6A/C4snzfZQ+uK5cJ5QW+bY+YVoCatjXRDdgHE8WKmpn5PnzphTX+1n9jS/NkiqflZVVRnu94VhBAAAEEEEAAgXQUIACYjnedOiOAAAIIIIBAQgQmVRaKTgziJO2Gesb88c6q/fr6liZZ19Bqj2PX1uUfy853ICs+gV7TZTq0pZ92o14a0v1XW/853X8Lzb3w3g9fhqwggAACCCCAAAJpJEAAMI1uNlVFAAEEEEAAgcQKaLdfnXzCvLjp6OnVUlbgnyRExwLUtK3J33XVPYmFfgI6cUqP6TrtTS+sbejXlVrH/3NSJd1/HQpeEUAAAQQQQCDNBQgApvkDQPURQAABBBBAIL4CRXnZUmdaAjpJxwd830HjnFX7VVutNbR0yk7zoy3bSAML6EQfoZN/aOu/+1/e7DtxclWhHOCxrzKzAZMQQAABBBBAAAEERAgA8hQggAACCCCAAAJxFqg1YwGWFmS7ub53do3kmkCgk3pNQOuvr2+1W7RpIJA0sEBjW7d0dO/1HaSt/3RSFW867z11bvdf7fpbmNt3D7zHsYwAAggggAACCKSbQN8n0XSrOfVFAAEEEEAAAQQSJKBj0E03XYGzs/b1BS7Jz5HjDhzju9oTb24XHQOQbsA+lrAr9Xv8gb5wrf8mmdZ/h02qcM+vovuva8ECAggggAACCCBAAJBnAAEEEEAAAQQQSIBAXnaWbzba0+eOF8/QgNLe3Sv/fGuHtHT22D8JKEIgslSfpnb/ZCnhWv+d72n9pxVn/L9A3H4qgQACCCCAAAJxEiAAGCdIskEAAQQQQAABBEIFqovzZEzJvnHoxpluwYdN7muhpsf+5bV6ewzAbU0doaeyvl9g6xBa/+XnZIqOxUhCAAEEEEAAAQQQ2CdAAJAnAQEEEEAAAQQQSKDA5KoiyTMBKU1nzq/1XWlnS5e8qDPZmteeXv8Yd74D03Slq2evmSily1f7aFr/VRXl+c5hBQEEEEAAAQQQSHcBAoDp/gRQfwQQQAABBBBIqEC2mfyjrqLAvsbMsSUyw4wN6E2PrKi3g3+hgS7vMem6rC0jzXwpbopm7D89uJLZf10zFhBAAAEEEEAAARUgAMhzgAACCCCAAAIIJFhgjOkKrN1SNZ0xb7zvamt3tsqbW5vNZCB0A/bCaLAv1CSa1n/a2rKY7r9eSpYRQAABBBBAAAECgDwDCCCAAAIIIIBAogV0VuC6ikL7MgsnV0pNib+L6hLTCrCtq1eaOroTXZSUyX9HS6d09/Y1/4u29R+z/6bMLaagCCCAAAIIIDCCArQAHEFsLoUAAggggAAC6StQbbqlFuRmSWZmhpxmZgT2ppc3NMqW3e2ynVaALkv9Hn+LyGha/+nJzP7rErKAAAIIIIAAAgi4AgQAXQoWEEAAAQQQQACBxAnsawW4byzA4w8cI0UmGOhNj66stycD6WYyENnd1iXtpkWkk8K2/qsslMMm+WdV1u6/Jfk5zmm8IoAAAggggAACCOwXIADIo4AAAggggAACCIyQgHZPLTSBv/ycLHnvnLG+qz79zg5pbOuWHc2dvu3puDLk1n+FuenIRZ0RQAABBBBAAIFBBQgADkrEAQgggAACCCCAQHwEtBXghP0zAp8yZ5xkme7ATtLx7h5/Y2u/iS+c/eny2tbVY1oA9o2FGKn134LJ/tZ/6sPsv+nylFBPBBBAAAEEEIhVgABgrGIcjwACCCCAAAIIDEOg2swIrK0Aday6o6dX+3J67I1t0tTeI3s8ATDfAWmwEm3rv0wTTPWm3OxMKaX7r5eEZQQQQAABBBBAwBUgAOhSsIAAAggggAACCIyMQN3+VoCnz/NPBtLc0SPPmK7A25r9E2CMTKlG/yo6/uFOTxfomFr/mYAqCQEEEEAAAQQQQCC8AAHA8C5sRQABBBBAAAEEEiZQZVoBFuVlyQFmIov5E8p811liJgPZ2dIpXT17fdvTYWWbmQV5r9VX00gz/4a2/tMzmP23z40lBBBAAAEEEEAgVIAAYKgI6wgggAACCCCAwAgI1FUU2lc5Y76/FaB2gX15/W7ZnoatALcPsfVfvpn9tzQ/ewTuGpdAAAEEEEAAAQRSU4AAYGreN0qNAAIIIIAAAikuoC3WivOyZZ5pAagtAb3pkRVbTACwUyzL0xzOe0AAlzu6e6Wzu6/VYyyt/yZXFYlOsEJCAAEEEEAAAQQQCC9AADC8C1sRQAABBBBAAIGEC+hYgBq4OiNkLMBVW5vljS1NvtlwE16YUb7AnvahzfxbXpgjFYz/N8p3j8sjgAACCCCAQLILEABM9jtE+RBAAAEEEEAgsAIauCoxXVePnFYlFSaQ5U1LVtSn1WQgTZ4A4ItrG2Tz7nYvh5z/njoJHftPG/1NqvK3nvSdxAoCCCCAAAIIIICALUAAkAcBAQQQQAABBBAYRQFtBZidlSnvO2icrxQaBHtnW4to19h0SE4LQJ35908vb/ZVeZLpIr1gcoVvm66MLc2XwlzG/usHwwYEEEAAAQQQQCBEgABgCAirCCCAAAIIIIDASAqUF+5rBXjS7LGSl9330Uxnw/2LmRF4U6O/JdxIlm2krtXa2SPdvfvGO4y29V9OVoZMNMFTEgIIIIAAAggggMDgAn2fMgc/liMQQAABBBBAAAEEEiAw0cwIrBOCnHBgjS/3f7y1XdY3tEp7V7BbATZ17Bv/L5bWfzqLsracJCGAAAIIIIAAAggMLsCnpsGNOAIBBBBAAAEEEEioQJkZ/0/HAjxt7jgzKUjfpTrMrLhPvLndjIfX1rcxgEtO999oW/8V5maZ7r95AZSgSggggAACCCCAQGIECAAmxpVcEUAAAQQQQACBmAS0FWCNGdPu8MmVvvP++vpW2bqnI7CtAC3LkuaOHoml9d/kqiJ79mQfFCsIIIAAAggggAACEQUIAEakYQcCCCCAAAIIIDByAtoKsLQgW86cP9530V2tXfL8uzorbjBbAbaY8f96zPh/0bb+qzQzJ6sVCQEEEEAAAQQQQCB6AQKA0VuN6JHr16+Xa6+9VmbNmiVFRUVSWVkpCxculFtuuUXa2ob3B8Ddd99tf2ueYfoYDfajx5IQQAABBBBAYGQEdFy76TUlcuDYEt8Fl5jJQHY0dwayFaB2/4229V+m6R49qarQZ8MKAggggAACCCCAwOACBAAHNxrxIx5++GGZP3++3HbbbfLWW2/ZAb/GxkZ56aWX5Prrr5dDDz1UVq9ePeLl4oIIIIAAAgggkFiBsoIc0Z8zQloBrm9ok9c2N5kZgYf3JWBiSz+03Jvae6Ju/Te+rEDyc7KGdiHOQgABBBBAAAEE0lggO43rnpRVf+WVV+RDH/qQtLe3S3FxsXzpS1+SE044wV6/99575Y477pC3335bzjjjDDsgWFLibyEQa6X+9re/SW1tbcTT6urqIu5jBwIIIIAAAgjEX6CuskAWHFAh48x4gFubOtwLPLJii8yrK5MJXT1SmBuMj3Da8q/ZzAD8yIp6t566MKmyUBZMrvBty83OkAkVBb5trCCAAAIIIIAAAghEJxCMT4/R1TUljrr66qvtYF92drY89thjsnjxYrfcJ554osyYMcNuBahBwFtvvVW+9rWvufuHsjBz5kyZPHnyUE7lHAQQQAABBBBIgEBpfo5UFufK6fPGyy+fW+te4d+b9sgG0xKwyoyBNyOki7B7UIotNJvx/3SMwzU7W30lP+89dZLpnQ7Z7J1ogoJZ2geYhAACCCCAAAIIIBCzAF2AYyZL3AnLli2TZ555xr7AJz7xCV/wz7mqjgs4e/Zse/UHP/iBdHd3O7t4RQABBBBAAIGACNSZlm7HzqyWknz/d7U6FmCDCZi1mVaAQUhNZvy/lZv3+KpSYLr4vmdSuW9bcV621JTk+7axggACCCCAAAIIIBC9AAHA6K0SfuQDDzzgXuPSSy91l70LmZmZctFFF9mbdu/eLU8++aR3N8sIIIAAAgggEACBEtMKcKzpAnzKnLG+2jy7eqfdYm5TY7tve6qu6AQgK03LRm+aU1sq2ebzjjdNrmbiD68HywgggAACCCCAQKwC/k9XsZ7N8XEVePbZZ+38dNbfBQsWRMz7uOOOc/c999xz7jILCCCAAAIIIBAcAe3yevKccZKT1dfttdeMmfe317dKQ0vqtwLUuuj4f6EtAOdPKPPdxDEluaYlZI5vGysIIIAAAggggAACsQkQAIzNK6FHv/nmm3b+06dPFx0DMFKaNWuWu8s5x90Q44K2NNRJQHJzc6W6uloWLVokX/nKV2Tz5s0x5sThCCCAAAIIIBBPAe32qi3fjp0xxpft39/YJh3dvWZG4NRuBajdfzfsapfd5tWb5nkCgDrmnwZCSQgggAACCCCAAALDE4gcZRpevpwdo0BHR4fs3LnTPmuwmXcrKipEWwm2trbKxo0bY7yS//B//vOf7oaGhgbRnxdffNGeYOT73/++XHXVVe7+WBY2bdo04OH19f7Z/gY8mJ0IIIAAAgikqcDEikJ7MpAnVm13BVq7euWfb22XU+eOl9byHikygcJUTE3a+i+k+2+1mfxkXFnfWH/jzXJedlYqVo8yI4AAAggggAACSSWQmp8Yk4owPoVpbm52MyouLnaXIy04AcCWlpZIhwy4ferUqXLeeefZE41MnDjRPnbNmjXypz/9Se677z7RgOQnP/lJyTAz8F155ZUD5hVup5NnuH1sQwABBBBAAIHoBDS4N3t8qSyYVCH/Wt/onvToyq129+DNu9tlZorOCKzj/63YvNutky7Mm1Buf/bQ5bycTJlQXqCLJAQQQAABBBBAAIFhChAAHCZgvE7XgJuTtDvuYCkvL88+pL099u4/5557rlx88cXuB2znWgsXLpQPfehD8sgjj9jBQZ1h+JprrpH3v//9Mm7cOOcwXhFAAAEEEEBgBAXGlubJmfPG+wKAO1o6ZdnaXbJ4WlVKtgLs7t0rja3dsqq+7wtQJZ1f1zf+3wGm62+m6QJMQgABBBBAAAEEEBi+AGMADt8wLjnk5/d1d+nq6ho0z87OTvuYgoLYvxkvKyvrF/zzXvDMM8+UG2+80d7U1tYmd955p3d3VMvaNXmgn2XLlkWVDwchgAACCCCQ7gKVRbkyzwTGpo0p8lEsWblFLMtKybEAdfy/t7c1S5cJBDpJQ30HmRmANZXkZ0t18b4vO+0N/IMAAggggAACCCAwLAECgMPii9/JJSUlbmbRdOvV8f80RdNd2M04hgXt9qvdfzU99dRTMZy571Adx3Cgn/Hjx8ecJycggAACCCCQjgL6fjy2NF/OnF/rq/67O1pl1dZm2dXaJa2dPb59yb6i3X9DZ/+dagKcOtuvfvyYUu0PdiZ7fSgfAggggAACCCCQ7AIEAJPkDmkLwKqqKrs0g02g0djYaE8Aogcnaqy9mpoatzzMCJwkDwnFQAABBBBIW4Ea0w348CmVUlPibxW3ZOW+SbVSbUbgpo4eWbGp//h/eoPHmDqm6sQmafuAUnEEEEAAAQQQSHoBAoBJdIvmzJljl2b16tXS0xP5m/xVq1a5pZ49e7a7HO8FpwVgvPMlPwQQQAABBBCITUBnwtUusafN9Y/JqxODbDETgWgrwJYUaQXY2dMr2/Z0yLqGNh+CdnPOzsoQnfmYhAACCCCAAAIIIBBfAQKA8fUcVm5HH320fb527/3Xv/4VMS9vl9yjjjoq4nHD2bFjxw7ZuXOnnUVtrb/L0XDy5VwEEEAAAQQQGJrAONMN+PgDa6QoN8uXwaNuK0B/QM13UBKtNLX39Ov+m5edKTNriu1Zf3PNMgkBBBBAAAEEEEAgvgJ8woqv57ByO+ecc9zz77rrLnfZu7B3716555577E3l5eVywgkneHfHbfn222+3BxbXDI877ri45UtGCCCAAAIIIDA0gbLCHCk3P++dM9aXwdPv7BAdU09n1U2FVoDhxv+bM75UNPCnYx2SEEAAAQQQQAABBOIvQAAw/qZDzvHwww+XY445xj5fZ95dunRpv7xuvfVWefPNN+3tV199teTk5PiO+ec//2lP3qHddy+55BLfPl1Zt26dvPLKK/22ezc88sgj8vWvf93epLMMX3rppd7dLCOAAAIIIIDAKAlogOx9B42TrEwzU8b+1N1ryWNvbLXXNjUmfyvAPe1d/VoAzjfdf3UCEG+9nPrxigACCCCAAAIIIDB8gezhZ0EO8RT4wQ9+INqtt729XU455RT58pe/bLfy0/V7771XtGWeppkzZ8q1114b86U1AKitBhcvXixnnXWWHHzwwaITfmhas2aN3HffffaPZVn2tu9+97syYcIEe5l/EEAAAQQQQGB0BXSCjKqiXDl6erU89fYOtzCPvb5N3n9wrWkFKNLc0W0H09ydSbTQ0d0ra3e02WMWeos1b0K53brRu41lBBBAAAEEEEAAgfgJEACMn2Vccjr00EPlD3/4g3zsYx+TpqYmOwAYmrEG/5YsWSIlJSWhu6Je19aF4VoYOhkUFhbK9773PbnyyiudTbwigAACCCCAwCgL5GRlSpWZDOSMeeN9AUDt+vu0CQiePGec6IzAs8f7ewiMcrHdy2v33xWb/bP/VpqAZm15PgFAV4kFBBBAAAEEEEAg/gIEAONvOuwctWXeihUrRFsDaqBv06ZNkpubK9OnT5cLLrhAPvvZz4oG6IaSFixYIL/5zW/s4N9LL70k9fX19mQfOutwRUWFHHTQQXLSSSfJ5Zdf7rYMHMp1OAcBBBBAAAEEEiMwtjRPJlYWyiETy+XVjX3BtEdXbpWTZo2V3W3dSdsKsMkEAFdu2uODmTehTPJysqQwl4+lPhhWEEAAAQQQQACBOApkmK6e+/p6xjFTskJgMAENak6cONE+bOPGjVJXVzfYKexHAAEEEEAAgf0CGkR7YU2D3PTovnGBHZj/996ZsnBKpd2abraZWCPZ0gtrdsrFv1wunT173aJ97sTpcu6hE2TqmGJ3GwsIIIAAAgggED8B/v6On2Uq58QkIKl89yg7AggggAACCKSlgLYCPKi2VCZV+XsELFlZb3s4rQCTCaetq0de39LsC/5p+ebWlpmAZW4yFZWyIIAAAggggAACgRMgABi4W0qFEEAAAQQQQCDoAjoOYE52ppw5v9ZX1be2Ncvb5kfTxl3tvn2jvaLj/63c1NdlWcszpbpIygpzpKwgOccsHG0zro8AAggggAACCMRLgABgvCTJBwEEEEAAAQQQGCGBrMwMqTZBwEVTK0Un0fAmpxWgBtwaWjq9u0Z12Q4Abu4//l9JfrZofUgIIIAAAggggAACiRMgAJg4W3JGAAEEEEAAAQQSJjCuNF+yMzPltLnjfNdYvnaXbGvqsLeta2iVnt6+8fZ8B47gig45vWV3u6zZ0eq7qk4AUkH3X58JKwgggAACCCCAQCIECAAmQpU8EUAAAQQQQACBBAsU5GZJaUG2nDirRgrMLLpO0tndHt0/FmBXjyUbG0e/K3BLZ4/8e+Me8c48l5uVKQeOK7EnLHHKzisCCCCAAAIIIIBAYgQIACbGlVwRQAABBBBAAIGEC4w1rQALc7PlpNk1vmv9860d0tzRbW/T1oDOsu+gEVxp6uiRFWbmYm+aPb5EivKy7fJ7t7OMAAIIIIAAAgggEH8BAoDxNyVHBBBAAAEEEEBgRASqzPh/udkZcupB4yQro28cvS7T7ffxN7bZZTC9b2XtzlbRbrijlXa3dsnKzf4JQOZNKKf132jdEK6LAAIIIIAAAmknQAAw7W45FUYAAQQQQACBoAhkmKBfTUm+6KzAi6dV+ar1NxMA7OrZN/5fa2ev1O/ZNy6g76ARWNm717JnJt7Z0uW72vw6xv/zgbCCAAIIIIAAAggkUIAAYAJxyRoBBBBAAAEEEEi0QE1pnmjjvzPmj/ddqsnMAvzs6p3utk1mLMCO7l53faQWms34f6Hdf8sLc2RiZYGUFeSMVDG4DgIIIIAAAgggkNYCBADT+vZTeQQQQAABBBBIdYG87Cx7Jt3JVUUyt7bUVx2dDGTv/q6/vaYlns4KPNJJA5ErNvvH/9PZf0tN8C8rs6/b8kiXi+shgAACCCCAAALpJEAAMJ3uNnVFAAEEEEAAgUAKjDWtADWdMb/WV7/Nu9vl1Y19Y+81tnZLQ0un75hEr+j13tjS5LvM/Dod/y/Xt40VBBBAAAEEEEAAgcQJEABMnC05I4AAAggggAACIyKgwbT8nEw52IyrN7GiwHfN+1/eJDoOn5O0FWCPmSRkJJK2Onx1025pD+l6rC0VK0w3YBICCCCAAAIIIIDAyAgQABwZZ66CAAIIIIAAAggkVGBsab4ZCzCj31iA7+5olb+/uW9GYC1AV48lG3a1JbQsTubNHab77yZ/999JlYVSY8pamJvtHMYrAggggAACCCCAQIIFCAAmGJjsEUAAAQQQQACBkRAYU5InOqTeUdOqZZwJsHnTvcs3+rr+bm/uFA3OJTrtMeP/rQwJAM4zrRR1EhASAggggAACCCCAwMgJEAAcOWuuhAACCCCAAAIIJEwgJytTqopzJdu8Xn7MFN91tAvuXc+vE2v/hCD6snZnq7vuOziOKzoG4eodLb4cdQKQCsb/85mwggACCCCAAAIIJFqAAGCihckfAQQQQAABBBAYIQHtBqzpoNoyOX7mGN9V/7W+UZat2+Vua+3slS17Otz1eC90m3EGX1rXaIKMfTnnZGXI7PGlUmZmACYhgAACCCCAAAIIjJwAAcCRs+ZKCCCAAAIIIIBAQgVK8nOkKC/LvsZHj5gkpSGBtrufWyetnT1uGTY3tktHyAQd7s5hLjSZ7r8rzAQg3jRrXKndSjFL+yqTEEAAAQQQQAABBEZMgADgiFFzIQQQQAABBBBAIPECTivA4vxsuXjxJN8Fd5ug3O+XbXC36Sy92hU4Eampo6ffBCDa/VdnLCYhgAACCCCAAAIIjKwAAcCR9eZqCCCAAAIIIIBAQgWqi/PMOID7Wtgtnlolh0ws913viVXb5c36Jnfb7rZu2dnS6a7Ha2HV1ibRyUa8ab6ZAKSCCUC8JCwjgAACCCCAAAIjIkAAcESYuQgCCCCAAAIIIDAyAtq9VoOAmjIyMuSyo6ZIXrb/I98vnlkjOkafk9Y3tEqPZ93ZPtTXzp5eWb620Xe6dkeeVlMshbnZvu2sIIAAAggggAACCCRewP9pMPHX4woIIIAAAggggAACCRYYW7ovAKiXGVOSJx88bKLvijr5xwOvbna3dfVYsmFXm7s+3IWm9h55bfMeXzba/beyiO6/PhRWEEAAAQQQQACBERIgADhC0FwGAQQQQAABBBAYKQFtZVdixgB00qkHjZNpY4qcVfv1wVe3yEZP0G9bU6c0d3T7jhnqyq7WLnltS/8AYHnIpCRDzZ/zEEAAAQQQQAABBGITIAAYmxdHI4AAAggggAACKSEwrizfLWem6RZ8xTFTxTv5rk4A8otn18hey3KPW7OjVSzPursjxoWXNzRKW1ev7ywd/6+MAKDPhBUEEEAAAQQQQGCkBAgAjpQ010EAAQQQQAABBEZQoMp0t/W2ApxUVSRnzq/1leDtbS3yxJvb3G0atNPuwcNJHd298q/1/vH/JlYUyKSqQjM5CR89h2PLuQgggAACCCCAwFAF+BQ2VDnOQwABBBBAAAEEklhAJwCZarr9mhc3nf+eOvGOD6g7fr9so2iXXSdtbmwXDeINNTW1d8vKTWG6/xYy/t9QTTkPAQQQQAABBBAYrkDf4DDDzYnzEUAAAQQQQAABBJJKQMcCnFBeIJtMUE9TrpkN+PKjp8pNj77plrPdBPvufn6t/L+TD7S3adfgVVubzWy9We4xsSzsaO6Qd7Y3+06ZV1cujP/nI2EFAQQQQAABBBAYUQECgCPKzcUQQAABBBBAAIGRFdAAoLbwc8bkm2tm4z1u5hh56u0dbkGWr2uU5Wt3ycIplfa2dtMVWH+Gkl5ev9uMK9h3ZrYZePBgM/5fUR4fO/tUWEIAAQQQQAABBEZWgC7AI+vN1RBAAAEEEEAAgREV0AlAQrsCf/SIA6TUM0uwFuiXphVgW1fPsMu2crO/+++B40pkrGdCkmFfgAwQQAABBBBAAAEEYhYgABgzGScggAACCCCAAAKpJVCSnyPjSvtmBdb1ixZP9lVid1u3GQ9wg2/bUFZWhBv/j9l/h0LJOQgggAACCCCAQNwECADGjZKMEEAAAQQQQACB5BWYWFkoeTl9H/2OnFZld831lvjvb2434/81eTfFtKzj/21t8s8ifPDEcikjABiTIwcjgAACCCCAAALxFuj7FBjvnMkPAQQQQAABBBBAIGkEsrQrcHWRWx6dJfgTR0+RPDMxiDf94pm10t2717sp6uUVId1/S0w347kTSiU7y3+NqDPkQAQQQAABBBBAAIG4CPBpLC6MZIIAAggggAACCCS/QHlhrowpyXMLOqYkXy5YMNFd14XNu9vlwVe3+LZFu7IypPuvTjhSWdR3vWjz4TgEEEAAAQQQQACB+AoQAIyvJ7khgAACCCCAAAJJLTCpqlByszPcMp46d5xM8bQM1B0PvrpZNje2u8dEs7DXTP372hb/BCDzTACwnO6/0fBxDAIIIIAAAgggkFCB7ITmTuYIIIAAAggggAACSSWQY7rjTqoqkne2tdjl0q7BVxwzVb7ywEoxMTw79ZiFHz35jhxqxu+LNrV09kqr+fGmBZMqpCiPj5teE5YRQAABBBBAAIHREOAT2Wioc00EEEAAAQQQQGAUBaqL86ShpUt2tXbZpdAWgKfPGy+PrKh3S7W+oU30Z6iptjxfptcUD/V0zkMAAQQQQAABBBCIowBdgOOISVYIIIAAAggggECqCEyuLjSTc/R1Bf7Agjqp8YwPONx6zJ9QTvff4SJyPgIIIIAAAgggECcBAoBxgiQbBBBAAAEEEEAglQTysrPkgMpCt8i6rl2BTY/gYadsk8lJs2ukjPH/hm1JBggggAACCCCAQDwE6AIcD0XyQAABBBBAAAEEUlBgbGm+7GzplKb2Hrv0Omvvl06bLUvXNEhHt388v2irV5ibLUdNr5I5taWmhSHfNUfrxnEIIIAAAggggEAiBQgAJlKXvBFAAAEEEEAAgSQXmDamWP69cbc7AYgGAfVnuKm8MHe4WXA+AggggAACCCCAQJwE+Fo2TpBkgwACCCCAAAIIpKJAfk6W1Hm6AserDuV0/40XJfkggAACCCCAAALDFiAAOGxCMkAAAQQQQAABBFJboLYsX4rz4tcxJDc7U4rimF9q61J6BBBAAAEEEEBg9AUIAI7+PaAECCCAAAIIIIDAqApkZGTI1DFFYl7iksoLc+KSD5kggAACCCCAAAIIxEeAAGB8HMkFAQQQQAABBBBIaQFtsVdbVhCXOtD9Ny6MZIIAAggggAACCMRNgABg3CjJCAEEEEAAAQQQSG2BuooCKcjNGlYltBVhGeP/DcuQkxFAAAEEEEAAgXgLEACMtyj5IYAAAggggAACKSqQmbmvK/Bwiq9jCWZn8RFzOIaciwACCCCAAAIIxFsgfqM9x7tk5IcAAggggAACCCAw4gKl+TmiLQF3t3UP6dpjSvKGdB4nIYAAAggggAACCCROgABg4mzJGQEEEEAAAQQQSEmBiZWFMrEyJYtOoRFAAAEEEEAAAQTCCNA/IwwKmxBAAAEEEEAAAQQQQAABBBBAAAEEEAiKAAHAoNxJ6oEAAggggAACCCCAAAIIIIAAAggggEAYAQKAYVDYhAACCCCAAAIIIIAAAggggAACCCCAQFAECAAG5U5SDwQQQAABBBBAAAEEEEAAAQQQQAABBMIIEAAMg8ImBBBAAAEEEEAAAQQQQAABBBBAAAEEgiJAADAod5J6IIAAAggggAACCCCAAAIIIIAAAgggEEaAAGAYFDYhgAACCCCAAAIIIIAAAggggAACCCAQFAECgEG5k9QDAQQQQAABBBBAAAEEEEAAAQQQQACBMAIEAMOgsAkBBBBAAAEEEEAAAQQQQAABBBBAAIGgCBAADMqdpB4IIIAAAggggAACCCCAAAIIIIAAAgiEESAAGAaFTQgggAACCCCAAAIIIIAAAggggAACCARFgABgUO4k9UAAAQQQQAABBBBAAAEEEEAAAQQQQCCMAAHAMChsQgABBBBAAAEEEEAAAQQQQAABBBBAICgCBACDciepBwIIIIAAAggggAACCCCAAAIIIIAAAmEECACGQWETAggggAACCCCAAAIIIIAAAggggAACQREgABiUO0k9EEAAAQQQQAABBBBAAAEEEEAAAQQQCCNAADAMCpsQQAABBBBAAAEEEEAAAQQQQAABBBAIigABwKDcSeqBAAIIIIAAAggggAACCCCAAAIIIIBAGAECgGFQ2IQAAggggAACCCCAAAIIIIAAAggggEBQBAgABuVOUg8EEEAAAQQQQAABBBBAAAEEEEAAAQTCCBAADIPCJgQQQAABBBBAAAEEEEAAAQQQQAABBIIiQAAwKHeSeiCAAAIIIIAAAggggAACCCCAAAIIIBBGgABgGBQ2IYAAAggggAACCCCAAAIIIIAAAgggEBQBAoBBuZPUAwEEEEAAAQQQQAABBBBAAAEEEEAAgTACBADDoLAJAQQQQAABBBBAAAEEEEAAAQQQQACBoAgQAAzKnaQeCCCAAAIIIIAAAggggAACCCCAAAIIhBEgABgGhU0IIIAAAggggAACCCCAAAIIIIAAAggERYAAYFDuJPVAAAEEEEAAAQQQQAABBBBAAAEEEEAgjAABwDAobEIAAQQQQAABBBBAAAEEEEAAAQQQQCAoAgQAg3InqQcCCCCAAAIIIIAAAggggAACCCCAAAJhBLLDbGMTAgkX6Onpca9RX1/vLrOAAAIIIIAAAggggAACCCCAAALxE/D+ze39Wzx+VyCnVBAgAJgKdymAZdyxY4dbq8MPP9xdZgEBBBBAAAEEEEAAAQQQQAABBBIjoH+LT548OTGZk2tSC9AFOKlvD4VDAAEEEEAAAQQQQAABBBBAAAEEEEBgeAIZlknDy4KzEYhdoKOjQ1auXGmfOGbMGMnODk5jVG1e7bRqXLZsmYwfPz52IM5AwAjwLPEYxEuAZylekuTDs8QzEC8BnqV4SZIPzxLPQLwEgvwsabdfpxfevHnzJD8/P15s5JNCAsGJuqQQOkUV+xfOwoULA0+hwb+6urrA15MKJl6AZynxxulyBZ6ldLnTia8nz1LijdPlCjxL6XKnE19PnqXEG6fLFYL4LNHtN12e3sj1pAtwZBv2IIAAAggggAACCCCAAAIIIIAAAgggkPICBABT/hZSAQQQQAABBBBAAAEEEEAAAQQQQAABBCILEACMbMMeBBBAAAEEEEAAAQQQQAABBBBAAAEEUl6AAGDK30IqgAACCCCAAAIIIIAAAggggAACCCCAQGQBAoCRbdiDAAIIIIAAAggggAACCCCAAAIIIIBAygsQAEz5W0gFEEAAAQQQQAABBBBAAAEEEEAAAQQQiCxAADCyDXsQQAABBBBAAAEEEEAAAQQQQAABBBBIeYEMy6SUrwUVQAABBBBAAAEEEEAAAQQQQAABBBBAAIGwArQADMvCRgQQQAABBBBAAAEEEEAAAQQQQAABBIIhQAAwGPeRWiCAAAIIIIAAAggggAACCCCAAAIIIBBWgABgWBY2IoAAAggggAACCCCAAAIIIIAAAgggEAwBAoDBuI/UAgEEEEAAAQQQQAABBBBAAAEEEEAAgbACBADDsrARAQQQQAABBBBAAAEEEEAAAQQQQACBYAgQAAzGfaQWCCCAAAIIIIAAAggggAACCCCAAAIIhBUgABiWhY0IIIAAAggggAACCCCAAAIIIIAAAggEQ4AAYDDuI7VAAAEEEEAAAQQQQAABBBBAAAEEEEAgrAABwLAsbEQAAQQQQAABBBBAAAEEEEAAAQQQQCAYAgQAg3EfqQUCCCCAAAIIIIAAAggggAACCCCAAAJhBQgAhmVhY5AElixZIl/72tfkjDPOkNmzZ0t1dbXk5ORIRUWFLFiwQK699lp56623oq7y+vXr7XNmzZolRUVFUllZKQsXLpRbbrlF2traos7n+eefl4997GMyadIkyc/Pl3Hjxsn73vc++f3vfx91HnqgHn/KKafY52s+mp/mu3Tp0qjz0XJ/5zvfseuh9dF6af3URutL2iewbt06+dGPfiTnn3++zJgxQwoLC+17V1dXJ+ecc47ce++90tPTEzXXa6+9JldddZVMmzZNCgoKZMyYMXLMMcfIz372s5jy+ctf/iLnnnuuaDny8vLsV13X7dEmLbdeV6+v5dDyaLm0fK+//nq02cjOnTvlxhtvlPnz50tpaan9o8u6raGhIep8gn5gS0uLPP300/Ld735XPvjBD8qUKVMkIyPD/pk8eXLM1edZipmME4YgEK/3vyFcmlOGKLB9+3Z55JFH7N/Bp512mv0ZyPldc8kll8ScaxDfb+L1+zNmzBQ74aWXXpKvf/3r9mdO5/NGcXGxzJw5Uy699FJ59tlnY6oRz1JMXIE5uKmpyf68rH9jHHfccTJ9+nQpKyuT3NxcqampkeOPP97+myTaz4xB/HuK99rAPO7JWRGLhECABbq7uy3zP2/QHxMQtL71rW8NKvHQQw9ZJqgRMT/zIch65513Bs3nq1/9qpWZmRkxHxOstNrb2wfMxwTtrNNPPz1iHpq/CXwOmIfu1PKaYFbEfLS+Dz/88KD5BP2Ar3zlK5b5oymik/OcmWCwZd64B+W4/fbbLfNhJ2J+hx9+uLVjx44B8+nt7bU+8YlPRMxDy3T55ZdbetxASa+j5XbqEPpqgorWHXfcMVAW9r4XXnjBMoHsiPmMHz/eevHFFwfNJx0OMB9wIzqZIH5MBDxLMXFx8BAF4vX+N8TLc9oQBUJ/n3vXL7744qhzDer7TTx+f0aNmMIHmi8HI75neZ+piy66yOrs7BywpjxLA/IEfufjjz8e1bNkGmxYf/3rXwf0COLfU7zXDnjL2RkHAYlDHmSBQNIKaADQfKtknX322dY3v/lNy7TQsp566ilr+fLl1oMPPmhdc8019n7nw8tPf/rTiHV5+eWXLdMqyn7TMt94WjfddJNlvnWynnjiCeuKK65w38w0CGi+3YqYj2ll5R5rWlhZd955p7Vs2TLrgQcesE444QR334c//OGIeeiOCy+80D1Wz9PzNR/NT/N16vTzn/88Yj5aTi2vc6zWQ+uj9dL6aT11n2npZr3yyisR80mHHU6gzbSOtEwLS+uuu+6yzLfdlvlG3Pr1r3/tC6BpQLW5uTkii2mV6gaAx44da/3whz+0A2Pm23DrvPPOc+/H0UcfbZmWeRHz+eIXv+gee+ihh1qmNaj9DOirrjv39Utf+lLEPDR/vY5zrF5fy6GBOi2X+TbW3qcB5UcffTRiPhs2bLBMy0H72OzsbOv666+3TAs3+0eXdZteQ/PbuHFjxHzSZYf51ts1N61uLdOK1/3/FksAkGeJZ2kk/s/E6/1vJMrKNfwCzu92fT3ggAPs3zXOtlgCgEF8v4nX70+/eDDXnM+VtbW11tVXX23dd9999ucN09vEuu2226wJEya472mDfX7lWYr8uS6YT4+/VhoAnDhxoqXB4h/84AfW/fffb+lz9Nxzz1l/+MMfrAsuuMDKysqynyf9ovzVV1/1Z7B/LYh/T/FeG/ZWszHOAgQA4wxKdsknMFAARUu7Zs0ay3QHtt9oNIAR6Xjn208NZGiALDSZLrTuhx/9RipcMs3Z3YCjfhAPbeGl1z7rrLPcfJ588slw2dhBOucDvB4fWmbNV/PXY8rLy61du3aFzeeGG25wr6XlD036ZuwEbjRgkc5Jg1g333xzxOCu3gPTldP1/O///u+wXF1dXdbUqVPt47R15erVq/sd9+lPf9rN5y4TaAyXTLd1994cdthhlrYI9abW1lZLt+szoPcwUstUDRg7z5JeNzTpeU6rV9NNw9Kgerj08Y9/3M3nj3/8Y79D9EOdc51Y/ujsl1FANmhg/ne/+53vvmjgT42iDQDyLInFszQy/yHi8f43MiXlKqECZvgFuxX/1q1b7V1r166N+XdxEN9v4vX7M9Q7qOvaM0Xfx0M/bzr11c+d3i+U9cv2cIlnSaxIn+vCeQVxW6RnyFvXP//5z+7vKTOkjXeXvRzUv6d4r+13q9mQAAECgAlAJcvUEzDjnLlvNGYsmH4V0NZQTvBCjw2XtEuDGWPQPk6DbvrhMjRpAMnJR1tphUvaOsr55ku7+IZLZhwfOx8N7ERqTaX5O9cKF9zT8mnrSD1Gyx2pm6jXRlsYkiILmPHv3G698+bNC3ugNxAWqdu5Bu+coPScOXPC5vOpT33Kvb/6zWm4pNudZyBccE/PcZ5ZbYWm1w2XtJxOPuGCe/X19W6LRjOOZbgs7G26T/PR1oR6DskvEGsAkGeJZ8n/BCVmLV7vf4kpHbnGKjCUAGAQ32/i9fszVv8gH6/DxTifFT73uc+FrSrPkliRPteFBUvjjQceeKD9PGlX4NAUxL+neK8NvcusJ0qAAGCiZMk3pQSuu+4690OLdukMTdqF0vlQo+OcRUreQMnf/va3foctXrzYzkdbVA00RooTKNGx10K7E+u6M3bcqaee2u8azgbN32m5pdcNTVo+p07f/va3Q3e7694g0kBdSd0T0nzBaXWn3abDJe0a47gPFATzBl71G3Nv2rt3r6XdcDQfM1mLd1e/ZecDlHbP0fO8SfN1yvLJT37Su8u3rOV0jgvXtUdbszn7tZt9pOQNSg/UNT3S+UHfHmsAkGdp3/iuPEuJ/Z8Rr/e/xJaS3KMViDUAGNT3m3j8/ozWPF2OM5NbuZ8Fwn2BzbN0lesT+rkuXZ6RWOrpfJ7W4YhCUxD/nuK9NvQus54oAWYBNn+1ktJbwEy2IWY8QBvBtEyyZzMLFXFmNtPZcXXm4EjJdJN1d5nus+6yLpgWd2Ja0NnbzBuXPduV7wDPipOPCeKJzrrmTWb8Qjsv3eYc593vLOtsWosWLbJX9RzTddPZZb86ddKVgfIxb8D2bLd6XGiddBvJL6D3TJNpxenfsX/NcTeBOXvm5rAHmY3eexLqbv6Aky1bttineo8Ll5ezf/PmzaKzGHuTUxbd5hzn3e8s6wzVpmuPvRpaFt0YbT7ea4TLx7ker9EJOO48S/7ftdHpcVS0As5zNpz3v2ivxXHJJxDU9xvnuR7O78/ku1ujWyLn84+WItxnIJ6lyH8jjO6dS76rmwCpmLH/7IKZL7p9BQzq31PO7yTea323m5UECBAATAAqWSa/gAbDzKQF9jT0Rx55pJhxzuxCX3bZZVJSUtKvAm+++aa9TaeqN91u++13NnjfpJxznH1vv/22mG629qr3OGe/99W7PzSfN954wz3Ue5y70bPg7Dfjbbh1dHZHm4/WV+utKbQsTl687hPYvn27a2S61vZjMd+Oi+mybW937k2/g/Zv8O4PdY/23mlW8c5Hy2+6CvuK7ZTHdCkfMKhpZgEW0yrVPje0Tr4MWRlUgGeJZ2nQhyROBzj/V4fz/henopDNKAg4v9/10t73k3BF8e53nhvnuKHkk6j3m3j9/nTqxus+ATPun0sR7jPQUJ4BzZBnyWUN9IIZy9r+W8VMKmN/Ka1/u2j6/Oc/76t3UP+ecp5z3mt9t5uVBAgQAEwAKlkmp4C2fsrIyLB/tHWc6W4npguI+w2T6XYrt956a7/Cd3R0iBnbzd5eV1fXb793gxm3TfSbG01OoMfZv2nTJmdRBsvHzI7lHpvofLS8ZsxC93rhFpzymEGexfsNb7hj03nbLbfcIs4HFjMhSD+KZH0GtKDRPpOmObp466HnOuuD5aHHOs9S6HOt+0jRCzjmesZg7o65HhvqPpr58CzpHUnuFK/3v+SuJaUbSCCIvyPiVaeB3NJtn+neK2Y4GbfayfYZKFHvNzxL7i0f0sLdd9/t/m2mf49ob5Nrr71Wtm3bZudnZoyWj3zkI76842Ue73yG8/cU77W+W8xKggUIACYYmOyTX8AMLitmMGhZsmSJ2zrJW+rm5mZ31YxD4S5HWnACgPoNszfFko+Th56f6HxiqVO48njrmM7LZvBe+f73v28TaEDGDHTdjyNZnwEt6GDPQTTP5GB56HWcfEKfa91Hil6AZ4lnKfqnZehHxvKc6VX4/z1062Q9M5ZnwLn/WpfQ3/Hxzmc47zfxKkuy3rPRKNf3vvc9d5ib8847L+xwOfFyj3c+PEuj8cQMfM1DDjnEfp7M2Op2gNB7dLzvv+Y92DMQze+2wfLQ60TKJ5Y6efMJ/T2r+0gIDCYQuS/jYGeyH4EUEzCTIMjKlSvtUmsrLR0T7a9//avceeedYiZAkHfffVfMAKz9aqXfyjhJWw4OlszEHfYhOragN8WSj5OHnp/ofGKpU7jyeOuYrsv6TeUHPvABu/WftjL91a9+5Y6b6DVJ1mdAyzjYcxDNMzlYHnodJ5/Q51r3kaIX4FniWYr+aRn6kbE8Z3oV/n8P3TpZz4zlGXDuv9Yl9Hd8vPMZzvtNvMqSrPdspMulXX+1pZammpoa+elPfxq2CPFyj3c+PEthb9eIbDznnHNExxrXpL8z9G+xP/7xj/LnP//Z7qWlX6yfeeaZvrLE+/5r5oM9A9H8bhssD71OpHxiqZM3n9Dfs7qPhMBgArQAHEyI/SMi4HTNHc6rNiMfKOXk5MjcuXPtH/1m6YwzzpAf/ehHYmb1tb9d+vKXvyw6BmBoys/PdzfpwLODJaeLbEFBge/QWPJx8tAMEp1PLHUKVx5fJZNgZTjPkHPuYM+St5r6rZ0+S05XAu0Cc+KJJ3oPcZeT9RnQAg72HETzTA6Wh17HySf0udZ9yZac52E4r7E8S7HUn2cptZ6lWO5tMh0by3Om5U6l/9/J5JzMZYnlGXDuv9Yn9Hd8vPMZzvtNvMqSzPdtpMr2+uuvy7nnnmt/Aaqu//d//2cHAcNdP17u8c6HZync3RqZbToEkfO32cKFC+XCCy+U+++/X+655x5Zs2aNnH322RL6OSre919rOtgzEM3vtsHy0OtEyieWOnnzCf09q/tICAwmQABwMCH2B15g/vz58o1vfMOu51133SWPPfaYr87eSUGiaWrtTJAQ2hQ8lnycPLQgic4nljqFK48PK81W9Bs7/XDyr3/9y675ddddJ9dff31EhWR9BrTAgz0H0TyTg+Wh13HyCX2udR8pegGeJZ6l6J+WoR8Zy3OmV+H/99Ctk/XMWJ4B5/5rXUJ/x8c7n+G838SrLMl6z0aqXDqr7ymnnCKNjY32rL/33nuvHHvssREvHy/3eOfDsxTxlo3ajo9//ONywQUXiI4t+dnPflZ27drlliXe918zHuwZiOZ322B56HUi5RNLnbz5hP6e1X0kBAYToAvwYELsHxEBZ+aj4VxMZxgdatIgzqc//Wn79Pvuu8/+QOPkpd/KVFVVSUNDg9vKy9kX+qofgpxf7t6B9/U470D9Tmux0POdde9A/YPl4zSdd871vg6Wj45bp+XdvXv3gBOBOPmMGTPG13zde61kWR6pZ0m7kesg108++aRd9csvv1x0EpCBknZDd1I8nwEnz3Cvzr3TfYM9SzoeZqTk5KOt4bzPsh6v69oNerA66bFOPqFl0X3JlkbqWRpKvXmWUutZGso9ToZz4vX+lwx1oQxDE/D+vh/sd7zz+12vFPo7PjSf0Xy/idfvz6GJBuOsLVu2yHvf+17RV/1c8Mtf/tL+QnSg2oU+AwMdy7M0kE567NO/zbQ7sP6dokM2OZOBJOo5Gs2/p3ivTY9nOllqSQAwWe5Empdj1qxZoyqggS0nrV+/3ll0X+fMmSPPPPOMrF692u7mkJ0d/r/OqlWr3HNmz57tLuuCzmyVlZUlvb294j3Od9D+Fe/+0Hy0LE7yHuds8746+7W8M2bM8O4SzedPf/qTvU2PW7RokW+/s6KBLh2TQ1NoWZxjkul1JJ4l/UZSv518+OGH7ap/6EMfkp///OeDMug3fPpHkX6wde5NpJO8+0Pdh/IM6HUGy0e7xkdKTnm0/N5BjPV4LY+2gtyzZ49s3bpVxo0bFzab+vp6aWpqsveFliXsCaO8cSSepaFWkWcptZ6lod7nZDhP/38P9/0vGepBGYYmEMT3m3j9/hyaaOqftXPnTjn55JPtLppaGx1O56KLLhq0YjxLkf9GGBQvDQ+I9LdZUP+e4r02DR/yUaoyXYBHCZ7LJpeATgjipHDNqY8++mh7t34L5XT3dI73vupAyE466qijnEX7VQeHPfzww+3lpUuXDjjehJOPDhYb+o2UjpHhDDTrHOe70P4VHYtCxzfUpOfoGIje5NRJtw2Uz0svveS2agytkze/dFq+6qqrRLu6aDrrrLPkN7/5jWRmRvfr1HF/66237GBZJDfvPQl1nzJlitTW1tqneo8Ll9fTTz9tb9YWD5MnT/Yd4pRFNw6Ujwb13n77bfvc0LLoxmjz8V4jXD72BfgnagHHnWfJ/7s2akAOjErAec6G8/4X1YU4KCkFgvp+4zzXw/n9mZQ3LMGF0i/63ve+98kbb7xhX0nHPf7MZz4T1VV5liL/jRAVYJodFOlvs6D+PeX8TuK9Ns0e9NGorkVCAAHrO9/5jmX+/9k/X/3qV/uJmK6y7n4T/Om3XzeYln2WadVkH2cGtbVMAK7fcTfffLObz+9///t++3WDaR1mmZaC9nGnn3562GNOO+00e79p2WcfH+4gzd+pk9YvNJmBaK2ysjL7GC23VP2BuAAAHHJJREFUadUWeoi9rvV18lm2bFnYY9Jp4zXXXON6nHTSSZYZBzCm6v/hD39wz//Wt74V9lzz5m9VVFTYx5lvBMMe86lPfcrNxwSUwx6j2517Z7q4hz3GeWYrKystvW64pOV08jHdMfodYlr2WSYAah9j/jDot9/ZoPs0Hz1WzyH5BSZNmmT76Gs0iWeJZyma52S4x8Tr/W+45eD8+AiYcdvc3+cXX3xxVJkG8f0mXr8/owIMyEH6GcF8eec+P//1X/8Vc814lsSK9LkuZsyAn6B/AzmfPc1wO77aBvHvKd5rfbeYlQQKSALzJmsERl3ATCNvmfFJBiyHaZVkmVZ/9puMBtRMV8ewxx9zzDHuMc8//3y/YwYLIuoJZhxBN+imf+SbbhS+fEx3W8u0KIv4hucc/MQTT7jHvP/977f0PG/asWOHdcABB9jHaDDSDJ7r3e0u33DDDW4+4YKEWk810Tfg4447zj0vXRc0OOx8GDnyyCMtM+BvzBQaGJ46daqdT2lpqWW6lffLQ4N1znXMxDT99usG02rBDRSbVqJWW1ub7zhd1+2aj95D04LPt99ZufPOO91rmW/xnc3uq5ZPy6n5TJ8+3eru7nb3eRdMl2g3HzMLoHeXvayBQ6dO0f7R2S+TgG+INQDIsyQWz9LI/KeIx/vfyJSUqwwmMJQAYBDfb+L1+3Mw76Ds1y+NzYQf7vv41VdfPaSq8SyJFelz3ZBAU/AkrX97e/uAJb/tttvcZ820HO33d05Q/57ivXbAx4KdcRIgABgnSLJJTgH949A0FbfOPfdc68c//rGl3yC98sorlukaa/32t7+1zHTzbsslDU58/etfj1iRl19+2TLTrdtvSBow/OY3v2lpC6t//OMf1pVXXum+UZmxKSwzzlnEfH72s5+5x06bNs0yAydby5cvtx588EHrhBNOcPd9+MMfjpiH7tCyOwEVPU/P13w0P83X2WfGpouYj5ZTy+scq/XQ+mi9tH5OYFTrrW7pnH74wx+6TqY7rfXss89aK1euHPAnXCtQNVyyZIn73I0dO9Yy4+dY+s2fGeTYOv/8893rmO4A/T70eO/BF7/4RffYQw891DLdku1nQF913bmvX/rSl7yn+ZY1eOz9Rl+vr+XQ8mi5ampq7Hy01d6jjz7qO9e7smHDBsuM12IfqwHHL3zhC5YZN8z+0WUnkKzHaCvXdE/vvPOO/UeAfhB2fsxkQ7afvjrbnNdILSZ5lniWRuL/Urze/0airFzDL6C/h53fI/pqJqty3xv0d793ny5HSkF8v4nX789IZkHaft5557nPzYknnmitWLFiwM8/GuiLlHiWeiLRpMV2/bJTe51cccUV1q9+9Sv78/Srr75qf178yU9+4vtMqn/DPf7442Fdgvj3FO+1YW81G+MsQAAwzqBkl1wCGgB0giADvWqA69Zbbx208A899JDbGipcfhpM0z/sB0s33nijZWZNi1g2bfY+2Ldj2sLL2zw+tDwasAnXnTm0bFpeM0FIxLJo6y8z2UXoaWm3ri0gQ40HW9eWFpHS7bffbgenI+Vhxou0tCXnQEm7nV922WUDlusTn/iE3T19oHz0OmacyIj5mLEorTvuuGOgLOx9Glg3E4BEzEf36TEky/6jO9K9D7c9tPuL15BnyavBcqIE4vX+l6jykW94gWg/Bzm/d8Lnsm+YkyC+38Tj92cksyBtd56PaF8HGsoiqJ9deJaie+Kd3g6DPUtmtl/rscceGzDTIP49xXvtgLecnXEQIAAYB0SySF6Bbdu22S3iLrnkErs7pJnB1NJghgb8tBWXdmcwAxgP2k3YW8N169ZZOg6cBvsKCwst7WKrXS11PIpIY6h5z3eWn3vuOctMaW9pmfQbLm1pZWZVs373u985h0T1qi0Z9Tw9X/PR/DTfcN2UI2WoXVm1/FoPrY/W68ADD7TrqfUlWXYX6ME+rITuHygAqKbaglC/AdUuwfn5+Za2/NJWfz/96U8jdrUNdy+0FcPZZ59tmYlB7GdAX3V9oBZ7oflo11795lWvr+XQ8mi5tHyvvfZa6OER1zWY+JWvfMWaO3eu3YJUW5HOmzfP3hba5T1iJmmwQ1vahD4vA60PFABULp6lNHhokqCK8Xr/S4KqpE0R4hUAdMCC+H4Tr9+fjlEQXwd6fwq3b6AAoOPDs+RIpNerDrWkjS60Ven8+fMt7QmjvUTM7Nx2DybtiaKfkaL9myqIf0/xXpte/ydGurYZekHzi5uEAAIIIIAAAggggAACCCCAAAIIIIAAAgEUyAxgnagSAggggAACCCCAAAIIIIAAAggggAACCOwXIADIo4AAAggggAACCCCAAAIIIIAAAggggECABQgABvjmUjUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIMIIAAAggggAACCCCAAAIIIIAAAgggEGABAoABvrlUDQEEEEAAAQQQQAABBBBAAAEEEEAAAQKAPAMIIIAAAggggAACCCCAAAIIIIAAAggEWIAAYIBvLlVDAAEEEEAAAQQQQAABBBBAAAEEEECAACDPAAIIIIAAAggggAACCCCAAAIIIIAAAgEWIAAY4JtL1RBAAAEEEEAAAQQQQAABBBBAAAEEECAAyDOAAAIIIIAAAggggAACCCCAAAIIIIBAgAUIAAb45lI1BBBAAAEEEEAAAQQQQAABBBBAAAEECADyDCCAAAIIIIAAAggggAACCCCAAAIIIBBgAQKAAb65VA0BBBBAAAEEEEAAAQQQQAABBBBAAAECgDwDCCCAAAIIIIAAAggggAACCCCAAAIIBFiAAGCAby5VQwABBBBAAAEEEEAAAQQQQAABBBBAgAAgzwACCCCAAAIIIIAAAggggAACCCCAAAIBFiAAGOCbS9UQQAABBBBAYOgCkydPloyMDLnkkkuGnkmSnNnQ0CCVlZV2fZYvXx63Ut199912nuq0bt26uOUb5Iwsy5J58+bZbnfddVeQq0rdEEAAAQQQQCCJBAgAJtHNoCgIIIAAAggggEAiBG688UZpbGyU008/XRYuXJiIS5BnlAIaLP2v//ov+2h9bW1tjfJMDkMAAQQQQAABBIYuQABw6HaciQACCCCAAAIIJL3A+vXr5Y477rDLqYFA0ugLfPCDH5QDDzxQ6uvr5X//939Hv0CUAAEEEEAAAQQCL0AAMPC3mAoigAACCCCAwFAEtEurdtfUbq6pnG6++Wbp7u6Wo446So444ohUrkpgyp6ZmSnXXHONXZ/vfve70tHREZi6UREEEEAAAQQQSE4BAoDJeV8oFQIIIIAAAgggMGyB3bt3yz333GPn87GPfWzY+ZFB/AQuuOACycnJkR07dsi9994bv4zJCQEEEEAAAQQQCCNAADAMCpsQQAABBBBAAIEgCGhgSceY00CTBpxIySOgk7KceuqpdoHuvPPO5CkYJUEAAQQQQACBQAoQAAzkbaVSCCCAAAIIIOAIbNmyRb74xS/Ke97zHikrK7ODYWPHjrVnYv3whz9sd/FtampyDndfI80CrF2DdSKHaH+OP/54N8/QhSeffFIuvvhimTp1qhQWFkppaaldrv/8z/8ULfdw0x//+Ec7Cy1DVVXVgNn94x//EPWYMmWKFBQU2OWZNGmSLFq0SK677jrR/YOlvXv3yu233y5HHnmkVFRUSFFRkcyfP19uuukmaWtri3i6nqf563W0q3J1dbV9n8rLy+WQQw6xt2/YsCHi+bpD66j3RF81vfPOO/LZz35WZsyYYddF94WbqXj16tV2d1ydmVefD6273g+d/fmll16y84r0j3bd/eEPf2hfc8yYMXaZNbCn4/uddtppctttt4W9ppPf+eefby8+99xzsnHjRmczrwgggAACCCCAQPwFzNg2JAQQQAABBBBAIJACTz/9tGWCapb5BDXgz8MPP9yv/ib4ZZ9jAnS+fWvXrh0wr9BrHXfccb7zdaW9vd268MILB8zHBM+shx56qN+50W4wwSkrLy/PvsYNN9ww4Gmf//znByyL1skEEPvlcdddd7nnvf7669ZJJ53kroc6HH744VZLS0u/PHTDV7/61YjnOfmYAKl1//33hz1fN6qzHquvDzzwgKV+zrnOq947b7rlllss0zqy33HO8SZoaEWyMwFaa86cORHPdfK49tprvZf0La9atco93wROfftYQQABBBBAAAEE4imQbT6ckBBAAAEEEEAAgcAJdHZ2igmyibbuKykpkU996lNywgknSE1NjXR1dYkJBsnzzz8vf/7zn2Oq+4QJE2TlypUDnqMt7/7nf/7HPkZb0XmT+SAnH/jAB2TJkiX25rPOOkt0VlhtdaaTQyxbtkxuvfVW0RZvepy2DjvssMO8WUS1vHz5clEDTQsXLox4ziOPPCLf//737f3aWk+dZs+ebbeG0zEETWBP/v73v9vlipiJ2XHFFVfICy+8YLdo1PqMGzfOrsN3vvMdWbp0qX3+N77xDfnWt77VL5uenh4ZP368nHvuubJ48WLbIj8/324Vp/foJz/5iZjgoXzkIx+Rl19+2S5fv0z2b1A3He9QW1Sa4J0cc8wxkpWVJepRXFzsnmaCf3L99dfb6069tbWgtjp866235Mc//rFdbr2P2iLxP/7jP9xzdeFzn/ucvPHGG/Y2vd55550ntbW19rV0dl9tPfjggw/6zgldmTlzpn09dX7qqadsw9BjWEcAAQQQQAABBOIiEM9oInkhgAACCCCAAALJIvDEE0+4ravCtfBzymlmyLX27NnjrLqvkVoAugdEWDCBJst0I7WvbQJp/fLWll7mQ5zd8uwvf/lL2Fx27dplHXTQQfZxpkts2GMG22hm/3Xrb7qXRjz84x//uH2c1re5uTnicQ0NDf32eVsAap1+/etf9ztGWyLOnTvXvoa2IlTv0KQt80xQNnSzu67lN4FXOw8TbHO3execFoBaDhOIs9avX+/d7VvW1opOyz9tfWi6IPv260pvb6+l19L8TODQ0nviJG3B6Zw/UAs/PT6cm5OPvpqgtH2NWbNmeTezjAACCCCAAAIIxFUg03yoISGAAAIIIIAAAoET2Lp1q1unY4891l0OXcjOzrbH3gvdPpR1Hbfv7LPPFhMgEh0LzgQefXmbT3FiAnN21tqizJkEIvRaOn6etlDTpC0AdTy7WNOmTZvcU7TVY6TkOOkYid4WcqHHa30GStoCTlvChSbTDdkei0+3m2CY22rOe5yOt6gTlURKdXV1ouMiajLdokUdB0rf/va35YADDoh4iLawNIFIu2WlCQDaYweGHqytMX/0ox+Jll9bH953333uISYYaJ+vGwZ6tnT/YG7OvdEWqYPVS/MjIYAAAggggAACQxEgADgUNc5BAAEEEEAAgaQX0C6lTjIt1ZzFhL1q0O+cc86xJ+/QoKIGjKZNm+a7nnYZfffdd+1t2r13oOQNLGkX2ljTjh077FO0K2xubm7E0x0nM16iW7aIBw+w46Mf/WjEvQsWLHD3rVmzxl2OtKDdtjUgpt2PX3vtNftH66HJ2RfpXK3rYDMea2BWk07CoZODREraHVgnB9HkvQc6oYpjalo9inZhHmpyAoTaXVu7ApMQQAABBBBAAIFECBAATIQqeSKAAAIIIIDAqAscffTR9lhyWhAzyYWYSSjs8ee0RZ2OARjvdNlll9njzGm+OjOsjjcYmryzyupYdxp8ivTjbY3ntNILzW+gdW2lpklbEw6ULrroInu3ts4zXXXtcRM1YKqz48aSTBfWiIc7QS49wHQz/v/t3VuITW8Yx/H3f6WIK6KERBTlECUJESaHFGFuHcexpCbiQrmQlKaQwyRCGSSHJM04nwoXTiWnC+QYkRuj5MJ//57+7/zXzKy1Zu+11zazxvet3R5rr/Wud32Wq6fnfZ7Q83Jbdq2unrIB1Y1XNRG1HgXg9KmoqGi47suXLw1/N/1DdfxUPzBq6D4+OLp+/fpIf/9e/DsLvgNlBZaXl9stFOjt37+/1RM8f/58wUG84Pupr6+PWjbHEUAAAQQQQACBogQIABbFx8UIIIAAAggg0FYFtKVUmV5qaKGhJhAbNmxwCgwqs0vbb2tqalyu1lvRj6BGEceOHbN5VqxYYY00wib9/Plz2OEWj/348aPFc5qe4INgykyMG7nOvdbwIle30OXq9bnjx487BTMVSNPW22XLlrlHjx7FTWG/+Qy9sBO1ndaPMO9cLUSX66hr61CArqUR90zBgFrYPGm9AzUJUQMXDa1ZW7anT5/ulB2opiv6d662ZNgSGh0LPkvcNuhGF/EPBBBAAAEEEECgQAG6ABcIxukIIIAAAgggkB0BBZXUsVeBQH20zVWZbQq61NXV2aeqqsopc8vXYiv06U6ePOlUR05DwbTt27dHThEMfmk9ynbLZyRZW7du3WxqbStVbbm4ra4rV660bbMKiF68eNHqDip49f79e1ddXe1yjUsseKouvmkPZfOpu6+CnMp6rKysdGVlZbZ9WpmAfqvtlStXzFf3j6uVp46/cSP4DjZu3NjidmE/V6dOnfyf9t2lSxerR6iuzer6fO3aNffw4UMLKCtrUJ9t27a5M2fOWGfjRhcH/uEzNXVIz8tAAAEEEEAAAQRKIUAAsBSqzIkAAggggAACbUZAASHV5tNH4+PHj662ttbt2rXL3bt3zz5Lly51p0+fLnjNDx48cNpCq4CUtoEqEKT6f1FD2WF+KAtRW1xLNXwAMNfh1jLRdL+4oSCjtkrro2sUzJKJMt0URNy8ebNltqnJSZpDW2h97Tvdb9KkSaHTBwNloSfkeTD4DpRxV+w70NZyfTS0vVmBwIMHD7pTp045ZRuqzqDqPirDMmx8+/bNDsvfZ22GnccxBBBAAAEEEECgGIH/92MUMwvXIoAAAggggAACGRFQ04sFCxZYUwd1vtU4d+6cZQUW8giqCadgmDLXlLmljL5grbuwuYYPH95wWLUISzl88wrd48WLFwXdSlt2ZaOtzZcvX264VgHOtIcafWjILir4p999LT79XcxQbUGfaZf2O+jcubNtC1ZWqLo8ayjgfOvWrcgl+3czePDgyHP4AQEEEEAAAQQQKFaAAGCxglyPAAIIIIAAApkUUPbX+PHjbe3q4uqz0PJ5GNXKU0bh27dvnTIMVf8vrgmGn1NBNdXV09C2Ws1TqjF27NiGqVX/MOnQmn1dvbjmG0nn9x10ZaHMw7ChIKu67aYx9L6mTZtmU124cME9ffo0jWmbzaHt4H5Euamj8fPnz+20UaNG+dP5RgABBBBAAAEEUhcgAJg6KRMigAACCCCAQFsQuHnzZmwnW3UCvn79ui1Vtef8ltl81r548WJ39+5dO1XNHtRQJJ+hzDo1ItF4+fKlbR/++fNn5KUKEGkLbpLRq1cv16dPH7tUdeqihpp+BBtRND1PmXd+m2rfvn2b/lz0v9VsRENBvrAMQ9Xsk/eHDx+KvpefQN1/FQhUwHHOnDnu3bt3/qdm37r/kSNHGp2jd+f/7zS74L8DCi76EeUmW1/PcMqUKf50vhFAAAEEEEAAgdQFoovUpH4rJkQAAQQQQAABBP6cgLauagurMuHUnXXIkCEW5FOwS9su9+7d6+7fv28LWrRoUWztvuCqDxw4YAEhHZs4caKbPHmye/z4cfCURn+reUQwAKSuumq0oXp3J06csDWoBqHqyGlrqoJ+z549s1pyZ8+etbpwq1atajRnvv/QFuUdO3a4q1evRjYCWbdunXX61bnjxo1zAwYMcFrz169fbevqzp077XYKmCkQl/aYN2+eBUUVCNXWbNUelKkstD1Y91etxjFjxlhzkjTur+3RatCxZs0a9+TJE6sDWFFRYe+ze/fulpn5+vVr2yauGoXaxqtmMj57882bN27ChAnWuXjWrFlu5MiRrmfPnrY0ZYUqqOqDmcOGDXNR2X1+e3XXrl2tO3Uaz8YcCCCAAAIIIIBAmAABwDAVjiGAAAIIIIBAuxBQhpcyteKytRT42rJlS97Pq+CPH+pMG6y1548Hv7XNWI0h/FA3XgWIVq9ebUFINYhYu3at/7nZd5IOwH6SJUuWWABQQSllRCrAFza0/fnQoUP2Cfu9Q4cOtlYFutIeCqrt2bPHgovaBrx161b7BO9TXl7u9CxxNQKD5+fzt5qdKNCpb3U8VianPmFDnYjDGnQoeKhP1NC2cDUDierAfPToUbtUz6ct6QwEEEAAAQQQQKBUAgQASyXLvAgggAACCCDQqgKVlZWW9Xfp0iWnbr3aQqqurBo9evSwjDt18FV24J8eCvbs3r3bLV++3O3bt88ChAosfv/+3Wk7sjIGR4wY4aZOnepmzJiReHnqcDt69GjLZKupqQkNACo7UA1Mbty4YZmRam6iLb8dO3Z0/fr1c6plp3WqeUaphjL/Bg4caAE4NeZQQFJZcUOHDrWsQGUJBoOoaa1DQcWZM2e66upqpy27qseneyvgqYw+BXeVjahOvlqPH8oq1Xrq6urcnTt3rBbkp0+fLHNQzUy07tmzZ7v58+fbXP664Pft27fdq1ev7JB8GQgggAACCCCAQCkF/snVHfldyhswNwIIIIAAAggggEDrCWgrqjLM1MhDQUYFGBmtL6Dt1Pv373dlZWWutra29RfEChBAAAEEEECgXQvQBKRdv14eDgEEEEAAAQT+doG5c+daNqGy+pI2FPnbDdN+fgViDx8+bNNu2rQp7emZDwEEEEAAAQQQaCZAALAZCQcQQAABBBBAAIH2I6D6c6qrp1FVVeXq6+vbz8Nl9ElUc/LXr19OwdmoBiEZfTSWjQACCCCAAAJtVIAtwG30xbAsBBBAAAEEEEAgTQF101VnX9XTGzRoUJpTM1cBAqq+o4CsGp4sXLjQ9e7du4CrORUBBBBAAAEEEEgmQAAwmRtXIYAAAggggAACCCCAAAIIIIAAAgggkAkBtgBn4jWxSAQQQAABBBBAAAEEEEAAAQQQQAABBJIJEABM5sZVCCCAAAIIIIAAAggggAACCCCAAAIIZEKAAGAmXhOLRAABBBBAAAEEEEAAAQQQQAABBBBAIJkAAcBkblyFAAIIIIAAAggggAACCCCAAAIIIIBAJgQIAGbiNbFIBBBAAAEEEEAAAQQQQAABBBBAAAEEkgkQAEzmxlUIIIAAAggggAACCCCAAAIIIIAAAghkQoAAYCZeE4tEAAEEEEAAAQQQQAABBBBAAAEEEEAgmQABwGRuXIUAAggggAACCCCAAAIIIIAAAggggEAmBAgAZuI1sUgEEEAAAQQQQAABBBBAAAEEEEAAAQSSCRAATObGVQgggAACCCCAAAIIIIAAAggggAACCGRCgABgJl4Ti0QAAQQQQAABBBBAAAEEEEAAAQQQQCCZAAHAZG5chQACCCCAAAIIIIAAAggggAACCCCAQCYECABm4jWxSAQQQAABBBBAAAEEEEAAAQQQQAABBJIJEABM5sZVCCCAAAIIIIAAAggggAACCCCAAAIIZEKAAGAmXhOLRAABBBBAAAEEEEAAAQQQQAABBBBAIJkAAcBkblyFAAIIIIAAAggggAACCCCAAAIIIIBAJgQIAGbiNbFIBBBAAAEEEEAAAQQQQAABBBBAAAEEkgkQAEzmxlUIIIAAAggggAACCCCAAAIIIIAAAghkQoAAYCZeE4tEAAEEEEAAAQQQQAABBBBAAAEEEEAgmQABwGRuXIUAAggggAACCCCAAAIIIIAAAggggEAmBP4FEdD3YsIGakgAAAAASUVORK5CYII=\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div id='79cd5743-2a0d-4666-b540-d155340c048f'></div>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div id='9a53d1ca-baa5-4250-be04-a732c455b9a8'></div>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAQABJREFUeAHs3QmcZFV9KOAzOA4M+74OyiYIiBsMIBDABREBWTQsUVmeiCYuqE+NeSiJuKFBxJjngmBABFF5CVFUJBqWgChCNKCgsjiyyyYzw/Qs3T317v+aW6nqqaqu7q7uqrr1nd+vrVv3nnvuOd+5PTL/OcusSpaSRIAAAQIECBAgQIAAAQIECBAgQIBAKQVWK2WrNIoAAQIECBAgQIAAAQIECBAgQIAAgVxAANCLQIAAAQIECBAgQIAAAQIECBAgQKDEAgKAJe5cTSNAgAABAgQIECBAgAABAgQIECAgAOgdIECAAAECBAgQIECAAAECBAgQIFBiAQHAEneuphEgQIAAAQIECBAgQIAAAQIECBAQAPQOECBAgAABAgQIECBAgAABAgQIECixgABgiTtX0wgQIECAAAECBAgQIECAAAECBAgIAHoHCBAgQIAAAQIECBAgQIAAAQIECJRYQACwxJ2raQQIECBAgAABAgQIECBAgAABAgQEAL0DBAgQIECAAAECBAgQIECAAAECBEosIABY4s7VNAIECBAgQIAAAQIECBAgQIAAAQICgN4BAgQIECBAgAABAgQIECBAgAABAiUWEAAscedqGgECBAgQIECAAAECBAgQIECAAAEBQO8AAQIECBAgQIAAAQIECBAgQIAAgRILCACWuHM1jQABAgQIECBAgAABAgQIECBAgIAAoHeAAAECBAgQIECAAAECBAgQIECAQIkFBABL3LmaRoAAAQIECBAgQIAAAQIECBAgQEAA0DtAgAABAgQIECBAgAABAgQIECBAoMQCAoAl7lxNI0CAAAECBAgQIECAAAECBAgQICAA6B0gQIAAAQIECBAgQIAAAQIECBAgUGIBAcASd66mESBAgAABAgQIECBAgAABAgQIEBAA9A4QIECAAAECBAgQIECAAAECBAgQKLGAAGCJO1fTCBAgQIAAAQIECBAgQIAAAQIECAgAegcIECBAgAABAgQIECBAgAABAgQIlFhAALDEnatpBAgQIECAAAECBAgQIECAAAECBAQAvQMECBAgQIAAAQIECBAgQIAAAQIESiwgAFjiztU0AgQIECBAgAABAgQIECBAgAABAgKA3gECBAgQIECAAAECBAgQIECAAAECJRYQACxx52oaAQIECBAgQIAAAQIECBAgQIAAAQFA7wABAgQIECBAgAABAgQIECBAgACBEgsIAJa4czWNAAECBAgQIECAAAECBAgQIECAgACgd4AAAQIECBAgQIAAAQIECBAgQIBAiQUEAEvcuZpGgAABAgQIECBAgAABAgQIECBAQADQO0CAAAECBAgQIECAAAECBAgQIECgxAICgCXuXE0jQIAAAQIECBAgQIAAAQIECBAgIADoHSBAgAABAgQIECBAgAABAgQIECBQYgEBwBJ3rqYRIECAAAECBAgQIECAAAECBAgQEAD0DhAgQIAAAQIECBAgQIAAAQIECBAosYAAYIk7V9MIECBAgAABAgQIECBAgAABAgQICAB6BwgQIECAAAECBAgQIECAAAECBAiUWEAAsMSdq2kECBAgQIAAAQIECBAgQIAAAQIEBAC9AwQIECBAgAABAgQIECBAgAABAgRKLCAAWOLO1TQCBAgQIECAAAECBAgQIECAAAECAoDeAQIECBAgQIAAAQIECBAgQIAAAQIlFhAALHHnahoBAgQIECBAgAABAgQIECBAgAABAUDvAAECBAgQIECAAAECBAgQIECAAIESCwgAlrhzNY0AAQIECBAgQIAAAQIECBAgQICAAKB3gAABAgQIECBAgAABAgQIECBAgECJBQQAS9y5mkaAAAECBAgQIECAAAECBAgQIEBAANA7QIAAAQIECBAgQIAAAQIECBAgQKDEAgKAJe5cTSNAgAABAgQIECBAgAABAgQIECAgAOgdIECAAAECBAgQIECAAAECBAgQIFBiAQHAEneuphEgQIAAAQIECBAgQIAAAQIECBAQAPQOECBAgAABAgQIECBAgAABAgQIECixgABgiTtX0wgQIECAAAECBAgQIECAAAECBAgIAHoHCBAgQIAAAQIECBAgQIAAAQIECJRYQACwxJ2raQQIECBAgAABAgQIECBAgAABAgQEAL0DBAgQIECAAAECBAgQIECAAAECBEosIABY4s7VNAIECBAgQIAAAQIECBAgQIAAAQICgN4BAgQIECBAgAABAgQIECBAgAABAiUWEAAscedqGgECBAgQIECAAAECBAgQIECAAAEBQO8AAQIECBAgQIAAAQIECBAgQIAAgRILCACWuHM1jQABAgQIECBAgAABAgQIECBAgIAAoHeAAAECBAgQIECAAAECBAgQIECAQIkFBABL3LmaRoAAAQIECBAgQIAAAQIECBAgQEAA0DtAgAABAgQIECBAgAABAgQIECBAoMQCAoAl7lxNI0CAAAECBAgQIECAAAECBAgQICAA6B0gQIAAAQIECBAgQIAAAQIECBAgUGIBAcASd66mESBAgAABAgQIECBAgAABAgQIEBAA9A4QIECAAAECBAgQIECAAAECBAgQKLGAAGCJO1fTCBAgQIAAAQIECBAgQIAAAQIECAgAegcIECBAgAABAgQIECBAgAABAgQIlFhAALDEnatpBAgQIECAwKoCRx55ZJo1a1baYost0pIlS1bNUJIz22yzTTrwwAMn3JovfvGLuU8Y3XDDDRO+3w0ECBAgQIAAAQK9JyAA2Ht9okYECBAgMCACEXz653/+5/T2t7897bHHHulZz3pWWmuttdIaa6yRNt988/SCF7wgnXDCCelzn/tcuu+++9pSiaBPBG4a/ay55pp50GunnXZKL33pS9N73/ve9PWvfz09+uijbZUdmRYvXpy23nrravl/+Zd/OaF7o41F3d761reucu+1115bvR75Op2uuuqq9K//+q95sX/3d3+Xezd6xkMPPZS++c1vpve///3pkEMOSc997nPTeuutl2bPnp3WXnvt9OxnPzsdfvjh6R/+4R/SE0880aiIhuceeeSRdPHFF6c3velNeZ9vtNFG6ZnPfGZaf/3182eceOKJ6Tvf+U5auXJlw/tn4uQpp5ySnvOc5+SPesc73pFGR0en9bE/+tGP8vd8xx13zPtjww03TM9//vPT+973vvTrX/96Qs9+/PHHU/TxRz7ykfSa17wmbbnllnXvU7xfE0n33ntv+upXv5re9a53pVe84hVphx12SOuss07+Hqy77rpp++23T6997WvTV77ylfT0009PpOg874oVK/L34dWvfnX+TsXvfgSm99lnn3T22WenaE+76Y9//GP61re+lf7qr/4qv3/TTTdNc+bMSUU9jzvuuHTJJZek4eHhdousyxd/BsXvzO6775422WSTNHfu3Lz9r3vd69K//Mu/pEqlUpe/1Zdo989+9rP0hS98If2v//W/0m677ZabFn82nHTSSa1ub3ntF7/4RXrnO9+Zv0MbbLBB/udp/L4efPDB6fzzz09DQ0Mt73eRAAECBAiUViD7P2uJAAECBAgQmEGB7C+glU9+8pOV7C/R8Tfmtn+yoEDlyiuvbFnT7C+6bZdXPDsLElSOPfbYyk9+8pOWZRcXow7Fvdlf2CvXXXddcanlZxbwq943b968ysKFC1fJf80111TzxDM6mbJAVmXXXXfNy8+CmJUsCNG0+CJf0c5Wn1mApfL5z3++aVlx4fe//30lC7pWVltttbr2NSs3C/5WbrvttpZljncx3oUDDjhgvGwNr2cBrWo9L7zwwoZ5pnoy+j/eu2YGcT4LjlY+/vGPt/Wol7zkJS3LivLi/ZpIygLy45ZZ1H+zzTarXH755W0Xf+edd1Ze+MIXtiw/C+JVvvvd77YsMwvKVw477LBK/B4XdWn1mf0jQdu/s8WDs8BZZTyLLEBaefjhh4tbmn5m/6Axbl2zQHjT+5tdiD9X475WbY9rWRC38uMf/7hZMc4TIECAAIHSCsyKlmX/ZygRIECAAAECMyCQBYLSEUcckf7rv/6r7mkxoubFL35x2njjjVOM1IuRPw8++GD6z//8zzQyMlKX95xzzknvfve7684VX2IEYDwj0stf/vJ8VFlxLUZyPfXUUylGCmXBpZT9Zb24lH9mwal81NWZZ56Zjxyquzjmyxve8IZ8NFGcjtFi0Z4YEdQsxcirl73sZdVRQlkQMR166KGrZI98MTqxSJ38z5QY/RT1jnTuueem0047rXjMKp/Pe97z0q9+9av8fIzI2nnnnVMWtMz7JkZBxsi06Jva+p1xxhnpwx/+8CplxYksuJqyAFXdtRiVlAWAUozUitGgMSLqrrvuquaJkWYxOm7+/PnVcxM5iHchfsJ0oilGiW277bb5Oxifv/nNb/KRihMtp1n+KP9Vr3pV+vd///dqljCP34Fly5al//iP/6h7P8M1fFul2ne/Wb4sADihadEx2rOYJh79FSNBY3RuvOvxu3T77bdX35N4Zoxgi1FmMaqtVXrggQfSXnvtlWKkaaS4b//9989H1D322GPphz/8YVq6dGl+LUaIxqjG+P1plGJUabyjtSkLRuYjTKOuYR2j4uJ3vkgxkjVG7WWBw+JU088Y3RgjVosUo1WjLjEi9pe//GX+3hbXYuTmjTfemI+SLc6N/YxRhM1+T4q8WSAvZYHn4uu4nzGiMOoUzy5SjP7cb7/98nrG6MXrr7++ahojreP3IkZeSwQIECBAYGAEIgAoESBAgAABAtMvcM8991RiRE/2Hxn5T4ye+/M///PKLbfcUsmmfDaswKJFiyrZX9QrWTCvel82vbFh3jgZo76K8v/pn/6pab64kE1vrJx++umVbBpq9Z64N5vaWonRcq1SFqCsG8GYTddsmj1G5mRTJavPeP3rX980b4zQKuofn51M2TTDvOwswNpw9GHts/7P//k/lWzqZyULHNSerjuO/oxRT0V9Y3TfT3/607o8xZebbropz5cFJSof/OAHK1mgr7hU93nFFVdUsiBwtczoz/AbL8X7k01trhxzzDGVLGBXiTYW9cqmlVa22mqrSrYeYP7sMM6CbOMVWfnbv/3bahlf+9rXxs0/kQwf+tCHqmVH/bKp6HW3L1++vBLvVNGG+F3JAjZ1ecZ+CasYMfiiF72ocuqpp1a+/OUvV7LAV7WMKCvaPpGUTaetZNNqK3/4wx+a3hbPyAKX1eeEfYz4bJX+7M/+rJo/6h1l1KYsCFj3O59Ni65kgfvaLNXjGHUXbcumu1ayoPYqZRUZs6Bq/m4UpjFyNQseFpcbfmaB7ty0uCd+d7OpznV5syB1/uwiz8knn1x3feyX4r2KUbhHH3105ROf+EQlysimEldNJjoCMPsHkeq98Xv493//95XsH07qHh1tzaZaV/PF70QW3K3L4wsBAgQIECizQGf/y7rMUtpGgAABAgSmIBBBnNrpfhEkiGDPRFIElyKI1akAYPHs+ItxBIeKv8DHZ7b2XXG56edll11WvecZz3hGJRvB1jDve97znmq+CIBG8LBZmq4AYDaiqlqHiQYXmtU1zkegKhsVVi07Wz+vYfZsNGElG7lZyUZ1Nbxee/Lmm2+uhGfRH9mmHLWXVznORqLVBSKL+1p9ZmuvrVLO2BMRxIrAW5STjZQae3nS3yOYVjudtFX7aqcIxxTfVikbhdrQt9ZhogHAVs+rvZatA1kXuP3oRz9ae7nuOKb0FnWKabvNpnpHoG277bar5v2bv/mbunKKL/HsbHTkuEHtyP+73/2uEoG/4vkf+MAHimIafsY/UBR5991336b/MPC9732vmi/e3TvuuKNheXFywYIFDQOPtdN3J/I7mo2Urqy++urV57eaMh7T/uNdLtrUqp+aNsAFAgQIECDQpwICgH3acapNgAABAv0lkE17q/6lM/7yGaP6JpMi4JRNu2t6a4wmKv5yO94IwNpCYkRY7Sim+Et8jHAbL2UbLVSfl03/W2VdvVhXsHbduwgatkrTFQA8/vjjq/WMYEUnUwRkC/Nsg4SOFB0j+YoyY0Rmq5RtblDNG/dk0z7zkYAR6IkRh9lGMpVsemQlm7pZzddOADCeWbuuXrNAVau6Nbr2qU99qlqPbOOPpqNf494IQta+P9m060ZFtjxXOMbndAUAowLZNNlqu7KNQZrWqXYU2pvf/Oam+eJCjLws6h+jALPpvC3zt3MxgvtFmfEPCs1S/MNArX02vbZZ1vx87WjYbBOOlnkbXZxsADDe5aI9sa7qeKNbs+nU1fzxuzLeaOdGdXWOAAECBAj0o4BdgLP/YpAIECBAgMB0CsQaYrFbbJGyYFQ68sgji68T+oxdPbMNKiZ0TzuZsxE06dJLL03ZX/jz7LFeYLZRybi3xi6esRZYpFhjLJvOV70n1uWKtdCKHW1j7cNsRFf1+kwdhH822jJ/XKyrF2sjdjLF+o1FivUBO5Gy0VbVYrIRU9XjsQf/9m//ln7wgx9UT3/sYx/Ld4z+xje+kWIH1Fif8aKLLsrXEox1JWNNwXj/Yl25dtJRRx1VzZYFo6rHUzko+iLKiN1eY/27Zil2ja5d+y7WrevV1M57EDsFRx8UKZsuWxw2/IwdhmMdwkhPPvlkvo5dw4wTONnuu/Xtb3+7+rsbOzTHzsStUu3OvbV93OqeTlzLRkZXi4nf7fizrFWK9yl2W46UBTnr1g1sdZ9rBAgQIECg3wUEAPu9B9WfAAECBHpeIFtDLP/Le1HRZht4FNe79bnTTjvVbcwR9S6Cd83qFAvtn3322dXLEYDKpv/l37ORcdXj2DgggoXdSBFwKTZUiOBHBFE7mYr2RpnbZJtudCLVBsUiGNssZev+VS9lI6hStnZh0+BeNqozD6ZFoHe8TSqKQmuDb7Fxy1RTbPARG6IUKZt6Xhw2/azdFKZ205CmN3TpQjvvQbb7bMpG8eY1jI0oxtvgJQJVtZvHdKL97b5b2WjJquRE+yk23bj77rur90/nQTalvFp8NgK6etzsIILf8edWkWoDssU5nwQIECBAoIwCAoBl7FVtIkCAAIGeEqj9i3S2QcO4f+nvZuWzNb+qj4/dgmOXz/FStu5ddZRWMeovdsitHUEYQcKxO5WOV26nrscouSJlmy8Uhx35jJ2CL7jggmpZ2UYG1eOpHMTuskXKNksoDlf5zNZ0q55rtKty9eKYg9og0JhLdV+zDTWqI9AiwFXsWluXaQJfYjfhIqgcdYjyx0uxM3CR7rzzzuKwpz4jMFcbIG32HtTWP5t+m2I33vFSp9vf7rtVW9faOjSrbwTVYvfhItXeX5ybjs9sCtaUii12+55SIW4mQIAAAQJ9ICAA2AedpIoECBAg0N8C2e6b1Qbstdde1eNePBhbv9rRWq3qm+24mrKNTfIsMSUvRm1l65Xl37O1wVK2Plqr26f1Wu0UwWydwik/K6Zx/vznP0/Zbqb56KyYYhzpgAMOSONN6Wzn4VH+5ZdfXs0afu2kbNfTdrJNKE9MCY9AVZGyDUqKw0l9RgCwSNmGMNWpmMW5Rp8xDbhIMQ022yG3+Nq1zwg6LVy4MB/NGCN6DznkkGpgM9spNx100EEN61bb/nZGq0Uhte3PduVtWG67JyP4evHFF1ezt3q3ul3XaiXHOaideh0jD8dL8edStnNyNdtMBSqrD3RAgAABAgS6JCAA2CV4jyVAgACBwRG4//77q43deeedq8e9eBBrxhXrAEb9aqfXtapvtltpynbUrGZZtGhRfhzTHM8777zq+Zk+iEBN7YinbMfeCVch1r6L0WrFT6wjGCOizjzzzBRr/sX5WP8s1uJrZ0TXeBXINoxJMfoyUqz/FlN7m6XoryLF1N7pSLXvbLbT7pQeke1YW72/drRY9WSDg2yjhrqzEQTsRor3u3gH4nckprXH9Nxzzz03xcjX6Pu//uu/Tl/96lebVm+q7Z9q2z//+c+nIogYbfjLv/zLhnWNKfPFtPnIMJm+mmpdG1aswcls453q2RiJWfzDQ/XkmINrr722rm0zVc8x1fCVAAECBAjMuMD48w5mvEoeSIAAAQIEyiMQgbDakVkRNBgvZbvUpvhplSL4lO0K2irLpK5FgCMCXDG6KVIRiGqnsNNOOy1FgKF27a9YEzCmPXcrxSL/se5ckebNm1ccduQzpudmuy13bGORWI8sAkpF+pu/+ZtUO8KpOF98xqYqn/3sZ/OvMQU1goURQOyk+VZbbVU8LrXakKSaqcVBjG4s0ty5c4vDlp9j89WW0fLGGby4yy67pAgUjzelubbuY9vVrLq1+Wrvb5a/2fmY6hrvU5FiVG6zDYXGPqe2DsX9jT5r840to1H+Tpw77LDD0nve8558BGb8g0X8Prz3ve9tWHT8WXzGGWfUXevUxj11hfpCgAABAgR6UEAAsAc7RZUIECBAoDwCY/9yGSPixksxzfL//t//2zJb/AV3OgKA8dAYdVYEAMfWv1WlbrjhhnTPPffUZYmAYjdT7QjGmKJc7P45kTrF5ihve9vb8ltiCmXYxHp4MRouRnfGdM/YLTf6bOxotYk85/e//3067rjjUrHpx3777ZePKGtVRoxAe+tb35q++MUv5tli9Fn8REDq0Ucfzdt73XXXpRglVewm26q8Rtc23njj6ukIqE4l1QZj292MZeyurrUj06ZSl4neGxt2FO9B9FEEx+MdiBF18T7sueee+RTwWO9y3XXXbVj8VNs/2bY/9dRT+c7jRVAuRo6ec845DesYJ2vrGd8n01eTrWs8byJp++23z3e2vuSSS/LbIsgZ9Y2+io1vihRTx9/85jfXbUIT12aqnkU9fBIgQIAAgW4JCAB2S95zCRAgQGAgBGI0XW0q1ourPddrx7VBv2aBjLF1jr9Ex2YgYxfkP/300/PAQ+06ZmPvnc7vtd7FGoUTfV4Efhrt1nrXXXeld73rXflozX/+539Ot956a4og6GRGGcbU0FhH7vHHH8+rF2V8/etfrwtgNKt3BB5jlN7HP/7xajCj2JE21nE78MAD84BI7OgbUz4PP/zwfCprs/LGnq91q/Ucm6+d77UB2Jg2204qds0t8taOMivOzcTnwQcfnOJnbIoNbyLYFOtlxlqYt9xyS4pppo1+d6ba/sm0PYJ5RxxxRHVkbtQr1phsFRCurWe0dzJ9NZm6jrVt9/vnPve5FGt9xujjGOUXo5E/9alPpdj0J9obgfoIhA8NDeW/UzFqsNhBe+yf0e0+Uz4CBAgQINBvAtYA7LceU18CBAgQ6CuB+Mtn7bpwMRJnvBRTOCOQVvtTu9vrePdP5XqMcKsNALY7yjA2xIiAWKQYBVdMLYwRR83WGZtKPSdz79jg5GTKqL0nRlF95zvfyQOccT5G8E1ms5MwevWrX52KzQg22mijfD3BdgOJsZbbBz/4wXx6bgQ99t133zR21FwEcK666qo8EPTyl788Hx1Y25ZWx510qw06tTvyamy+2jJa1XumrsV6kLH2XLGBTmwQ8773va/h42vrPrZdDW/ITtbmq72/Wf7a8xEMi2ni119/fX46Anvf/va303ib4Yx9Tm0dassfe1ybb2wZY/N28vsGG2yQbrzxxnwjnqLcBx98MF122WX5GqTf//738+BfBPtiE5T999+/yJav5Vj94oAAAQIECJRYQACwxJ2raQQIECDQGwK1o9+KkVm9UbNVa/Hb3/62bhRfO1NaY8RT7XTCGJF2wQUXVDcTifUMp2uDilVbUH+mdsp1bXCiPtfkv0XwLdYcK6Y6X3311emXv/xl2wXG6KzXvOY1qdhdNwIUEayIKbwTTbGrbgSeYhRirD0ZAcTYafb4449PESAp0jXXXJNe9apXjbtZQpG/1q3Ws7g+kc8Ibhapdnp2ca7R59hpx+0GpRuVNV3nYrRbTP0tUqwL2Wj9zKm2fyJtj2B+bE4TAb9I8Q8R3/rWt+qCZEV9x35Ge2pH8E2mryZS17HPn8z3eP9j5OV3v/vdFDsxx8ZEMXo1ApHxDxKxQUv8bsbvQzHSNp4T63hKBAgQIEBgEAQEAAehl7WRAAECBLoqENPQilQEeorvvfYZ0+hq09577137dZXj2HEzRr0V69YdffTRKX5iNNQ73vGOav6YKlu7A2r1wjQf1AYwY/pfbTCrU4+OAG+sE1ikGInUTgq71772tSkCcpEi4BIjChtNN26nvNo8sQZarH+2zTbb5MHXGA31kY98pJolRqlFkLadFGunFanWszg3kc9ap1ijcOxac43Kuu+++6qnI6jUalOUasYuHOyzzz55wCkeHX3b6He9tv0xYrSdVNv+iexiHWtDFuviRaA61oaMqa/tppmsa7t1aidfjKaNDVliPdKYsh4jmiPwd9ZZZ6XiH2NiQ5QideL3rSjLJwECBAgQ6GUBAcBe7h11I0CAAIFSCLz0pS+ttiOm8jYKDFQzdPkgRggVKTZ/GG8k2ic+8Yl022235bfEKLPazUtiB+AYgRYpgkgRBJzptNlmm9WNZHrggQempQq1I+zaCXRGwPQv/uIvqrs9P/OZz8zXZTvggAOmpX4RXIxpwu985zur5RdroFVPNDmI4GGRIqA4lRRBpQhGRYqpxb/4xS/GLS7W2CvSzjvvXBz23Ge0a7311qvWq9F7UFv/22+/vW6H8OqNYw4m0/53v/vd+XqERVFf+tKX8pFvxfd2PmvrGgHj8dJDDz2UakcK1t4/3r0zeT1GRtb+Q0cEbiUCBAgQIDAIAgKAg9DL2kiAAAECXRV43etel2qn/p177rldrU+zh8dupjH9tEjHHHNMdWprca72M0bRRJCvSJ/+9KfrdsGN6aIReChSjMr5wQ9+UHydkc+YmrvbbrtVnxWbYkxHevjhh6vFjjf1sZiaGRsxRIqRejFFOkYuTXeK9eCKtGDBguKw5WexNmFkesELXtAy73gXYw262lGlMWVzvBSbNxQpNjLp1RSj/mqDfo3egwg2Feszxui0mD7fKsUGKLG5SJHaaX9svFP7Z8xnPvOZfIOeoox2P2v/4WKi/RQj7XbYYYd2HzWj+aItRaAy+ujQQw+d0ed7GAECBAgQ6JaAAGC35D2XAAECBAZGIAJhtdNhY3fXK664oqfaH4GGWDcrglORYkRarJnVLEW+mPpb7A560EEHpZNPPnmV7LFr6hvf+Mbq+be85S35tLzqiRk42HPPPatP+a//+q/qcacOIhBaG0wbb+RTTM2MYGikCFB+5StfSREknolUuzNtTBMeL0U/165pWGs53r3Nrh955JHVSxdeeGH1uNFB7N76ox/9qHqp9t7qyR45iPUfi9+HqFKj9yDWo4tNWIo0Xvtjd+liU54IVtVuXlGUUfsZAfnYDbpIZ5555qRH3sbalMVozQic1wYii/JrP2vbErsO92o644wzqlWLNRLH7nhcveiAAAECBAiUTEAAsGQdqjkECBAg0JsCEUx70YteVK1cBNvanYJZvWmaDmItttgUonaq4Qc+8IHqelmNHhsjjIppdBHgPO+88xply8/FCKRi3bZY9yxGKM1kiuBkkWKDjPFS7Siu8fLGGnZ/9Vd/Vc0WGxHst99+1e9jD97znvfUTc2MKdMnnHDC2Gxtfx9vBNnYgmKNwSLtuOOOxWHTz5j6GbsUR4rp4FtuuWXTvO1eOPHEE1OxmUgEls4///ymt8bvTbG+5Ete8pIUO+7OVJrIe/Dkk0+m9773vdWq7b777tXp79WT/31Q+75E0Kx2PbravLFmZW2w6tRTT63bUbw2bxzHZjQxzbtI73//+9OHPvSh4uuEP2P6fKznWaQor9mO0BH8jJ9IMaI1gty9mGKH9WKNzljPstarF+urTgQIECBAoKMC2f+RSwQIECBAgMAMCNx7772VLEBUyf6PPP/JRn9Vsmm2lVtvvbWSjbRqWIMs+FHJNomoZEGs6n1xf7aWYMP82Zp71XzZTqQN8xQno4wsQFDJpidX74mys9FoTesT9959992VbHfN6j1ZgK8osulnNuqxmj8bVVTJRhM1zBttLXzisxMpC2BVsjXw8nKzXXYr2WjHlsVmaxVWXvGKV1SyKbqVbNOQpnmzYGIlGxFXV98smNU0fxbMqcv7qU99qmnedi9kawZWsg1XKtlIsUo2BbXutngX4nqRLrvssko2/bRah2984xvFpaafUceiP7IAUNN8E70Q711RbvTN2LpkI+kqWfCvmifyZlM3J/qYPH/xnPiM96vddNRRR1WyTVoq2bT4VWyLMuL3M9t1tpIFU+vq+sMf/rDI0vAz2xiomj9bV7GSjUyty5ftUlv3O5+N/qtkuwrX5an9km3oUok/T4q2vu1tb6u9POnjbPp3JRsNXC03G81bid+n2vTv//7vdX+GZCOBay+3fZwFhqvPieOJpPCL39tsXcWGt2VT9CvZaL9q+fFnUPYPMA3zOkmAAAECBMoqMCsalv3HgkSAAAECBAjMgECMgIvpcWOnosYIuRg1FBtvxDTBWB8sNqyIDTbGjkSKtbli9GAWzFqlxrFJQ7G7aEw1rN01NEZSLVy4MGWBhLzcWLS/NsXInRj5F6NkZs+eXXupehz/2RDlZoGU/Fys5xYjaoqpgtWMDQ5iB9IsWJJfed7znpePOIypxrUp1ueqXXusU/+ZEhtuxNTrSN/73vfSIYccUvvYuuPYrCRGU0WK9dp23XXXfD2z9ddfPx+NFhuaxGjJsRuKnHbaaXVrr9UWGs+sXWssRldNZNpvTOVstKbcgQcemIo18mL0YUwRjVFycRx9GZuTxCiucK+dyhv5wjqmILdKsWbdTTfdlGeJd/b5z39+q+xtX4v18mLUaRY8qt4TazVG3WNU5fXXX59q11X88Ic/XDcarnpTzcG3v/3thnlqf9e23377/Per5rYUU13Dd2yK6cbFKN0YsRj1i9+v2Ogjpvo+8sgjKQvepxhBW5vOOeecFJtwtErx7sR06qKN0Q9ZsDZF/eL9ygKIKUYARorfxauuuqpu6nBt2bGZyAtf+MLq9P2oa4yyHK9vizLivX3Oc55TfF3lM3aLPuWUU6rn452KtQhjOvkdd9xRHQkcGeL9iFG2jf5sqhaQHUR9x6bY7Tj+bIoUzyh27K3N12zTmJieHCNEI8XGQ1F+/A7Ert/ZP7zk05eL5Q3iz6qYdh9GEgECBAgQGCiBCABKBAgQIECAwMwJZMG9SrZ7biUL9lVHpGT/8dHyOEb3xKihbO3AlhWtHQE4XpnF9RgVdtxxx1V+9rOftSw7LmabelTrma0hV8mmL457T5Eh+wt+JUbgFc/Ngi7FpepnjCYqrsconU6lGJFVlDve6KIY6VbkbeczRnVedNFFLasaozHbKatZnmYjPuM9yoIlEyo7RpNmU1Zb1jcuRn/Fexd1yoLT4+afaIannnoqHwHbrM1xPkafZevatVX0ZI2bvQ8xOrdV3cZe22677fLRgG1VNssUo+uyQFXLZ2T/MFC58sorWxYZoxrH1mUi39sZFfnlL3+5kgUWWz4n+4eBSvaPCi3rWlycSP1q8xb3j/3MgtQt61aUkQU6K+ONzhxbtu8ECBAgQKAsAkYAZv9FIBEgQIAAgW4IxCi/GNkTmxzEenoxkihG+8VIlRhtFqMCYyTL/PnzU4yeywIM41YzRigVIwDHZo7RbDF6KX622mqrfLTVHnvskWKNvBh5OF568MEH83XgFi1alGdtZ1TW2DI///nPp2x6Yn466hMjempHKcYuxMVuuDHCKEYsdirF6KQYLZVNX04x+jEcmqUYNRaj026++eYUu+DGZhSxGUOMHop6bb311vmajjGK7fDDD6/u7NqsvFjrrdEmKc3yjz2fBQDz0Wdjz8f32MAlRozFGmyxZt9dd92Vj6SK85FiRFisdxYjzmIkZIxEbGd0WPRvjAaNdPHFF6c3vOEN+XGn/yfqngVQ85GGMSIuRoWGb2wgExvNNNpMo1EdJmscI8Hi3rEpfg+j/2OkZBYcT7FeYYzcizURo47x/sTvW4zcjXcgfo9iFO1EUowkzKZm56NTYy3A2J02fvfjdz1GbsY7M97vZtSvdtTsRJ4feWM0b4wkHS/FCL0YDRjrSMZxOGyxxRb5nyPxbsSIyXbeq3hOu/nG1in7C9jYU/n3cIzf1/iz9Mc//nGKP6viz9MYPRl1jN2rs+nceR3jzx2JAAECBAgMooAA4CD2ujYTIECAAIEeFYjdcYtdg2Mq34IFCzpW00svvTTf6TgKjI1JYqpvmVMEp+InAkQTTTFFN4JQEfCKMn7729/mQa+JliM/AQIECBAgQIBAbwis1hvVUAsCBAgQIECAQMpH2xUO7Y78KvKP95lNc87X84t8sU5bBLmkxgKXXHJJdY3Dv/3bvxX8a8zkLAECBAgQIECgbwQEAPumq1SUAAECBAiUWyCm9xWbLkRLi0X9O9XqmL579tln58XFlN7YCEBaVWBkZCRlawvmF2LqZDEic9WczhAgQIAAAQIECPSLgABgv/SUehIgQIAAgRILxHprMdIs1kGLFGuExYi9TqdYsy92YY4U69vFOoxSvUCs8xZTfiP94z/+44TXtasvzTcCBAgQIECAAIFeELAGYC/0gjoQIECAAIEBFIjNKj772c/mG33Ewv333ntvVeGUU05J2c6j1e8OJi4Qa/fFz2TWAJz409xBgAABAgQIECDQywICgL3cO+pGgAABAgRKLBCBqUa7l8aOx9/85jfT3LlzS9x6TSNAgAABAgQIECAwcwKzZ+5RnkSAAAECBAgQWFVgjTXWSJtssknaa6+90hve8IbqFN1VczpDgAABAgQIECBAgMBkBIwAnIyaewgQIECAAAECBAgQIECAAAECBAj0iYBNQPqko1STAAECBAgQIECAAAECBAgQIECAwGQEBAAno+YeAgQIECBAgAABAgQIECBAgAABAn0iIADYJx2lmgQIECBAgAABAgQIECBAgAABAgQmIyAAOBk19xAgQIAAAQIECBAgQIAAAQIECBDoEwEBwD7pKNUkQIAAAQIECBAgQIAAAQIECBAgMBkBAcDJqLmHAAECBAgQIECAAAECBAgQIECAQJ8IzO6TeqpmyQSWLVuWbr/99rxVm2yySZo926tYsi7WHAIECBAgQIAAAQIECBDoAYGRkZH02GOP5TXZbbfd0hprrNEDtVKFmRYQdZlpcc/LBSL4t+eee9IgQIAAAQIECBAgQIAAAQIEZkjg5ptvTvPnz5+hp3lMLwmYAtxLvaEuBAgQIECAAAECBAgQIECAAAECBDosYARgh0EV155ATPstUvwLxBZbbFF89UmAAAECBAgQIECAAAECBAh0SODhhx+uzsCr/bt4h4pXTJ8ICAD2SUeVrZq1a/5F8G/evHlla6L2ECBAgAABAgQIECBAgACBnhKo/bt4T1VMZaZdwBTgaSf2AAIECBAgQIAAAQIECBAgQIAAAQLdExAA7J69JxMgQIAAAQIECBAgQIAAAQIECBCYdgEBwGkn9gACBAgQIECAAAECBAgQIECAAAEC3RMQAOyevScTIECAAAECBAgQIECAAAECBAgQmHYBAcBpJ/YAAgQIECBAgAABAgQIECBAgAABAt0TEADsnr0nEyBAgAABAgQIECBAgAABAgQIEJh2AQHAaSf2AAIECBAgQIAAAQIECBAgQIAAAQLdExAA7J69JxMgQIAAAQIECBAgQIAAAQIECBCYdgEBwGkn9gACBAgQIECAAAECBAgQIECAAAEC3RMQAOyevScTIECAAAECBAgQIECAAAECBAgQmHYBAcBpJ/YAAgQIECBAgAABAgQIECBAgAABAt0TEADsnr0nEyBAgAABAgQIECBAgAABAgQIEJh2AQHAaSf2AAIECBAgQIAAAQIECBAgQIAAAQLdExAA7J69JxMgQIAAAQIECBAgQIAAAQIECBCYdgEBwGkn9gACBAgQIECAAAECBAgQIECAAAEC3RMQAOyevScTIECAAAECBAgQIECAAAECBAgQmHYBAcBpJ/YAAgQIECBAgAABAgQIECBAgAABAt0TEADsnr0nEyBAgAABAgQIECBAgAABAgQIEJh2AQHAaSf2AAIECBAgQIAAAQIECBAgQIAAAQLdExAA7J69JxMgQIAAAQIECBAgQIAAAQIECBCYdgEBwGkn9gACBAgQIECAAAECBAgQIECAAAEC3RMQAOyevScTIECAAAECBAgQIECAAAECBAgQmHYBAcBpJ/YAAgQIECBAgAABAgQIECBAgAABAt0TEADsnr0nEyBAgAABAgQIECBAgAABAgQIEJh2AQHAaSf2AAIECBAgQIAAAQIECBAgQIAAAQLdExAA7J69JxMgQIAAAQIECBAgQIAAAQIECBCYdgEBwGkn9gACBAgQIECAAAECBAgQIECAAAEC3RMQAOyevScTIECAAAECBAgQIECAAAECBAgQmHYBAcBpJ/YAAgQIECBAgAABAgQIECBAgAABAt0TEADsnr0nEyBAgAABAgQIECBAgAABAgQIEJh2AQHAaSf2AAIECBAgQIAAAQIECBAgQIAAAQLdExAA7J69JxMgQIAAAQIECBAgQIAAAQJ9JrBseLTPaqy6BFISAPQWECBAgAABAgQIECBAgAABAgTaFFiyfKTNnLIR6B0BAcDe6Qs1IUCAAAECBAgQIECAAAECBHpcYMlyIwB7vItUr4GAAGADFKcIECBAgAABAgQIECBAgAABAo0ElqwwArCRi3O9LSAA2Nv9o3YECBAgQIAAAQIECBAgQIBAjwisXFlJQyuMAOyR7lCNCQgIAE4AS1YCBAgQIECAAAECBAgQIEBgcAWG8g1AKoMLoOV9KyAA2Lddp+IECBAgQIAAAQIECBAgQIDATArYAGQmtT2rkwICgJ3UVBYBAgQIECBAgAABAgQIECBQWgEBwNJ2bekbJgBY+i7WQAIECBAgQIAAAQIECBAgQKATAnYA7oSiMrohIADYDXXPJECAAAECBAgQIECAAAECBPpKoFKJDUDsANxXnaayVQEBwCqFAwIECBAgQIAAAQIECBAgQIBAY4Gl2QYg2SbAEoG+FBAA7MtuU2kCBAgQIECAAAECBAgQIEBgJgWeXm7030x6e1ZnBQQAO+upNAIECBAgQIAAAQIECBAgQKCEAkPLR0vYKk0aFAEBwEHpae0kQIAAAQIECBAgQIAAAQIEJi1gBOCk6dzYAwICgD3QCapAgAABAgQIECBAgAABAgQI9LbA0AojAHu7h9SulYAAYCsd1wgQIECAAAECBAgQIECAAIGBF1iaBf9G7QAy8O9BPwMIAPZz76k7AQIECBAgQIAAAQIECBAgMO0CS1bYAGTakT1gWgUEAKeVV+EECBAgQIAAAQIECBAgQIBAvwsssQNwv3fhwNdfAHDgXwEABAgQIECAAAECBAgQIECAQCuBJXYAbsXjWh8ICAD2QSepIgECBAgQIECAAAECBAgQINA9AVOAu2fvyZ0READsjKNSCBAgQIAAAQIECBAgQIAAgRIKLBseTSOjlRK2TJMGSUAAcJB6W1sJECBAgAABAgQIECBAgACBCQlY/29CXDL3qIAAYI92jGoRIECAAAECBAgQIECAAAEC3RcYWjHa/UqoAYEpCggAThHQ7QQIECBAgAABAgQIECBAgEB5BZ62A3B5O3eAWiYAOECdrakECBAgQIAAAQIECBAgQIDAxASGVoxM7Aa5CfSggABgD3aKKhEgQIAAAQIECBAgQIAAAQLdF1g+MppWjNgApPs9oQZTFRAAnKrgf99/yy23pDPPPDO98pWvTPPmzUurr756WnvttdOOO+6YTj755HTDDTdM6Enf//7301FHHVUtK8qM73G+k2loaCh96lOfSvPnz08bbrhhWmuttdJzn/vc9L//9/9Ov//97zv5KGURIECAAAECBAgQIECAAIG+Ehhabv2/vuowlW0qMKuSpaZXXWhLYP/990//8R//MW7eE044IX35y19Oc+bMaZp35cqV6dRTT00XXHBB0zynnHJK+tKXvpRWW21q8du77747vfrVr0533XVXw2etu+666ZJLLkmHHXZYw+tTOfnAAw+krbfeOi/i/vvvzwOdUynPvQQIECBAgAABAgQIECBAoNMC9z85lB7449K6YufMnpV2f/aGded6+Yu/f/dy78xc3aYWQZq5evb0kx566KG8fltuuWU67bTT0uWXX55uvvnmdNNNN6VzzjknbbXVVvn1r371q+mkk05q2ZbTTz+9Gvx70YtelL7+9a/nZcVnfI90/vnnpw9+8IMtyxnv4uLFi9Ohhx5aDf69+c1vTj/60Y/Sj3/84/Sxj30sH724aNGidOyxx6Zf/OIX4xXnOgECBAgQIECAAAECBAgQKJ2AHYBL16UD2yAjADvQ9TFCLkb3vfa1r03PeMYzVinx8ccfT/vuu2/67W9/m1+77rrrUowaHJvi+q677ppGRkbSHnvska6//vo0d+7caraYrnvAAQekmG48e/bsdOedd6Yddtihen0iB2eccUb6yEc+kt8SU4Df97731d0egcB4VtQlPq+99tq661P94l8gpirofgIECBAgQIAAAQIECBCYboFbf//HbA3AlXWPMQKwjsOXPhEwArADHXXllVemY445pmHwL4rfeOON06c//enqk2KEYKN07rnn5gG3uPa5z32uLvgX59Zcc838fBxHYO4zn/lMHE44DQ8Pp3/4h3/I79t5553z9f7GFrLPPvukN73pTfnpCFj+7Gc/G5vFdwIECBAgQIAAAQIECBAgUFqB4dGVqwT/SttYDSu9gADgDHXxS1/60uqT7rnnnupxcRBLMf7rv/5r/jU24dh7772LS3WfcX6nnXbKz0X+ySzheM0116SFCxfmZZx44olN1xKsna78L//yL3X18IUAAQIECBAgQIAAAQIECJRZYMnykTI3T9sGTEAAcIY6fPny5dUnNZom/Lvf/S4VawnGlNtWqbj+4IMPpgULFrTK2vBa7Y7ERVmNMsY05Bh1GOnGG29slMU5AgQIECBAgAABAgQIECBQSoElK+wAXMqOHdBGCQDOUMfHNNoixbTbsemOO+6onooRgK1S7fVYB3Ciqd1nxTqDxRqDk3nOROslPwECBAgQIECAAAECBAgQ6BUBIwB7pSfUoxMCsztRiDJaC6xcuTKdddZZ1UyxXuDYFJtiFGnevHnFYcPPrbfeunr+/vvvrx63e1A8a6211krrr79+y9viWbfddlt67LHHUoxiXH311VvmLy4Wzyi+j/18+OGHx57ynQABAgQIECBAgAABAgQI9IyAAGDPdIWKdEBAALADiOMVEZt13HzzzXm2o48+Ou2+++6r3LJ48eLqubXXXrt63OggAndFevrpp4vDtj+LZ433nChw7LPaDQDWBinbrpiMBAgQIECAAAECBAgQIECgBwRGsg1Alg3X7/7bA9VSBQKTFjAFeNJ07d0YU38/8IEP5Jk33XTT9IUvfKHhjcuWLauenzNnTvW40UFtEG7p0qWNsrQ8VzxrvOdEIVN9VsuKuEiAAAECBAgQIECAAAECBHpQwPp/PdgpqjQlASMAp8TX+uZf/epX6aijjkojIyNpjTXWSN/61rdSBAEbpbhepBUrVhSHDT9rNxSZO3duwzytThbPGu85UcZknzXe1OSYArznnnu2qqZrBAgQIECAAAECBAgQIECgKwJDK+wA3BV4D502AQHAaaKNXX1f+cpXpj/+8Y8pdv297LLL0v7779/0aeuss0712njTepcsWVLN28403mrm/z4onjXecyL7ZJ813jqGY+vkOwECBAgQIECAAAECBAgQ6BUB6//1Sk+oR6cETAHulGRNOQ899FB6xStekeJz1qxZ6Stf+Uo64ogjanKselgbMBtvA43a0XWTWWuveFYE95566qlVK1NzpnjWJptsUjcduCaLQwIECBAgQIAAAQIECBAgUCqBJctHS9UejSEgANjhd+Dxxx9PBx10ULr33nvzkj/3uc+lE044Ydyn7LLLLtU8v/71r6vHjQ5qr++8886NsrQ81+6zYuryPffck5c1mee0rISLBAgQIECAAAECBAgQIECgBwVGV1bS0mEBwB7sGlWagoAA4BTwxt66cOHCdPDBB6c77rgjv3TWWWelt73tbWOzNfy+7bbbpi233DK/FhuHtErXX399fnmrrbZK22yzTausDa/tt99+1fOtnnXLLbdUpwDvu+++1XscECBAgAABAgQIECBAgACBsgrE+n+VSllbp12DKiAA2KGeHxoaSoceemj6z//8z7zE008/Pf31X/9126XHVOFimnCM8PvJT37S8N44X4wAjPxx30TTgQcemNZbb738tosuuij7g63xn2wXXnhhtejYzEQiQIAAAQIECBAgQIAAAQJlFzD9t+w9PJjtEwDsQL/HbroRILvxxhvz0k477bT00Y9+dMIlv+td78o3DIkb3/GOd6SlS5fWlRHf43yk2bNnp8jfKJ100kl5YDCCg9dee+0qWebMmZPe+c535ufvvPPOdPbZZ6+S56abbkoXXHBBfv6AAw5I8+fPXyWPEwQIECBAgAABAgQIECBAoGwCS+wAXLYu1Z5MwC7AHXgNjj/++HT11VfnJb3sZS9Lb3rTm9Ivf/nLpiVHAG7HHXdc5Xqce9/73pdi6nBMv41ptzGKcPvtt8/X4vvkJz+Zfv7zn+f3Rb7nPOc5q5TR7om4/xvf+Eb67W9/m97//venu+++Ox133HFp7ty56Zprrkkf//jHU6wBGN/PPffcdouVjwABAgQIECBAgAABAgQI9LWAHYD7uvtUvonArGz6Z+P5n01ucHpVgYlOw332s5+dFixYsGpB2ZmVK1emN7/5zfnOwQ0zZCcjwHjeeeel1VZrPIAzRgDG1N5IEcyLKb+NUgT9Xv3qV6e77rqr0eW07rrrpksuuSQddthhDa9P5WTsdFzsYBw7DRc7E0+lTPcSIECAAAECBAgQIECAAIGpCKzMNgC5ecGTLdcAnDN7Vtr92RtO5TEzeq+/f88od88+rHEEqWerW/6KRVAvpt5+97vfzdcEjI1BYsRgfMaaf9/73vfS+eef3zT4NxGhHXbYIR9RGCML99hjj7T++uunNddcM+20007p3e9+d7rtttumJfg3kTrKS4AAAQIECBAgQIAAAQIEZkpgKNv91zCpmdL2nJkUMAJwJrU9qyrgXyCqFA4IECBAgAABAgQIECBAoEcEHl20LN3z2JKWtTECsCWPiz0qYARgj3aMahEgQIAAAQIECBAgQIAAAQIzK/D08pGZfaCnEZghAQHAGYL2GAIECBAgQIAAAQIECBAgQKC3BYZWjPZ2BdWOwCQFBAAnCec2AgQIECBAgAABAgQIECBAoDwCsUeqHYDL059aUi8gAFjv4RsBAgQIECBAgAABAgQIECAwgAJLsw1Ask2AJQKlFBAALGW3ahQBAgQIECBAgAABAgQIECAwEQHr/01ES95+ExAA7LceU18CBAgQIECAAAECBAgQIECg4wJDy63/13FUBfaMgABgz3SFihAgQIAAAQIECBAgQIAAAQLdEjACsFvynjsTAgKAM6HsGQQIECBAgAABAgQIECBAgEBPC9gBuKe7R+WmKCAAOEVAtxMgQIAAAQIECBAgQIAAAQL9LbB0xWgatQNIf3ei2rcUEABsyeMiAQIECBAgQIAAAQIECBAgUHaBJStGyt5E7RtwAQHAAX8BNJ8AAQIECBAgQIAAAQIECAy6gA1ABv0NKH/7BQDL38daSIAAAQIECBAgQIAAAQIECLQQsAFICxyXSiEgAFiKbtQIAgQIECBAgAABAgQIECBAYLICQ6YAT5bOfX0iIADYJx2lmgQIECBAgAABAgQIECBAgEDnBZYNj6bh0UrnC1YigR4SEADsoaa8g0YAAEAASURBVM5QFQIECBAgQIAAAQIECBAgQGBmBYayHYAlAmUXEAAsew9rHwECBAgQIECAAAECBAgQINBUYMlyOwA3xXGhNAICgKXpSg0hQIAAAQIECBAgQIAAAQIEJiqwxPp/EyWTvw8FBAD7sNNUmQABAgQIECBAgAABAgQIEJi6QKVSSYuXGQE4dUkl9LqAAGCv95D6ESBAgAABAgQIECBAgAABAtMisCRb/2/EBiDTYqvQ3hIQAOyt/lAbAgQIECBAgAABAgQIECBAYIYEFi4dnqEneQyB7goIAHbX39MJECBAgAABAgQIECBAgACBLgksHBIA7BK9x86wgADgDIN7HAECBAgQIECAAAECBAgQINB9gZUrY/0/AcDu94QazISAAOBMKHsGAQIECBAgQIAAAQIECBAg0FMCsflHFgOUCAyEgADgQHSzRhIgQIAAAQIECBAgQIAAAQK1Atb/q9VwXHYBAcCy97D2ESBAgAABAgQIECBAgAABAqsICACuQuJEiQUEAEvcuZpGgAABAgQIECBAgAABAgQIrCowPLoyLVkxsuoFZwiUVEAAsKQdq1kECBAgQIAAAQIECBAgQIBAY4FFS4dTxfp/jXGcLaWAAGApu1WjCBAgQIAAAQIECBAgQIAAgWYCpv82k3G+rAICgGXtWe0iQIAAAQIECBAgQIAAAQIEGgoIADZkcbLEAgKAJe5cTSNAgAABAgQIECBAgAABAgTqBZYNj6ZlwyvrT/pGoOQCAoAl72DNI0CAAAECBAgQIECAAAECBP5HINb/kwgMmoAA4KD1uPYSIECAAAECBAgQIECAAIEBFjD9d4A7f4CbLgA4wJ2v6QQIECBAgAABAgQIECBAYNAEBAAHrce1NwQEAL0HBAgQIECAAAECBAgQIECAwEAILFk+koZHKwPRVo0kUCsgAFir4ZgAAQIECBAgQIAAAQIECBAorYDRf6XtWg0bR0AAcBwglwkQIECAAAECBAgQIECAAIFyCAgAlqMftWLiAgKAEzdzBwECBAgQIECAAAECBAgQINBnAitXVtLiZSN9VmvVJdAZAQHAzjgqhQABAgQIECBAgAABAgQIEOhhgcXZ+n+jWRBQIjCIAgKAg9jr2kyAAAECBAgQIECAAAECBAZMYNHS4QFrseYS+B8BAcD/sXBEgAABAgQIECBAgAABAgQIlFTA+n8l7VjNaktAALAtJpkIECBAgAABAgQIECBAgACBfhUYGV2Zns6mAEsEBlVAAHBQe167CRAgQIAAAQIECBAgQIDAgAgsyjb/qFj+b0B6WzMbCQgANlJxjgABAgQIECBAgAABAgQIECiNgOm/pelKDZmkgADgJOHcRoAAAQIECBAgQIAAAQIECPSHgABgf/STWk6fgADg9NkqmQABAgQIECBAgAABAgQIEOiywPKR0bR0xWiXa+HxBLorIADYXX9PJ0CAAAECBAgQIECAAAECBKZRwOi/acRVdN8ICAD2TVepKAECBAgQIECAAAECBAgQIDBRgUVLhyd6i/wESicgAFi6LtUgAgQIECBAgAABAgQIECBAoBBYuHSkOPRJYGAFBAAHtus1nAABAgQIECBAgAABAgQIlFtgaMVIWjGystyN1DoCbQgIALaBJAsBAgQIECBAgAABAgQIECDQfwLW/+u/PlPj6REQAJweV6USIECAAAECBAgQIECAAAECXRYQAOxyB3h8zwgIAPZMV6gIAQIECBAgQIAAAQIECBAg0CmBSqWSFi+z/l+nPJXT3wICgP3df2pPgAABAgQIECBAgAABAgQINBBYvHwkjYxWGlxxisDgCQgADl6fazEBAgQIECBAgAABAgQIECi9wMKh4dK3UQMJtCsgANiulHwECBAgQIAAAQIECBAgQIBA3whY/69vukpFZ0BAAHAGkD2CAAECBAgQIECAAAECBAgQmDmB0ZWV9HQ2BVgiQOBPAgKA3gQCBAgQIECAAAECBAgQIECgVAKLlg6nbA8QiQCB/xYQAPQqECBAgAABAgQIECBAgAABAqUSWLTM+n+l6lCNmbKAAOCUCRVAgAABAgQIECBAgAABAgQI9JKA9f96qTfUpRcEBAB7oRfUgQABAgQIECBAgAABAgQIEOiIwIqRlWnJ8tGOlKUQAmUREAAsS09qBwECBAgQIECAAAECBAgQIJBM//USEFhVQABwVRNnCBAgQIAAAQIECBAgQIAAgT4VMP23TztOtadVQABwWnkVToAAAQIECBAgQIAAAQIECMykgADgTGp7Vr8ICAD2S0+pJwECBAgQIECAAAECBAgQINBSYNnwaFo+vLJlHhcJDKKAAGCHev3RRx9NV155ZTrjjDPSIYcckjbeeOM0a9as/Oekk04a9ykLFiyo5i/uG+9zm222GbfcZhni3vHKj+uRTyJAgAABAgQIECBAgAABAv0gYPRfP/SSOnZDYHY3HlrGZ2622WYz3qyddtppxp/pgQQIECBAgAABAgQIECBAoFcFFi0d7tWqqReBrgoIAE4D/7Oe9az03Oc+N1199dVtl77VVlul22+/fdz8n/jEJ9Kll16a5zvxxBPHzT9ehiOOOCJ99KMfbZptzpw5Ta+5QIAAAQIECBAgQIAAAQIEeknADsC91Bvq0ksCAoAd6o2Y+jt//vz8J0YDxpTebbfdtu3Sn/nMZ6bnPe95LfOPjo6ma6+9Ns+zzjrrpKOOOqpl/nYurr/++uM+t51y5CFAgAABAgQIECBAgAABAt0UiPX/VoxUulkFzybQswICgB3qmg9/+MMdKql5MT/84Q/TQw89lGd43etel+bOnds8sysECBAgQIAAAQIECBAgQGCABEz/HaDO1tQJC9gEZMJk3bvhq1/9avXhnZj+Wy3MAQECBAgQIECAAAECBAgQ6HOBRctG+rwFqk9g+gQEAKfPtqMlL168OF1xxRV5mbEz7/7779/R8hVGgAABAgQIECBAgAABAgT6WWDxMhuA9HP/qfv0CggATq9vx0q//PLL09DQUF7eG9/4xjRr1qyOlH399denF77whSnWFFxzzTXzdQuPPfbYPNhYqVg7oSPICiFAgAABAgQIECBAgACBaRVYPjKalg2vnNZnKJxAPwtYA7BPeq92+u8JJ5zQsVr/7ne/qysrNi+Jn29+85tp3333Td/4xjdS7FA80fTAAw+0vOXhhx9ued1FAgQIECBAgAABAgQIECDQrsBi03/bpZJvQAUEAPug4++777503XXX5TXdZ5990g477DDlWs+ZMye95jWvSa985SvzXYDXW2+99NRTT6WbbropfeELX0j3339/uvHGG9NBBx2Un4vrE0lbb731RLLLS4AAAQIECBAgQIAAAQIEJi1gA5BJ07lxQAQEAPugo7/2ta+lYjpup0b/3XzzzWn99ddfpfUHHnhgevvb355il+Grr7463XnnnSl2OD7nnHNWyesEAQIECBAgQIAAAQIECBDoBQEjAHuhF9ShlwUEAHu5d/67bhdffHF+tPrqq6dYn68TqVHwryg31gOMKcDbbbddevLJJ9N5552XzjrrrBSjBttNMYKwVYopwHvuuWerLK4RIECAAAECBAgQIECAAIFxBYZHV6ahFaPj5pOBwCALCAD2eO/HSL1f//rXeS1jym6rwF0nmxJTfo877rj0+c9/Pi1ZsiTdcsstKaYft5vmzZvXblb5CBAgQIAAAQIECBAgQIDApAWM/ps0nRsHSMAuwD3e2dO1+Uc7zd5ll12q2R588MHqsQMCBAgQIECAAAECBAgQINArAtb/65WeUI9eFhAA7OHeGR4eTpdddllew0033TS96lWvmtHazpo1a0af52EECBAgQIAAAQIECBAgQGCiAkYATlRM/kEUEADs4V7/7ne/m5544om8hn/xF3+RZs+e2Rnbd9xxR1Vnyy23rB47IECAAAECBAgQIECAAAECvSAwurKSlqwY6YWqqAOBnhYQAOzh7qmd/nviiSfOaE0XLlxYHX245pprpj322GNGn+9hBAgQIECAAAECBAgQIEBgPIHFy4ZTpTJeLtcJEBAA7NF3IHbfjRGAkXbbbbf0whe+sO2aHnjggSmm78bPggULVrnvqquuSkuXLl3lfHHi6aefTsccc0x19OGb3vSmFDsQSwQIECBAgAABAgQIECBAoJcETP/tpd5Ql14WmNk5pb0sMcW63XDDDenuu++ulvL4449Xj+P8hRdeWP0eByeddFLd97FfYu2/FStW5Kc7PfrvrLPOSq9//evT0Ucfnfbbb7+0/fbbp7XXXjvFqL8f//jH6Ytf/GK677778mfvtNNO6e/+7u/GVs93AgQIECBAgAABAgQIECDQdYGFS4e7XgcVINAPAgKAHeql888/P1100UUNS7vxxhtT/NSm8QKAxfTfZzzjGXmwrvbeThzHCMOoc/w0SwcccEC65JJL0oYbbtgsi/MECBAgQIAAAQIECBAgQKArAitj/b/l1v/rCr6H9p2AAGAPdtldd92VfvrTn+Y1O+igg9Lmm2/e0VqeffbZ6Uc/+lG66aab0m9+85sUoxWfeuqpFGv9xWYfe+21Vzr++OPTK1/5ynwacUcfrjACBAgQIECAAAECBAgQINABgcVZ8C+LAUoECLQhMKuSpTbyyUKgowIPPPBA2nrrrfMy77///jRv3ryOlq8wAgQIECBAgAABAgQIECi3wAN/HEr3P9l8ffvpav2c2bPS7s/un5ly/v49XW9Cf5VrE5D+6i+1JUCAAAECBAgQIECAAAECBDIBG4B4DQi0LyAA2L6VnAQIECBAgAABAgQIECBAgEAPCMRkRgHAHugIVegbAQHAvukqFSVAgAABAgQIECBAgAABAgRCYMmK0TRqAUAvA4G2BQQA26aSkQABAgQIECBAgAABAgQIEOgFgUVLh3uhGupAoG8EBAD7pqtUlAABAgQIECBAgAABAgQIEAgB03+9BwQmJiAAODEvuQkQIECAAAECBAgQIECAAIEuCyxeZgRgl7vA4/tMQACwzzpMdQkQIECAAAECBAgQIECAwCALDK0YScOjlUEm0HYCExYQAJwwmRsIECBAgAABAgQIECBAgACBbgksWjrSrUd7LoG+FRAA7NuuU3ECBAgQIECAAAECBAgQIDB4Aqb/Dl6fa/HUBQQAp26oBAIECBAgQIAAAQIECBAgQGCGBBYtMwJwhqg9pkQCAoAl6kxNIUCAAAECBAgQIECAAAECZRZYNjyaVoysLHMTtY3AtAgIAE4Lq0IJECBAgAABAgQIECBAgACBTgsssvtvp0mVNyACAoAD0tGaSYAAAQIECBAgQIAAAQIE+l3ABiD93oPq3y0BAcBuyXsuAQIECBAgQIAAAQIECBAgMCEBG4BMiEtmAlUBAcAqhQMCBAgQIECAAAECBAgQIECgVwWWj4ymZcPW/+vV/lGv3hYQAOzt/lE7AgQIECBAgAABAgQIECBAIBNYbPdf7wGBSQsIAE6azo0ECBAgQIAAAQIECBAgQIDATAkIAM6UtOeUUUAAsIy9qk0ECBAgQIAAAQIECBAgQKBkAouWDpesRZpDYOYEBABnztqTCBAgQIAAAQIECBAgQIAAgUkIDI+uTEMrRidxp1sIEAgBAUDvAQECBAgQIECAAAECBAgQINDTAqb/9nT3qFwfCAgA9kEnqSIBAgQIECBAgAABAgQIEBhkgcXLTP8d5P7X9qkLCABO3VAJBAgQIECAAAECBAgQIECAwDQKLFo6Mo2lK5pA+QUEAMvfx1pIgAABAgQIECBAgAABAgT6VmB0ZSUtWSEA2LcdqOI9ISAA2BPdoBIECBAgQIAAAQIECBAgQIBAI4GY/lupNLriHAEC7QoIALYrJR8BAgQIECBAgAABAgQIECAw4wI2AJlxcg8soYAAYAk7VZMIECBAgAABAgQIECBAgEBZBBbZAKQsXakdXRQQAOwivkcTIECAAAECBAgQIECAAAECzQVWZuv/Pb3M+n/NhVwh0J6AAGB7TnIRIECAAAECBAgQIECAAAECMyzwdLb5RxYDlAgQmKKAAOAUAd1OgAABAgQIECBAgAABAgQITI/AoqXD01OwUgkMmIAA4IB1uOYSIECAAAECBAgQIECAAIF+EbABSL/0lHr2uoAAYK/3kPoRIECAAAECBAgQIECAAIEBFKhUKkkAcAA7XpOnRUAAcFpYFUqAAAECBAgQIECAAAECBAhMRWBoxWgatQDgVAjdS6AqIABYpXBAgAABAgQIECBAgAABAgQI9IrAkuV2/+2VvlCP/hcQAOz/PtQCAgQIECBAgAABAgQIECBQOoEYASgRINAZAQHAzjgqhQABAgQIECBAgAABAgQIEOigwJIVRgB2kFNRAy4gADjgL4DmEyBAgAABAgQIECBAgACBXhRYagRgL3aLOvWpgABgn3acahMgQIAAAQIECBAgQIAAgbIKLB8ZTcOjlbI2T7sIzLjA7Bl/ogcSIECAAAECBAgQIECAAAECBFoIDC3vzfX//u2OR9LiZSNpnTWemXbcbJ0WLXCJQG8JCAD2Vn+oDQECBAgQIECAAAECBAgQGHiBoeHeCwBWKpV01S8fSQ8tXJa+desD6Tmbrp0+dNguaf8dNxn4/gLQ+wKmAPd+H6khAQIECBAgQIAAAQIECBAYKIGh5b23Ach9Tw7lwb+iI+569OlsJKBxVYWHz94WEADs7f5ROwIECBAgQIAAAQIECBAgMHACS3pwA5Cf3PtkXT9stf7c9MKt16875wuBXhUQAOzVnlEvAgQIECBAgAABAgQIECAwgAIrV1bSsh6bAhzTf39y7xN1vXHo87dIs2bNqjvnC4FeFRAA7NWeUS8CBAgQIECAAAECBAgQIDCAArH+XxZv66n0+2z67yOLltXV6dDdtqj77guBXhYQAOzl3lE3AgQIECBAgAABAgQIECAwYAK9uP7f2NF/8zaYm54/b70B6xnN7WcBAcB+7j11J0CAAAECBAgQIECAAAECJRMY6rH1/0z/LdkLNqDNEQAc0I7XbAIECBAgQIAAAQIECBAg0IsCS1b01g7AC54YSn9YtLyO6rDdtqz77guBXhcQAOz1HlI/AgQIECBAgAABAgQIECAwQAJLe2wE4Njpv5utu3p63lbrDlCPaGoZBAQAy9CL2kCAAAECBAgQIECAAAECBEogsHxkNA2P9s4OII2m/+6z/UZ2/y3BuzZoTRAAHLQe114CBAgQIECAAAECBAgQINCjAkPLR3uqZvc+viQ9urh++u8+22/cU3VUGQLtCAgAtqMkDwECBAgQIECAAAECBAgQIDDtAkPDvRUAHDv9d/N110jbbrzmtDt4AIFOCwgAdlpUeQQIECBAgAABAgQIECBAgMCkBIaW984GII2m/+693Yam/06qZ93UbQEBwG73gOcTIECAAAECBAgQIECAAAECucCSHtoA5J7HlqTHn15R1zN7b7dR3XdfCPSLgABgv/SUehIgQIAAAQIECBAgQIAAgRILrFxZSct6aArw2Om/W6y3RnrWhqb/lvgVLHXTBABL3b0aR4AAAQIECBAgQIAAAQIE+kMg1v+r9MgGwI2n/9r9tz/eJLVsJCAA2EjFOQIECBAgQIAAAQIECBAgQGBGBXpp/b+7H306PbHE9N8ZfQE8bFoFBACnlVfhBAgQIECAAAECBAgQIECAQDsCQz20/t/Y6b9brr9G2nqDue00Qx4CPSkgANiT3aJSBAgQIECAAAECBAgQIEBgsASWrOiNHYBXZvOQf/K7J+vwY/OPWbNm1Z3zhUA/CQgA9lNvqSsBAgQIECBAgAABAgQIECipwNIeGQF41x+eTk+Onf67rd1/S/raDUyzBAAHpqs1lAABAgQIECBAgAABAgQI9KbA8pHRNDzaGzuAjJ3+Oy+b+ru13X9788VRq7YFBADbppKRAAECBAgQIECAAAECBAgQmA6BoeWj01HshMv80/TfJ+rui+m/EoF+FxAA7PceVH8CBAgQIECAAAECBAgQINDnAkPDvREA/O0ji9NTQ8N1mnub/lvn4Ut/CggA9me/qTUBAgQIECBAgAABAgQIECiNwNDy3tgA5KZ760f/xdTfrez+W5r3bJAbIgA4yL2v7QQIECBAgAABAgQIECBAoAcElvTABiArV1bSzWN3/912wx7QUQUCUxcQAJy6oRIIECBAgAABAgQIECBAgACBSQpE4G1ZD0wB/vUfsum/S8dM/7X+3yR71W29JiAA2KEeefTRR9OVV16ZzjjjjHTIIYekjTfeOM2aNSv/Oemkk9p6yoUXXli9p7i32Wfk7UR6/PHH8zo///nPT+uuu27+E8fRjieeqB/63InnKYMAAQIECBAgQIAAAQIECNQKxPp/lR7YAHjs7r/Pzqb/brn+3NqqOibQtwKz+7bmPVbxzTbbrMdqNH51fvrTn6YjjzwyPfLII3WZb7/99hQ/559/frriiivSnnvuWXfdFwIECBAgQIAAAQIECBAg0CmBXlj/L0Yh/nTs9F+j/zrVxcrpAQEBwGnohGc961npuc99brr66qsnXfoPfvCDtOWWWza9f968eU2vtXPh/vvvT4cffnh67LHH0uzZs9N73vOedNhhh+W3xkjGc845Jz388MN5nltvvTVN9Xnt1EkeAgQIECBAgAABAgQIEBg8gaEeWP/vzkcWpUVjpv/utZ31/wbvbSxviwUAO9S3MWV2/vz5+U+MBlywYEHadtttJ136jjvumLbZZptJ3z/ejaeffnoe/It8l156afrzP//z6i1/9md/lnbfffd07LHHppja/MEPfjB1aspx9SEOCBAgQIAAAQIECBAgQIBAJrBkRfd3AB47/XebjdZMW6xn+q8XtDwC1gDsUF9++MMfzkfQ9cNU4Jjye8kll+QtP/jgg+uCfwXHMccck+JapIsvvniVacJFPp8ECBAgQIAAAQIECBAgQGAqAku7PAJwtNHuv6b/TqVL3duDAgKAPdgp012lb3/722nlypX5Y04++eSmjys2L4m8cY9EgAABAgQIECBAgAABAgQ6KbB8ZDQNj3Z3B5A7H86m/y6rH4W4twBgJ7tZWT0gIADYA50w01W44YYbqo884IADqsdjD2qv3XjjjWMv+06AAAECBAgQIECAAAECBKYkMLR8dEr3d+LmsdN/t914rbTZumt0omhlEOgZAQHAnumK+orEyLzYBGTOnDlp4403TnvvvXe+Ft+DDz5Yn3ES3+644478rvXWWy9tvvnmTUvYYost0rrrrptfv/POO5vmc4EAAQIECBAgQIAAAQIECExGYGi4uwHAmP5r99/J9Jx7+k3AJiA92mPXXntttWZPPPFEip+f/vSn6dOf/nQ699xz01ve8pbq9YkePPDAA/kt7ezsu/XWW6df/epXKXYNnkgqntHsnthhWCJAgAABAgQIECBAgACBwRYYWl4/9XamNX710ML09Jg67L2t3X9nuh88b/oFBACn33hCT9huu+3S0UcfnV7ykpekCL5Fuvfee9P/+3//L11++eVp2bJl6a1vfWuaNWtWOvXUUydUdpF58eLF+eHaa69dnGr6udZaa+XXnn766aZ5Gl0o6t7omnMECBAgQIAAAQIECBAgQCAElnR5A5Cf3PtkXUdsv8laaVPTf+tMfCmHgABgD/XjUUcdlU488cQ8uFdbrfnz56djjz02XXnllXlwcHh4OL373e9Or3nNa1pO4a0to/Y4goiRYnrxeGn11VfPsyxdunS8rK4TIECAAAECBAgQIECAAIG2BVZm02+XdXEK8Ei24eXPFtQHAG3+0Xb3ydhnAtYA7KEOizX5YmRfs3TYYYelM844I788NDSULrjggmZZW55fY40/LWa6YsWKlvni4vLly/M8c+fOHTdvbYaYMtzq5+abb67N7pgAAQIECBAgQIAAAQIEBkwg1v+rdHED4F89uGiV6b97bbvRgPWC5g6KgABgn/V0TPstgoTXXXfdpGq/zjrr5Pe1M613yZIled52pgvXVibWF2z1ExuMSAQIECBAgAABAgQIECAwuALdXv/vpnufqMPfYdO10ybr/GkWXN0FXwiUQEAAsM86cdNNN00bbfSnf5GY7I7AxeYf423UETTF5h/W9OuzF0V1CRAgQIAAAQIECBAg0OMCQ11c/29JtvHHT8YEAF+yndF/Pf7KqN4UBAQAp4DXrVuLEYCTff4uu+yS37pw4cL0yCOPNC0mdupdtGhRfn3nnXdums8FAgQIECBAgAABAgQIECAwUYElK7q3A/C1v3ksLR9ZWa3yatlqXHvZ/bfq4aB8AgKAfdanjz32WHr88cfzWm+55ZaTqv1+++1Xva/VNOLaa/vuu2/1HgcECBAgQIAAAQIECBAgQGCqAku7NAIwNh/5wa/qB8PM32bDtNHapv9OtU/d37sCAoC92zcNa3beeedli6T+aZXUAw44oGGe8U7G7sGrrfanrv+nf/qnptkvvPDC/FrkjXskAgQIECBAgAABAgQIECDQCYHlI6NpeLQ7O4Dcet8f02NP/2nDy6IthzzPOvWFhc9yCggA9ki/LliwIP385z9vWZsrr7wynXnmmXme2JX35JNPbpj/wAMPzDcKianCUe7YtPnmm6fXv/71+ekf/OAH6fLLLx+bJX3rW99KcS3SG9/4xhT3SAQIECBAgAABAgQIECBAoBMCQ8tHO1HMpMq46pf1o/+23XittONma0+qLDcR6BeB2f1S0V6v5w033JDuvvvuajWLabpxIs4Xo+mKDCeddFJxmH9GoO6lL31peslLXpIOP/zw9IIXvCDFhh+R7r333jxIF4G6YvTf2Wefnbbaaqv8+mT+52Mf+1i66qqrUkwpPv7449Mtt9ySDjvssLyoCDR++tOfzo832WST9NGPfnQyj3APAQIECBAgQIAAAQIECBBoKNCt9f9+/8SSdMfDf1rrvqjYIc/bPB9EU3z3SaCMAgKAHerV888/P1100UUNS7vxxhtT/NSmsQHA4tpNN92U4qdZWnPNNdNnPvOZdOqppzbL0tb52NX3O9/5TjryyCPzjUA++clPpvipTTHq74orrkjFrsG11xwTIECAAAECBAgQIECAAIHJCnRr/b+xa/+tN/eZaW+7/062G93XRwICgD3SWbvvvnv62te+lgf/YjRe7MAbowhHRkbSBhtskHbdddf08pe/PJ1yyinVkYFTrfpee+2Vbr/99vTZz342D/QV04W33XbbdMQRR6R3vetdaaONbIM+VWf3EyBAgAABAgQIECBAgEC9wJIubACyaNlwuuHuP22qWdTmFTtvmp75DKujFR4+yyswK5tS2p1VN8trqmVtCDzwwAMpRiFGuv/++40ybMNMFgIECBAgQIAAAQIECJRBIHbhvXnBk9kSVzPbmit+/mD6xi33Vx/6jNVmpX88/kVp/TXnVM+1czBn9qy0+7M3bCdrT+Tx9++e6IauV0KYu+tdoAIECBAgQIAAAQIECBAgQGBwBIaGR2c8+DeycmX6tzv/UIe8Tzb1d6LBv7oCfCHQRwICgH3UWapKgAABAgQIECBAgAABAgT6XWBo+ciMN+Fnv3syPblkRd1zX5Vt/iERGBQBAcBB6WntJECAAAECBAgQIECAAAECPSAw1IX1/77/y0fqWr7TZuuk7TZZu+6cLwTKLCAAWObe1TYCBAgQIECAAAECBAgQINBjAktWzOwIwHseezrd9ejTdQoH72r0Xx2IL6UXEAAsfRdrIAECBAgQIECAAAECBAgQ6B2BmR4BOHb034ZrzUnzt92gd0DUhMAMCAgAzgCyRxAgQIAAAQIECBAgQIAAAQIpLR8ZTSOjM7f97x+HVqSf3PtEHf0rd9kszV5NOKQOxZfSC3jjS9/FGkiAAAECBAgQIECAAAECBHpDYGj56IxW5Id3/CGNrvyfgOOcZ6yWXvbcTWe0Dh5GoBcEBAB7oRfUgQABAgQIECBAgAABAgQIDIDATK7/Nzy6Mv3wzj/Uqe73nI3TOms8s+6cLwQGQUAAcBB6WRsJECBAgAABAgQIECBAgEAPCCydwR2Af3zPE2nRsvoNR15l848eeAtUoRsCs7vx0Ol+5ne+8530zW9+Mz3++ONp2223Taecckp68YtfPN2PVT4BAgQIECBAgAABAgQIECDQQmDJDAUAK5VKuuqXD9fVZNct101bb7hm3TlfCAyKQN+NALzmmmvSpptump71rGelp556apV++tCHPpSOPPLIdOmll6arr746felLX0p77713uvjii1fJ6wQBAgQIECBAgAABAgQIECAwMwIrs7X4lg3PzBqAv3lkcVrwxFBdw171vM3rvvtCYJAE+i4A+L3vfS8f2Td//vy0/vrr1/XVbbfdlj7+8Y+niPTHT1yPz5GRkfSWt7wlLViwoC6/LwQIECBAgAABAgQIECBAgMDMCAxlwb/sr+gzkr7/q0fqnrPpOqunF2+9Qd05XwgMkkDfBQBvuOGGNGvWrPSKV7xilX76whe+kAf8Nthgg3TrrbemJ554It18881pww03TMuXL09f/OIXV7nHCQIECBAgQIAAAQIECBAgQGD6BYaW16/HN11PfGzx8vSzBU/WFX9wtvbfaqvNqjvnC4FBEui7AODDD/9pDv+uu+66Sj9deeWVeXDw7W9/e/r/7N0HnFTV3fj/L9v7LssufWGXDtJEUERFxBJbIpaoeTSKNTFFY9THJOYXkyfGRx+JLdFExahJ/Ce2WGJXlCaooNKkL52FrWzvu/zP95JZpmyfuTNzdz/n9Rrn1nPvfd+Rnfnec8732GOPtdZPnz5ddF5bAn744Yc++7AAAQQQQAABBBBAAAEEEEAAAQTsF6gO0vh/H2w86NHSMC46QuaMzbT/AjkCAmEs4LgAYGFhocXp3f03NzdX9u/fb6278MILPchPOeUUa163oSCAAAIIIIAAAggggAACCCCAQPAFqurtbwGoYwx+tKXA4+JOHdNfEmJ6ZA5Uj+tkBoH2BBwXANSWfFrKyso8rmvZsmXWfGpqqkydOtVjXb9+/az56mrPAUA9NmIGAQQQQAABBBBAAAEEEEAAAQRsEdAEIMFoAbh8e5FU1XkmGvnGhAG2XBOVIuAkAccFAAcOPJK1Z9OmTR7O7733njV/0kkneSzXmaqqKmuZjg1IQQABBBBAAAEEEEAAAQQQQACB4AqUVNdLY5O9GUC0wdC7GzyTf0zNSpNBafHBvViOhkAYCjguADhz5kxrPD9N+OFq0bdjxw55/fXXrfH/zjzzTB/mrVu3WstcwUOfDViAAAIIIIAAAggggAACCCCAAAK2CRRV1tlWt6vi9fvLZH9pjWvWej9n4pFGRB4LmUGgFwo4LgB4/fXXW7dp3bp1MnHiRLnkkktEg4K1tbUSHx8v//Vf/+VzG5cuXWotGzNmjM86FiCAAAIIIIAAAggggAACCCCAgH0C9Y3NUlrdYN8B/lOzd+u/Iabl36QhqbYflwMg4AQBxwUA586dK7fccovVCnDXrl3y6quvSlFRkWX9wAMPSEZGhoe7BgZdrQNnz57tsY4ZBBBAAAEEEEAAAQQQQAABBBCwV0Bb//1nOH/bDnSgrEa+2lvqUf83jhlo9RT0WMgMAr1UwJFpcB566CE5/fTT5aWXXpKDBw/KoEGD5KqrrhINDnqXN954Q1JSUkSTg3zzm9/0Xs08AggggAACCCCAAAIIIIAAAgjYKFBYYX/33w83eWb+TYyJlFNGezYQsvESqRqBsBdwZABQVc8//3zr1ZHwpZdeKvqiIIAAAggggAACCCCAAAIIIIBAcAUq6xptz/7b2NQsy7YVelzYaeP6S1x0pMcyZhDozQKO6wLcm28W144AAggggAACCCCAAAIIIICAkwSKgtD674s9h6SittGDZe7Y/h7zzCDQ2wUc2wLQ+8Y1NjbKoUOHrMV9+/aVqKgec2nel8o8AggggAACCCCAAAIIIIAAAmEvcNgM/BeM7L+Lt3i2/hs3MFkGmQQgFAQQOCrg6BaAGzdulJtvvlkmTJggcXFxMnDgQOul0+PHj5cf//jHsmHDhqNXyxQCCCCAAAIIIIAAAggggAACCARF4JDJ/NvQdNjWYxWbBCNr93km/5hD6z9bzancmQKODAA2NzfLbbfdJlOmTJHHHntMNm/eLLpMny7oS6e3bNkijz/+uBx77LFy6623WsuceYs4awQQQAABBBBAAAEEEEAAAQScJxCM5B9LtxV5ZBiON+P+nZCT7jwszhgBmwUc2U/2v/7rv6wMwBrs03LMMcfI8ccfLwMGDLDm8/PzZdWqVVbrv6amJnn00UclLy9PXnjhBWs9/0EAAQQQQAABBBBAAAEEEEAAAfsEGkxijtLqevsOYGpuNjGBxVs8s/+eOLIfyT9sVadypwo4LgD4z3/+U1588UXp06eP1QLwySeflBkzZrTqr0HA73//+/LVV1/Jyy+/LLrv5Zdf3uq2LEQAAQQQQAABBBBAAAEEEEAAgcAIFFfWmwBdYOpqq5ZNB8qlwCvJyGljM9vanOUI9GoBx3UB1oCfljFjxsjy5cvbDP7pNhoYXLp0qYwdO9bqGvzEE0/oYgoCCCCAAAIIIIAAAggggAACCNgoEIzuvx97Jf8Y2jdeRmYm2XhVVI2AcwUcFwBcu3at1frvzjvvlMTExA7ldRvdVovuS0EAAQQQQAABBBBAAAEEEEAAAfsEqusbpbKu0b4DmJqrTP2f7yz2OMZpJvmH9hakIICAr4DjAoD19UfGEJg8ebLv1bSxxLVtQ0NDG1uwGAEEEEAAAQQQQAABBBBAAAEEAiEQjNZ/K3KLPDIMR0b0kZNHZQTi9KkDgR4p4LgA4PDhw60bUVZW1ukbUl5ebm3r2rfTO7IhAggggAACCCCAAAIIIIAAAgh0WkCTdRZV1nV6++5u6N3997jhfSUlPrq71bEfAj1ewHEBwIsvvtgaz++VV17p9M3RBCDaDPjCCy/s9D5siAACCCCAAAIIIIAAAggggAACXRMoq2mQ+kZ7s3/sLq6SnUVVHidG8g8PDmYQ8BFwXADwpz/9qYwYMUI0oYdmA+6oaPBPt83JyZHbb7+9o81ZjwACCCCAAAIIIIAAAggggAAC3RQIRvdf79Z/6YkxMnlIWjfPmN0Q6B0CjgsApqamyocffijTpk2T73znOzJv3jx57bXXZP/+/aJj/DU2NlrTukxb/F122WXWtosWLRLdl4IAAggggAACCCCAAAIIIIAAAoEXaGxqlpKqI+P2B772IzXWNzbL8u2FHtXPHp0pEWYMQAoCCLQtENX2qvBcExkZ2XJiOrbAv//9b+vVstBrQrdZvXq11WrQa1XLrHYP1sAhBQEEEEAAAQQQQAABBBBAAAEEuidQbIJ/zfb2/pUvdpeYDMBNHic4Z2ymxzwzCCDgK+C4AKAG9NyL97z7Otd0Z7Zxbcs7AggggAACCCCAAAIIIIAAAgh0XSAU3X8nDEqRASlxXT9Z9kCglwk4LgB4991397JbxOUigAACCCCAAAIIIIAAAgggEN4CtQ1NUlFrb886DTBu2F/mAXHauP4e88wggEDrAgQAW3dhKQIIIIAAAggggAACCCCAAAIIdFIgGK3/lmwtEPc+gQkxkXJ8dnonz5DNEOjdAo5LAtK7bxdXjwACCCCAAAIIIIAAAggggED4CRRW1tl6Us1mcMElWz2Tf8wamSExUYQ1bIWn8h4jwP8pPeZWciEIIIAAAggggAACCCCAAAIIBF+grLpB6hqabT3whrwyKar0zDB8Gsk/bDWn8p4l4LguwN78mr33yy+/lPXr10tJSYm1Oj09XSZOnCjTpk2T6Oho712YRwABBBBAAAEEEEAAAQQQQACBAAnY3fpPT3PxFs/Wf8PTEyQnIzFAV0A1CPR8AccGAKuqquS3v/2tPP300y2BP+/b1bdvX7nuuuvkl7/8pSQnJ3uvZh4BBBBAAAEEEEAAAQQQQAABBPwQaDJdc0uqPFvm+VFdq7tW1DbIql1HGvy4NphjWv/16dPHNcs7Agh0IODILsBbtmyxWvg98MADUlxcLIcPH271pS0CFyxYIJMmTRLdh4IAAggggAACCCCAAAIIIIAAAoETKK6qEw0C2lk+2V4kjW7HiIroIyeNyrDzkNSNQI8TcFwLwLKyMjn99NPlwIEDVtBPu/peffXVcvzxx8uAAQOsG5Sfny+rVq2S5557zuoavGfPHjnjjDNkw4YNkpqa2uNuIheEAAIIIIAAAggggAACCCCAQCgE7M7+qw1+Pvbq/jvDZP5NjmO4r1Dcb47pXAHHtQC8//77JS8vzxLXLsBr166V2267TU455RQZM2aM9dLpn/70p7JmzRq55557rG11H92XggACCCCAAAIIIIAAAggggAAC/gvUNjRJeU2j/xW1U8POoirZU1LtsYV2/6UggEDXBBwXAHz11Vetfv6XXnqp3HXXXe32+dfxAH7xi1/IZZddZrUW1H0pCCCAAAIIIIAAAggggAACCCDgv0BRZZ3/lXRQg3frv4ykGJk4hJ59HbCxGgEfAccFAHfv3m1dxPz5830upq0Frm1d+7a1HcsRQAABBBBAAAEEEEAAAQQQQKBzAnZ3/61vbJYVuUUeJ3PqmEyJIPmHhwkzCHRGwHEBQFc23/79+3fm+qxtXNsmJSV1eh82RAABBBBAAAEEEEAAAQQQQACB1gXKTWbe2obm1lcGaOlnO4ulur6ppTbN+XvqmM7HAlp2ZAIBBMRxAUDN6Ktl27Ztnb59rm1d+3Z6RzZEAAEEEEAAAQQQQAABBBBAAAEfgaIK+7v/LvZK/qFdfzOTY33OhQUIINCxgOMCgN/73ves8fwefvhhaW7u+GmDbvPQQw9ZYwXeeOONHYuwBQIIIIAAAggggAACCCCAAAIItCmgmXlLqurbXB+IFfnltbLxQLlHVaeR/MPDgxkEuiLguADgt7/9bbnmmmvk008/lXnz5snBgwfbvN78/Hy56KKL5LPPPhMdB1CTgVAQQAABBBBAAAEEEEAAAQQQQKD7Apr5t6HpcPcr6MSe3q3/EmMj5bjh6Z3Yk00QQKA1gajWFobDsr/+9a9tnsapp54qGzZskDfffFNGjBghZ511lsyYMUN0rD/N/KuBv1WrVsn7778vdXV11jrdR+u86qqr2qyXFQgggAACCCCAAAIIIIAAAggg0L5AcZW93X+bmw/L0m2FHidx8qhMiYlyXBsmj2tgBoFQCvQxTXftDdt38+oiIiKsYF5Hu+vpa9CvteK9TrdrbGxsbVOWBVlg3759kpWVZR117969MnTo0CCfAYdDAAEEEEAAAQQQQAABBBDoqoD+zv5yzyGpb7QvlLB+f5nc+/Ymj1O776JJMrxfoseyUM3ERPVxVGtEfn+H6pMSXscN2xaAytTZ2GR727W3LrxuBWeDAAIIIIAAAggggAACCCCAQHgLVNQ12hr806tfvavEAyG7X0LYBP88TowZBBwkELYBwJ07dzqIkVNFAAEEEEAAAQQQQAABBBBAoOcLlFTam/yj2bQwXL37kAfk8Tn9POaZQQCBrguEbQBw+PDhXb8a9kAAAQQQQAABBBBAAAEEEEAAAdsEim3O/ruzqMonw/CM7L62XQ8VI9BbBBhBs7fcaa4TAQQQQAABBBBAAAEEEEAAAT8EKmobTPffZj9q6HjXVV7dfwemxMmQtPiOd2QLBBBoV4AAYLs8rEQAAQQQQAABBBBAAAEEEEAAARUosbn1nx5j9S7P7r/a+q+txJ+6PQUBBDonQACwc05shQACCCCAAAIIIIAAAggggECvFrC7+29eaY3sNy/3MiM73X2WaQQQ6KYAAcBuwrEbAggggAACCCCAAAIIIIAAAr1FoNJk/61rsLf7r3f237SEaBnZP6m3EHOdCNgqQADQVl4qRwABBBBAAAEEEEAAAQQQQMD5AnZn/1WhVV7Zf6cP7ysRffo4H48rQCAMBAgAhsFN4BQQQAABBBBAAAEEEEAAAQQQCGeB4qo6W09PxxfcXlDpcYzpw+n+6wHCDAJ+CBAA9AOPXRFAAAEEEEAAAQQQQAABBBDo6QJVpvtvrc3df7/YXeLBGB8dKccMTvFYxgwCCHRfgABg9+089iwoKJA333xTfvWrX8k555wjGRkZVqYizVY0f/58j23bmqmurpZ//etfctNNN8mMGTOkb9++Eh0dLf369ZMTTzxRfv3rX8vBgwfb2r1Ly7Ozs1vOT8+xrZduR0EAAQQQQAABBBBAAAEEEOi9AsHI/rvKK/vvscPSJCqSkEXv/dRx5YEWiAp0hb21vgEDBvh16evWrZOTTjpJKis9mzxrpSUlJfLpp59ar4ceekiefPJJueyyy/w6HjsjgAACCCCAAAIIIIAAAggg0BkBuwOA2sJwY165x6mQ/deDgxkE/BYgAOg3oW8Fw4YNk3Hjxsn777/vu7KNJeXl5S3BPw0Enn/++TJ9+nSr9V9hYaHVMvCpp54S3e6KK66QlJQUq6VhG9V1evEFF1wg99xzT5vbx8TEtLmOFQgggAACCCCAAAIIIIAAAj1boKa+SarNy87y1d5SaTp8uOUQ0ZF9ZMrQtJZ5JhBAwH8BxwUAc3JyJCIiQt577z0ZNWpUpwT27Nkjc+bMsbq55ubmdmqfrm6kXX+1266+tDXgrl27RM+1s0Wv6dJLL5W7775bJkyY4LPbWWedZQX8LrzwQmlqapIf//jHsm3bNuuafDbuwoK0tDSZOHFiF/ZgUwQQQAABBBBAAAEEEEAAgd4iYHfyD3Vcvctz/L+Jg1MlPiaytxBznQgERcBxAcDdu3dbQa/6+vpOAzU0NFgBOR3nzq7ym9/8xq+qZ82aJfpqr2hrvYsuukheeeUV0UDmV199JdOmTWtvF9YhgAACCCCAAAIIIIAAAggg0G0Bu7v/1jc2yxrTAtC9TM8m+6+7B9MIBEKAETUDoRjEOk477bSWo9nVmrHlAEwggAACCCCAAAIIIIAAAgj0WoHahiapqrO3+++G/WVSZ4KArqLNdo4b3tc1yzsCCARIoFcEAMvKyiyuhISEALGFrpq6urqWg0dG0iS6BYMJBBBAAAEEEEAAAQQQQACBgAoUV3W+5113D7zKq/vvmAHJkhof3d3q2A8BBNoQ6BUBwL///e/W5Q8fPrwNBucsXrJkScvJjh8/vmW6uxNLly6VqVOnSnJysmiAVMct1AzDr732mhx2G4S1u/WzHwIIIIAAAggggAACCCCAgDMFSirtDQA2Nx+WL/Yc8sAh+68HBzMIBEwg7McAnDt3bqsXe80110hiYmKr61wLtbXcjh07pKCgwBo3UBNpOLmsXbtW3nrrLesSJk2aJIEIAO7cudODRJOX6OvFF18UzUb8wgsvyJAhQzy26czMvn372t3swIED7a5nJQIIIIAAAggggAACCCCAQOgEtPtvZV2jrSewJb9CKmo9jzE9m+6/tqJTea8VCPsA4OLFi63gnXtrNJ1etWpVl27aiBEj5Oc//3mX9gmnjTWYef3111sZgPW8fve73/l1ejExMfKtb31LNCiqWYBTU1OltLRUVq5cKX/6059k79698sknn8iZZ55pLdP1XSlZWVld2ZxtEUAAAQQQQAABBBBAAAEEwkjA7uQfeqne2X+HpSfIgJS4MFLgVBDoOQJhHwCcPXu2FQB0kWsXWM3me9xxx7XbAlC3iYuLk0GDBlnZdS+//PJ2t3fVH67vP/rRj2T16tXW6V199dXyzW9+069T/fzzzyUtLc2njjlz5oge65JLLpH3339fNm3aJJrh+MEHH/TZlgUIIIAAAggggAACCCCAAAI9U8DuAKDVsGeXZ/dfWv/1zM8SVxUeAmEfANQWgO4lIuLIsIXPPvusTJgwwX1Vj53+3//9X1m4cKF1fTNmzJDHHnvM72ttLfjnqlTHA9QuwNpqsqSkRJ588km57777RFsNdrZoC8L2inYBPv7449vbhHUIIIAAAggggAACCCCAAAIhEKhrbPLpmhvo09hTUi2FlUeTXGr9jP8XaGXqQ+CoQNgHAI+e6pGpq666ymoB2Ldv7xgX4IknnpBf/OIX1sWPGzdO3n777aC0ZNQuv9pq8vHHH5eqqiqr9eGsWbO8b0eb80OHDm1zHSsQQAABBBBAAAEEEEAAAQTCV+BQVYPtJ+ed/TczKVaGmy7AFAQQsEfAcQFAbfnXW8o//vEP+cEPfmBdrmYw/uCDDyQjIyNol+/ewnL//v1BOy4HQgABBBBAAAEEEEAAAQQQCJ1AcZVnyzw7zmR1K91/dSgvCgII2CNwpD+tPXVTqx8Cb7zxhmhrx+bmZmscw0WLFkmwW9Xxj68fN5BdEUAAAQQQQAABBBBAAAEHCtQ3Ntve/begvFZ2my7A7oXuv+4aTCMQeAHHBQDXr19vjU03evRo6UyrNN1m1KhRMnLkSNm6dWvgBW2oUYN9l156qTQ2Nkq/fv2sln96/sEuGzdubDnk4MGDW6aZQAABBBBAAAEEEEAAAQQQ6JkCh6rr5fBhe69t9W7P5B/JcVEydkCyvQeldgR6uYDjAoB///vfZdeuXVZQb8iQIR3ePt1mzJgx1j66b7iXFStWyAUXXCB1dXWi4/C99957cswxxwT9tMvKyuSf//ynddyEhASZPn160M+BAyKAAAIIIIAAAggggAACCARXoLiy3vYDeo//d9ywvhIRQfdf2+E5QK8WcFwAcMmSJVYSkG9961udvnEaUNMU49qyLpzLmjVr5LzzzrOSbiQmJspbb70lxx13XJdPec6cOZaRduHVYKl3effdd6WmpsZ7cct8ZWWl1QKxuLjYWnbddddJbGxsy3omEEAAAQQQQAABBBBAAAEEep5AQ1OzlNfamwCkrKZBtuRXeOBNz073mGcGAQQCL+C4JCCubryTJ0/utMbEiROtbbds2dLpfbq64fLly2X79u0tuxUVFbVM63Lv5CXz589vWa8Tubm58o1vfENKS0ut5ffcc4/VAnDDhg0e27nP9O/fX/TV1XLffffJFVdcIRdddJGcfPLJVvfopKQk0VZ/2gLxz3/+s+zZs8eqduzYsfLrX/+6q4dgewQQQAABBBBAAAEEEEAAAYcJHKqyv/vvl6b7r3sX49ioCJk0JNVhUpwuAs4TcFwAUFunadGAVWeLa9vy8vLO7tLl7RYuXCjPPfdcq/t98sknoi/34h0AXLZsmRQUFLRscuutt7ZMtzVx9913dzs4V1JSInrO+mqrnHrqqfL8889LejpPY9oyYjkCCCCAAAIIIIAAAggg0FMEik0A0O6yeneJxyGmZKVJjAkCUhBAwF4BxwUA+/btK9q67uDBgzJlypRO6ei2WpKTGVRUHRYsWGB1h165cqVoq0j11JaHOtafJvs44YQT5Dvf+Y6cddZZVldi3YeCAAIIIIAAAggggAACCCDQcwUaTfdf7Z5rZ6mpb5L1+8s8DkH2Xw8OZhCwTcBxAUDN/qsBKx3HTrvMdqa888471mZ2ZtLVLr7e3Xw7c26ubbRFoHerQNe6rr4vXry43V00oQdJPdolYiUCCCCAAAIIIIAAAggg0KsESoKQ/XfdvlJpaDqaYjjSjFs/1bQApCCAgP0Cjmtnq0E/Tejx5JNPyqZNmzoU+vrrr+Wpp56yWrKdffbZHW7PBggggAACCCCAAAIIIIAAAgj0NoFDVfa2/lNP7+y/EwanSFKs49ol9baPBtfbQwQcFwC86aabRDPk1tbWyty5c+XNN99s81a88cYbcsYZZ1gZb+Pj4+WHP/xhm9uyAgEEEEAAAQQQQAABBBBAAIHeKNDUfFhKTQtAO4t2Mf5q75Gkl67jTM/u65rkHQEEbBZwXKg9IyPDylL73e9+10qaccEFF8iIESOsbLaDBg2yuA7EO5V3AABAAElEQVQcOCCaVGPnzp1Wa8E+plnxn/70JxkwYIDNnFSPAAIIIIAAAggggAACCCCAgLMEDpngn4kB2lo2HiiXajMGoHuZPpyEk+4eTCNgp4DjAoCKccUVV0hzc7Noa8Dq6mrJzc2VHTt2eDhpN2Et2lpQg39XXnmlx3pmEEAAAQQQQAABBBBAAAEEEEBApLjS3tZ/arxq1yEP6pGZiZKeGOOxjBkEELBPwHFdgF0U2gJw+/bt8rOf/UwmTZpkLdagn760xd/kyZPlrrvusrYh+OdS4x0BBBBAAAEEEEAAAQQQQACBowLB6P7bbH6nr95dcvSgZorsvx4czCBgu4AjWwC6VAYOHCj33nuv9WpsbJSSkiP/oKSnp0tUlKMvzXWJvCOAAAIIIIAAAggggAACCCBgm0Awuv/uKKw0Ywx6JhkhAGjbLaViBFoV6DFRMg349e/fv9WLZCECCCCAAAIIIIAAAggggAACCPgKhKL77+C0OBmcFu97MixBAAHbBBzbBdg2ESpGAAEEEEAAAQQQQAABBBBAoBcIBKP7b4PJ/rsyt9hDk9Z/HhzMIBAUAQKAQWHmIAgggAACCCCAAAIIIIAAAgiEl0Awuv++ue6AFFbWeVw42X89OJhBICgCju4C/PHHH8trr70ma9eulaKiIqmpqbGSgLQlp8lBNGMwBQEEEEAAAQQQQAABBBBAAIHeLlBSZW/238KKWnntq/0ezKP7J4lmAKYggEBwBRwZACwoKJDLL79clixZYmlp5t/Wigb83NfpPAUBBBBAAAEEEEAAAQQQQACB3i6g3X8P2RwAfG7lbqk3XYBdRX+SX3NSjvDb3CXCOwLBE3BcALChoUHOOeccWbNmjRXcmzp1qgwZMkTeeust6x+RK6+80soG/OWXX8qBAwesZdOmTZOJEycGT5UjIYAAAggggAACCCCAAAIIIBDGAnZ3//1i9yHRl3s5c/wAycmg9Z+7CdMIBEvAcWMAPvvss/LVV19ZPs8884xooO++++5r8Xruuefk3//+t+zfv1/+9a9/yaBBg2Tjxo1y/vnni25PQQABBBBAAAEEEEAAAQQQQKC3C9jZ/beusUmeW7HLgzg1PlounZ7lsYwZBBAInoDjAoCvvPKKpXP22WfL1Vdf3a7UvHnzrG7CMTExMn/+fNm2bVu727MSAQQQQAABBBBAAAEEEEAAgZ4uYHf339fX5Pkk/rjihGGSGOu4Tog9/aPA9fUiAccFADXhh44XoF19WyvuY/7p+pEjR8ott9wiVVVV8sgjj7S2C8sQQAABBBBAAAEEEEAAAQQQ6DUCdnb/PVBWI/9em+dhOW5gspw8KsNjGTMIIBBcAccFAEtKSiyhnJycFilt4ecq1dXVrsmW99NPP92a/uCDD1qWMYEAAggggAACCCCAAAIIIIBAbxSwq/uvNsh59pNd0mgSjLhKhEn8cS2JP1wcvCMQMgHHBQBdwT7Xu8qlpKS0AOrYf94lLi7OWtTaOu9tmUcAAQQQQAABBBBAAAEEEECgpwrY2f33850lsm5/mQfdORMHSVZ6gscyZhBAIPgCjgsADhs2zFLKz89v0RowYIAkJydb85999lnLctfEhg0brElSjbtEeEcAAQQQQAABBBBAAAEEEOiNAnZ1/61taJK/frrbg7RvQrRcPG2oxzJmEEAgNAKOCwBOmzbNknJlAnaxzZ49W7S5sY7zV1dX51ospaWlcv/991vjBk6YMKFlORMIIIAAAggggAACCCCAAAII9DYBu7r/vvLlPvGu+7szsyU+JrK3EXO9CISlgOMCgDqenwb63nrrLQ/Q73//+9a8BgYnT54sd9xxh/zgBz+QSZMmydatW611V111lcc+zCCAAAIIIIAAAggggAACCCDQWwTs6v67t6Ra3ll/0INx0pBUmTki3WMZMwggEDoBxwUA582bJ9oNeN++fZKbm9sid95558m1115rBQe3bdsmDz74oDzxxBPiGvfvrLPOkptuuqlleyYQQAABBBBAAAEEEEAAAQQQ6E0CdnT/1QY6z6zYKU3m3VWiTOaPa2ZlWz3xXMt4RwCB0ApEhfbwXT96Wlqa7Nq1q9UdFy5cKCeeeKLo+9dffy2NjY0yevRo0ZZ/t9xyi0REOC7e2ep1shABBBBAAAEEEEAAAQQQQACBrgp4d9Ht6v6tbf9JbrFsOlDhser8yYNlUFq8xzJmEEAgtAKOCwB2xHXdddeJvigIIIAAAggggAACCCCAAAIIIHBEwI7uv9X1jfJ3r8QfGUkxMu/YwbAjgECYCdAkLsxuCKeDAAIIIIAAAggggAACCCCAQKAF7Oj++9LqfVJW0+Bxqlebrr+xUST+8EBhBoEwECAAGAY3gVNAAAEEEEAAAQQQQAABBBBAwE6BQHf/3VVcJe9t9Ez8MW1YmkwfTuIPO+8jdSPQXQFHdwFuamqS119/XT788ENZv369lJSUWA7p6ekyceJEOeOMM+SCCy6QqChHX2Z37y37IYAAAggggAACCCCAAAIIICDa/be02rOlnj8szSbhx1+W7zRJOI/WEh3ZR64+MfvoAqYQQCCsBBwbGXvjjTfkRz/6UUuWX1XV7ENa+vTpIytWrJAnn3xSBg0aJH/84x9FswdTEEAAAQQQQAABBBBAAAEEEOhtAtr9V4OAgSpLthbKtoJKj+rmTR0i/VPiPJYxgwAC4SPgyC7AjzzyiFx44YVW8M8V9MvOzpaZM2daL53Wouvy8vLk4osvlocffthaxn8QQAABBBBAAAEEEEAAAQQQ6E0Cgez+W1nbKP/4fI8H30AT+NPMvxQEEAhfAccFAD/77DO57bbbrOBecnKy3H///ZKfny+5ublWqz9t+afTukzXpaamWtvecccdovtSEEAAAQQQQAABBBBAAAEEEOgtAoHu/vvKV/ukwgQB3ct8k/gjJspx4QX3S2AagR4v4Lj/Qx988EFpbm62Ansa7NPAXkZGhs+N0mW6TrfRIKDuo/tSEEAAAQQQQAABBBBAAAEEEOgtAqUB7P6rXYkXbcr3oDs+J12mZKV5LGMGAQTCT8BxAcBly5ZZY/zdeeedMmHChA5Fx48fL7qtdgdeunRph9uzAQIdCewvreloE9YjgAACCCCAAAIIIIAAAmEhUFxVH7Dz+PfaPGloOjqWYGREH7nyhOEBq5+KEEDAPgHHBQAPHTpkaZx22mmdVnFtW1pa2ul92BCBtgQOltVIYUVdW6tZjgACCCCAAAIIIIAAAgiEhUAgu/9q678PvVr/zRmTKZnJsWFxrZwEAgi0L+C4AKBm9e1u8Wff7h6T/XqmwI7CSqmq8xz3omdeKVeFAAIIIIAAAggggAACThUIZPffN71b//XpIxdMJfGHUz8bnHfvE3BcAPCMM86w7tKSJUs6fbcWL15sbTt37txO78OGCLQn0GxavW/JrzDN35vb24x1CCCAAAIIIIAAAggggEDIBALV/VcDiR9uKvC4jlPHauu/OI9lzCCAQPgKOC4AqBmA4+Pj5b777pOtW7d2KKvbaDbgxMREKylIhzuwAQKdFKhraJZt+ZXW+JKd3IXNEEAAAQQQQAABBBBAAIGgCASy+++b6w5IvVvjh0jT+m8erf+Cch85CAKBEnBcAHDs2LHy8ssvW9c/c+ZMefjhh6WkpMTHQ8cKfOSRR2TWrFnWuhdffFF0XwoCgRQoq2mQPSXVgaySuhBAAAEEEEAAAQQQQAABvwUC1f1X6/lgo2fm39ljMmj95/cdogIEgisQFdzD+X80VzfezMxM2bZtm2iLwNtvv11ycnKkf//+Vobg/Px82blzZ0vLrFGjRskDDzxgvVo7gz7m6cWiRYtaW8UyBDoUyCutlcTYKMlIYvDbDrHYAAEEEEAAAQQQQAABBIIiEKjuv2+t9239d8HUIUG5Bg6CAAKBE3BcAFDH89OAnascPnzYCvTl5uaKvlor27dvF33ptu5F69Fl7vW5r2cagc4K7CiskvjoSCsQ2Nl92A4BBBBAAAEEEEAAAQQQsEMgUN1/tceTd+u/U0ZnyIAUxv6z475RJwJ2CjguADh79mwCdnZ+Iqi7WwL6B3arSQoyaUiqREU6rmd9t66ZnRBAAAEEEEAAAQQQQCA8BQLV/ffNdXlS13g08WGEaYsz71ha/4XnXeesEGhfwHEBQFdG3/Yvi7UIBF+gVpOCFFTKuIHJBKmDz88REUAAAQQQQAABBBBA4D8CJVX1fluUt9r6L5PWf37LUgECoRGgqVJo3DlqDxUorW6QvSU1PfTquCwEEEAAAQQQQAABBBAId4FGk633kPld4m/Rsf98Wv8x9p+/rOyPQMgECACGjJ4D91SB/aU1UlxZ11Mvj+tCAAEEEEAAAQQQQACBMBY4UFYrOkSRP6W8tkHe+/qgRxUnj8qQgamM/eeBwgwCDhIgAOigm8WpOkcg1yQFqa5vdM4Jc6YIIIAAAggggAACCCDgeIEG0/pPA4D+lrfWebb+0zycjP13RDUmqo+MHpDsLzH7IxB0AceNAegu1NzcLBs3bpQdO3ZIRUWFNDU1ua9udfqqq65qdTkLEQikgD5x23KQpCCBNKUuBBBAAAEEEEAAAQQQaF8gz/RG8rf1X4Vp/ff+Rt/Wf4NS49s/eC9YmxQbJWMGJklsVGQvuFousacJODIAWF1dLffcc48sXLhQiouLO31P+pjHFgQAO83Fhn4KaFKQ7YWVMtY8HdLPHgUBBBBAAAEEEEAAAQQQsEug3mTrzS/3fyiit83Yf/pbxlX0p8yFZP6VjKQYGZGZJJGaCpmCgAMFHBcArKyslNNOO02+/PJLOXzYv3ENHHi/OGWHCRyqOpIUZFi/BIedOaeLAAIIIIAAAggggAACThLQscgD0frvXa+x/04amSG9vfVfVnq8DO3Lbzon/f/AufoKOC4AqC3/vvjiC+tKZs6cKTfeeKNMmTJF0tLSJCKCIQ19bzFLQi2gf4h1EN2hfeMlLSEm1KfD8RFAAAEEEEAAAQQQQKCHCdQ2NElBuf9j/729/iCt/9w+G9rab1T/JElP5HecGwuTDhVwXADw5ZdftrpTnnvuufL6668T9HPoB6+3nXZFbaNsOlAhyXFRkpWeIKnx0b2NgOtFAAEEEEAAAQQQQAABmwS00YGfiX+l0vxm8c78O8u0/huc1jvH/ouNjpBxA5MlIcZxYRObPmVU63QBxzWZ279/v2V+8803E/xz+qfPgeeviT0e+mCr6Pga3SkaCNyYV269tFUgBQEEEEAAAQQQQAABBBDwR0Bb/xVWBGDsvw0HpMbU5So60l1vHfsvJT5KJg1JJfjn+jDw3iMEHBfK7t+/v+zbt08yMjJ6xA3gIpwhoONNPv/ZHvntmxulzgT/kuKi5eoTs7t98mU1DVK2v8F0CY62WgRqNikKAggggAACCCCAAAIIINBVgX2Hqs34+F3dy3P7yrpGeXeDZ+bfE0f2kyG9sPXfgJRYyclIJJGj50eEuR4g4LgWgMcff7zFvmXLlh7AzyU4ReC+dzbLL1/bYAX/9Jz1j+OXuw/5ffql1Q2yfl+ZaMvCKvNHl4IAAggggAACCCCAAAIIdFagur5RiirrO7t5m9u900rrv4uOHdrm9j1xhWY7HpGZaGX67aMzFAR6mIDjAoC33nqrdQv++Mc/kgW4h30Yw/lyLpo2VGKjPP93+fPSXCmp8v+PrV631rPOBAK35leI/hGnIIAAAggggAACCCCAAAIdCew7VON36z9tiODd+m+mtv4zSQx7S4mO7CPjB6XIgJS43nLJXGcvFPCMaDgAYNasWXL//ffLihUr5PLLL5fS0lIHnDWn6HSBsWbw11+eP8HjMnQ8v8cXb5dmf0fbdau12Dy900Dg9oJKaWjq3jiDbtUxiQACCCCAAAIIIIAAAj1UQAN3+vvB36Kt/6rrPcf+u+jYIf5W65j942MiZaIZ749EjY65ZZxoNwUcOfDY7bffLiNHjpQbbrhBsrKy5Mwzz5QxY8ZIQkJChwy/+tWvOtyGDRBoTeDKE4bJsq2F8v7G/JbVX5uEHm+sy5N5UwP3B1LH79BBfEur62V4v0TJTI5tOR4TCCCAAAIIIIAAAggggIAK7DVj//lbNIj4jtfYfyeMSJehfTv+be3vscNhfw36jRmQJFGRjmsbFQ58nIPDBBwZACwoKJBXX31VysrKTOurZnn99dc7zU4AsNNUbOgloONA/N8lk+WL3y+RYreuvy+t3ivHmObiowcke+3h32xD02GrJWBRZZ01CG1cdKR/FbI3AggggAACCCCAAAII9AiBitoGOVTV4Pe1vN1q67/eMfYfyT78/vhQgcMEHBfmLi4ultmzZ8vzzz8vTU1N1jiAmqG1sy+H3R9ON8wE0hJi5JYzRpmMUEdPTHsA/+Gj7baN3aeJQrRbcF6pju/hZ3qvo6fNFAIIIIAAAggggAACCDhUYG9Jjd9nrkHEd9Z7Zv49PiddstJ7dus//S2XnZFAsg+/P0FU4DQBxwUA7733Xtm6dasVCLnkkkvko48+Eg0KajBQWwN29HLaDeJ8w0/gmMGpcqHXmBiFppXewmU7bQvQNZko4+7iatmwv5xsweH3keCMEEAAAQQQQAABBBAImkBZTYPoy9/y5roDUtPgOfbfxSb5YU8ukRF9ZKzpuTUotfckOOnJ95Nr65qA47oAv/HGG6b1VR+58sor5bnnnuva1bI1AgESuOjYofK1CcZtMVl7XWXljmKZPDRV5ozt71oU8PdKM0bH+v1l5g9WnGSZcTkizB8wCgIIIIAAAggggAACCPQegb0l/o/9pwHE9772bP03y2T+7cmt/2KiImScSe6YGOu4MEjv+XBzpbYKOK4F4P79+y2Qa6+91lYYKkegPQF9cvSjuaMk0WSMci/PrthlddV1Xxboae0FnFdaK2v3lUqZ6R5MQQABBBBAAAEEEEAAgd4hoIkCK2ob/b7YN9bmSV1jc0s92i22J7f+SzJBv0km0y/Bv5ZbzkQvFHBcADAjI8O6TcnJgU240AvvPZfsp0BGUqzcMHuERy36R/TRj7ZJQ9PRP6YeGwRwprahWTYeKLcShQTjeAE8dapCAAEEEEAAAQQQQACBbggEYuy/EpPQ8IONnq3/Zo/OlEFpPbNbbHpijEwYnCLaApCCQG8WcNz/Aaeccop1vzZs2NCb7xvXHiYCJ+T0k9PHeXb51bH6/r/P9wTtDAsr6kySkFLRp4EUBBBAAAEEEEAAAQQQ6JkCGrjTIYH8La+v2W8aLBxNLhhpmv9d5DXGub/HCJf9h5ig5ljT7Vd7cFEQ6O0CjgsA3nbbbRIdHS0LFiyQ2tra3n7/uP4wEPjuicNF/7C4l3c3HJQvdx9yX2TrdH3jYdl8sEI0GEhBAAEEEEAAAQQQQACBniVw2IwDFIix/4pM8sKPNhd44MwZmyn9U+I8ljl9Rrs0j8xMlGH9enZGY6ffJ84/uAKOCwBOmzZNFi5caGUCPuuss6z34JJxNAQ8BWKjIuXm00dLdKTnU6U/L80VfUoXrKJjA24vqLR9DMJgXQ/HQQABBBBAAAEEEEAAgSMCRZX1Ul1/NGNvd11e/Wq/NDYfbf0XZVrGXdjDWv9Fmd9l4wem9LigZnfvOfsh4BJwXPobV/KPCRMmyPLly0XfJ0+eLGPGjJGEhPaj+5o9+Omnn3ZdO+8IBExgWHqCfHfmcPnLJ7ta6tTBeR9fvF1+cc74oGbr1S7IOibg8H6JLefCBAIIIIAAAggggAACCDhTQFv/7Tvkf+bf/PJaWbKl0ANhrhnOqJ8Z27ynFG2UoeP9JcQ4LtTRU24B1xHGAo77v+LZZ58VDeRp0ffm5mZZu3at9WrPWf/RJADYnhDr/BU4Y/wAMxZfmax26/r7dV65/HtdnlwwdYi/1Xdpf80SrON6aLN31/8vXaqAjRFAAAEEEEAAAQQQQCAsBHSYH00A6G/515f7pEm7Df2naLBsXg9q/act/8YNIvjnur+8I+At4LgA4LBhw8IyoFFQUCCff/659Vq1apXoq7i42PK++uqrRQOXXSnvvPOOPPnkk1Y9hYWFkpmZKTNmzJAbb7xRzjnnnK5U1e621dXV8sc//lFeeuklyc3Nlbq6OsnKypLzzjtPbr75Zhk+fHi7+7PyqIAG2r43e6Ts+Nc6j66/L67eKxPMH6LRA4KbuVq/KDSaAPno/gx6e/QuMYUAAggggAACCCCAgHMEqusbZU+J/63/8kprZNn2Io8LP3PCQOmbEOOxzKkzmuRjnEn2kRTruBCHU8k5bwcKOO7/jl27doUl84ABAwJyXtqiUYN83l2V9+/fL/p67bXX5Prrr5cnnnjCdCv1bwjH7du3y7nnnivbtm3zOPctW7aIvnSsxeeff17OP/98j/XMtC2QFBclPzxtlNzz1kZxPVzTITb+8NF2uefCiZISF932zjasOVTVIJsOlFuZr6Ij/fu82HB6VIkAAggggAACCCCAAAJtCGjwT7/Lu2fsbWPTDhe/Ylr/uX6f6MaxURHyrSmDO9zPCRtogl/N9Jsc5N9aTrDhHBFwFyAi4K4RoGltpagJSrpT7rrrrpbg37HHHiv/+Mc/rFaF+q7zWjQw98tf/rI71bfsU1FRYbXycwX/brjhBlm0aJGsWLFCfve730lSUpKUl5fLZZddJmvWrGnZj4mOBbS1n/dAuoUm29ZP/rlGXli1R8prGjquJIBb6FiE2hW5rtH/QYMDeFpUhQACCCCAAAIIIIAAAm0I1JiEHxr8q2882mW3jU07XKzZg1fmHumd5tr47IkDJTU+uI0TXMcO5Lsr+NcTriWQLtSFQGsCjmsB2NpFhMOyX/3qV1YXXe2mq60BtaViTk5Ol05t69atsmDBAmuf6dOny9KlSyU+Pt6a13q/9a1vyamnniqrV6+WBx54QDQhyqhRo7p0DNfGur8eT8v//d//yR133OFaJSeeeKLMmTPHOpZ2Ef7JT34iixcvblnPRMcCFx07VL7eXy5b8itaNq5paJLX1uTJOxsOyulmsN3zJg+W9MTgNLnXLxAbzPmMH5TMgLgtd4QJBBBAAAEEEEAAAQTCT0C/u288UBaQ4J9e3cva+s/tMuOjI+X8Sc5v/aepAXSYpbQe0o3Z7RYxiYAtArQADBDrb37zG6urrD9dgR9++GFpbGy0zugPf/hDS/DPdYqa5ViXa9HtHnroIdeqLr03NDTIo48+au0zfvx4ue2223z2nzVrllx33XXW8iVLllhjEfpsxII2BXQMih/NHWW6/PrG2Osam+VtEwS85Z9fydPLd0phRW2b9QRyRb05rrYELK8NbgvEQF4DdSGAAAIIIIAAAggg0JMFAh3821VcJZ/vLPEgO3fSQNGhi5xcNPg3qn9S0BpUONmKc0fAJUAA0CUR4nfNUvz6669bZzFu3DiZOXNmq2eky8eOHWut0+11v66Wjz/+WMrKyqzdNEFJW2MJzp8/v6XqV199tWWaic4JZCTFyj3zJsrMEeli/j75lEYzOOCHm/Ll1hfWyp+X5MoBMzCv3aXRZAbeZIKAh6rq7T4U9SOAAAIIIIAAAggggEAXBGpNj6GNAer26zrsS6v3uSat98TYSDl30iCPZU6cGZGZKPp7i4IAAp0XCNsAYGRkpOgrKsrzyYRreXfevevqPJP9W+7cuVPy8vKsA2k33/aKa70mBelOUpTly5e3VO+qq2WB24R2Q9ZWh1o++eQTtzVMdlYgMzlObjl9jDzw7Skye3SG6BgV3qXJBHGXbC2U215eK49+tC0gWb68j+E+r0lJtGtyQZBaHrofm2kEEEAAAQQQQAABBBDwFdDgn/bW0V47gSrbCyrlyz2HPKrTrr8JMZ6/sT02cMCMBv/6m99ZFAQQ6JpA2AYAtWWb6+V+Sa5l3X13ryucpjdu3NhyOtoCsL3ivn7Tpk3tbdrqus4eSwOmrjEGu3OcVg/eAxZ2J5PvkLR4uWnOKHno0qnW+H9RrUQCtTGnDs575yvr5Pfvb5GdRVW2aemxcguqJLewUhqbAvclw7YTpmIEEEAAAQQQQAABBHqogB3BP6V66Yu9HmLJptuvJv9wcsnOSJABKQT/nHwPOffQCYRt6P/uu+9uVaWt5a1u7KCF+/btaznboUOHtky3NpGVldWyeO9ez3/UW1a0M+E6VmJioqSlpbWzpYgea926dVJYWCh1dXUSG9u5ZtauY7RV+YEDB9paFfbLszMSrXH0upORq7/5Y3X9KSPkomlD5c11ebJoU4HUtxKAW737kHxhXmdOGCCXzciy7SldQXmdlJmsxCMzkiQ1wflZwML+w8MJIoAAAggggAACCCDgJmBX8G/zwXJZt+/IsE+uw31rymCJMwlAnFqG9UuQQalHkmQ69Ro4bwRCKUAAMJT6bseuqDiaLTYpKcltje+kBu5cpbKy0jXZ6XfXsTo6jlbofazOBgDdg5SdPjGHbBgdGSHZ/RJla37X7V2XqNl/rzoxWy6YOkTeXn9APtiYL5ol2L2YRnryvlm+aleJzJ+VIzOy+0ofHe02wKWuodkaa2RgapwMS08QTWBCQQABBBBAAAEEEEAAAXsFjo75F/geOd5j/6XFR1uNC+y9IvtqH9o3XrRXFQUBBLovELZdgLt/Sc7cs7b2aCbYmJiYdi/CPQhXU9P1xBGuY3V0HD0Jf4/V7oU4eGU/M+BsRlL796kzl5dq/hB/5/hh8uh3jpVLjhsqOiivdzlU3SAPfbhVFry/VYoq67xXB2z+YFmteUpYSpbggIlSEQIIIIAAAggggAACrQu4gn/6MD7QZcP+MusBv3u9F0wdLLFRvr813LcJ1+nBaXGSZRoqUBBAwD+BsG0B6N9lOW/vuLij4xjU17efoVW74rpKfHzXn4K4jtXRcfQY3T1WR12TtQvw8ccf77oMR7770xXY+4KTYqPkYtMt+NyJg+Tdrw/Ka1/t9+karAP4fp1XJpdOz5JvHDPQlpZ6tdoa0Aw+PNB0VdbWgBG0BvS+VcwjgAACCCCAAAIIIOCXgJ3BPx0r33vsP+19NHfcAL/OOVQ7a6s/7fpLQQAB/wUIAPpvGJAakpOTW+rpqFtvVdXR5BCd6cbbUvF/JlzH6ug4unl3j9XROIbe5+TEee0KnGPGztty8Gj3bX+vIz4mUi48dojMGtlP/vLJTp9xO+pMVrC/fbpblm8vkutPzpERme13F+/O+WiCkAOmNWCpjg1oMmwlxzE2YHcc2QcBBBBAAAEEEEAAAW8BO4N/eqy1Ztw/76GK5plhh2KinNX5T0c+GmHGXtcx1CkIIBAYAWf9KxCYaw7LWtwDZh0l0HBvXdedsfZcx9LgXmlpabsermNlZmZ6dAdud6detFKfpgWiK7A3mWa2+tnZ4+THc0eJdhP2Lpoh+Jevb5DnVu6SmnrPsQO9t+3uvNb7tWkNuKe4WpqbdURCCgIIIIAAAggggAACCHRXoMwM7aM9euzo9qvn1GS+s7+42jNJZKYZuui0sZndPeWQ7BcV2UfGD0wh+BcSfQ7akwUIAIbJ3Z0wYULLmWzevLllurUJ9/Xjx49vbZN2l3X2WI2NjZKbm2vV1Z3jtHsSPWildgWOiQp84gxN+DFrZIYs+PYUOX1cfx8xban37oaDcvvLa2W1SRRiR9Fj7C+tkfVmHJHKukY7DkGdCCCAAAIIIIAAAgj0aAHtlru3pFo2mcy89Y32PVj/99o80YYC7uXCaUMkyvRcckqJjY6QYwanSGqCbyMIp1wD54lAuAo451+CcBUM0Hnl5OTI4MGDrdqWLFnSbq1Lly611g8ZMkSys7Pb3ba1lSeffHLL4vaOtXr16pYuwCeddFLLPkx4Cri6AnsuDdycjg94/Skj5NffPKbVzFclVfXy+w+2yu/f3yLFNiUJqTatAXUwYf3iol9gKAgggAACCCCAAAIIINCxQF1jk5WQY9+hGvM9uuPtu7uFBv5e/mKfx+6DU+Nk9mjntP7T3z0TB6dKQgwjlXncSGYQCJAAAcAAQfpbjbb2uuCCC6xqtIXfp59+2mqVutzVAlC31/26WubMmSOpqanWbs8991ybAZ1nn322peoLL7ywZZoJXwHtCpyZ7H9WYN+ajy4ZOzBZ7rtoklxmkoBEm2bx3mX17kPy81fXy+YD5d6rAjKvX1j0i8s6M64IrQEDQkolCCCAAAIIIIAAAj1YoLS6Xtab787lNfb2pKk344Q/vni7NLlFGPVn4vdPHWlL4kA7bpn+nppgWv45baxCOyyoEwG7BAgA2iXbjXp/8pOfSGTkkdTsP/7xj6WmpsajFp3X5VqioqJEt2+tzJ8/3woManBw8eLFPpvExMTIzTffbC3ftGmTLFiwwGeblStXytNPP20tP/XUU2XGjBk+27DAU2B4P3u6ArsfRZvvzzNJQv7v4ikyaciRIK77+oraRrnn7U3y8eYC98UBnXa1BmRswICyUhkCCCCAAAIIIIBADxHQHjP6XXnTgQppaLKx2d9/vHTcP31Q714umDJERg84mmjSfV24TQ9Oi5MxA5IcE6wMNz/OB4HOCtC2trNSHWy3fPly2b59e8tWRUVFLdO63L01na7QIJ13GTNmjNxxxx1y3333iXa/1W63d955p4wcOdIai+/++++Xr776ytpNtxs9erR3FZ2e1/1feOEF2bp1q/z3f/+3de6XX365xMfHy8cffyz33nuv6BiAOv/www93ut7evKGrK3AgswK35TnQNOf/+Tnj5JPcYvnbyl1SbgJ/rqKD/z65bIf5ElAtV5wwXCIifFsLurbt7rs+XNSxAQ+Zp5ojyBTcXUb2QwABBBBAAAEEEOhhAtrld1t+peiD+WCUjab3z9vrD3gcani/BLnYjP0X7kVbKeaY8dQ1ASIFAQTsF+hjnk7Y/0jC/usI+RE0oKfdaTtb2mJvbm6WG264Qf7yl7+0WdV1110nTz75pAnstN6A0/1cNJinXX5bKxqYPPfcc2Xbtm2trZaUlBR5/vnn5fzzz291vT8LNdOxK4OxZhp2ZSb2p85w2Xd7QYUUVtQH7XQqzZeLx0yT/zV7fTM6TxmaKjefPtrWcTT0D/cgE5DM6ptgS7AxaJAcCAEEEEAAAQQQQAABPwS0y+/2gsqgtPrT06yub5SfvbJeCt3GAY8yD//vvXCSZKUn+HEl9u8aac5TW/2lJdg7jJL9V+KMI/Tk39/OuAPhcZatR5DC49xaPYu//vWvoq/y8s6Pc1ZZWWnto/uFe9Ggnna9feutt6wxATUxiHbZ1Xcd8+/tt9+WhQsXthn868r1jRo1ympRqC0Lp0+fLmlpaZKQkCBjx46VW2+9VdatW2dL8K8r5+jEbbOD0BXY3SUpLkruOGusnDdpkPtia3qtGXPk/72+QQ6W1fqsC9QCfYSQV1or60ySkPLahkBVSz0IIIAAAggggAACCDhCINhdfl0of1u52yP4p8svm5EV9sE/HedPM/0S/HPdSd4RCI6A41oAaoBMx7Zbv369TJgwoVNKubm5VndZ3Ve7tVJCL9DTn0AcMpl5Nx+sCDr04i0FsnD5TtFuwO5FM2r95IzR5g+t77iB7tv5O62tAQeaJvz6xFGf6lEQQAABBBBAAAEEEOjJAsHu8uuyXL27RH7//lbXrPU+ziQN/H/nTQjrXjn6u0STG5Lsw+PW2T7T039/2w7YQw7guBaA/ri31e3WnzrZF4HWBPoGIStwa8edM7a/9Uc/xbQKdC+atfd/394sH2zMd18c8GltDXjAtDZct69UympoDRhwYCpEAAEEEEAAAQQQCBuBCtP7RbP8Bmu8P9eFl5vv2U8t2+matd7joiPkB3NGhm3wzzVsEJl+PW4bMwgEVaBXBACbmposVM2cS0EgWALB7grsui59onbPvIk+Tf+bTHTuL5/slGfMy7uFoGvfQL3XNjTLxrxy2VlUZfuxAnXO1IMAAggggAACCCCAQGcFahuaRJP/BSPLr/s5aaOWhct3iAYB3ctVJ2ZLZnJ4JtNINo0TJg1JlWyT8INeQu53jWkEgivQKwKAW7ZssVTT09ODq8vRerVAVGSEjMhIComB/vH/n28dI9OH9/U5/vumFeB9724WbRVod9GxBzeZzGR2Bxztvg7qRwABBBBAAAEEEEDAJdDQ1GwN9xPs4J8ef+m2Ilm165DrVKz348x3/jljMj2WhcNMdGQfGZmZKBNN8C/RdP2lIIBAaAXC/v/CpUuXtiq0atUqKSoqanWda2FdXZ3o+H8LFiywxg2cOnWqaxXvCARF4EhX4FiTFbguKMdzP0hcdKTceuYYeXH1Xnl9TZ77KtlgEnb8v9c2yB3fGCuD0+I91gV6RrtEaBBw/KAUnvgFGpf6EEAAAQQQQAABBIIq0GzG2taWfzX1R3qZBfPg+pviuRW7PA6pQ/9cf3KO9XvXY0WIZzKTY2V4vwSJNo0iKAggEB4CYR8AnDNnjs8/Ztrs+dprr+20oG6viUO+973vdXofNkQgUAI5pql7QkykNTZefWNzoKrtVD0R5nN/+YxhMrRvgjy5NNeji8LB8lorQ/A1J+XISSP7+fx/1qkDdHIjDQLqFyUdmDiC5CCdVGMzBBBAAAEEEEAAgXATyC2sDPqYf2rQbH7T/nlJrtSYrsfu5fqTR4RVNl393ZNjWv2lxEW7nybTCCAQBgKOCMdrAM/1cpm55jvzPnToUHnsscdk3rx5rt15RyBoAjrOhbaymzYsTUb1TzLN3yODdmzXgU4elWElB0mN9/xDXG2eXD728Xb5nzc3yu7iKtfmtrxrUpAt+RWiT00pCCCAAAIIIIAAAgg4TWBPcbUUVdaH5LTf3XBQNppeNe5l9ugMmZETHsNc6W+eYabF3+ShqQT/3G8S0wiEkUDYtwD8+OOPW7g02Dd37lyrpdLTTz8tOTk5Leu8J7TFX1xcnAwaNEiysrK8VzOPQNAF9DOpTeH1VVbdIHllNVJq3oNVRg9Ilt+Z5CAL3t8iu8yXF/ey2bTO+/mr6+XM8QPk29OzJMmmMTr0ercVVMqYAUm2tjh0vzamEUAAAQQQQAABBBDwVyDf9J7ZX1rjbzXd2n/foWr556o9HvtmJMXI1bOyPZaFaiY9Mcbq7qtDEFEQQCB8BcI+AHjqqae2qnf88cfLhAkTWl3HQgTCXSA1IVr0VV3fKHmlteZJYp1p5Wr/WfdLipW7v3mMPGG6A3+6o8TjgHp8TRCyckex1W14zthM0S7EgS4lVfVWEHC0aQ2pQVEKAggggAACCCCAAALhLFBaXS87i+ztLdPW9Tc2N8vjiz2H8tFtv3/qSDPMUGh/zsdGR0hOv0TRcc8pCCAQ/gKh/RejGz47d+609hoyZEg39mYXBMJLQP9oa7fgrPR4yS+rk/yKWmlssjcSqE/mbp47WmaNPCR/W7lbCk3w0b3oeH1PLdshH23Ol/mzcqzzc18fiOli03Uiok+lyQpGEDAQntSBAAIIIIAAAgggYI9AVV2jbM2vDMrD+tau4NWv9vsEH8+ZOFCOGZza2uZBW5Zsko/o+N5RJPkImjkHQsBfAUeMAeh+kTt27JDhw4dLVFTXY5c/+MEP3KtiGoGwEYiNirTGzJg2rK/VfD4myt7/NbXl3YzsdFnw7SlyyXFDTXYu35Z4uYVVVpKQJ8xgwzp+X6BLYUW96DEoCCCAAAIIIIAAAgiEo0BdY5PoUDlNIRrDevPBcnnNBADdyxAztrgm+Qtl0eDf+EEpBP9CeRM4NgLdELA3ytCNE+poF03k8cUXX3S0mc/6G2+8UZ544gmf5SxAIJwEXAlDjs1KM5l7400rOXvPTgONF08bKr83gcAZ2X1bPdjirYXy0xfXyLsbDgT8y09hRZ3sMJnUKAgggAACCCCAAAIIhJNAY1OzbD5QIfWNzSE5ra/zyuT+dzeb7L9HDx9pHuL/YM5IsbuxwNEj+k5pUkEN/unvFgoCCDhLwHEBwIqKCjn33HNly5YtnZa+/vrrZeHChZ3eng0RCLVAhPmDmpWuWbTSJCW+661du3r+mclx8tMzx8rPzxkng1PjfHbXbMHPme7CP//XOtlovowEsuSX18muEI2pEsjroC4EEEAAAQQQQACBniGgySc1cZ1+Bw5F+WL3ISv4V9vgGXy8aNoQGWGG0AlV6ZsYbXX7JfgXqjvAcRHwT8BxAcBRo0ZJYWGhnHnmmbJ3794Or37+/PnyzDPPWNtdfvnlHW7PBgiEk0B8TKQ1voeOExgTZf9TNg043n/xZLnihGESZwb19S57D9XIb9/aZI0RqAlMAlUOlNXKHq/MxIGqm3oQQAABBBBAAAEEEOiKwA7zcLq0OvBD4HTmHD7ZXiQPfbBVGrzGBZ9qeghdMDV04+D3M1mHxw5IFm2oQEEAAWcK+P7CD/Pr+OCDD0QTgOzbt88KAhYUFLR6xvrU5rvf/a789a9/NQO2HpYrr7xS/va3v7W6LQsRCHeBzORYmWKCcwNSYm0/VR3I9/zJg0234Kly8qiMVo/30eYCuePldbJm76FW13dn4f7SGtlbUt2dXdkHAQQQQAABBBBAAIGACOw7VC0FpodKKMqHm/LlsY+3S5P5/epeTshJl9vOHBOybreZyTEy2jRI0HHEKQgg4FwBxwUANQHI+++/L/369ZNt27bJ2WefLeXl5R53oNmkSr/iiivk+eeft5ZfffXV8txzz5mnFY67XI/rYqZ3C2hgTpv8TxySIomxkbZjpCfGyA9PGyV3f3OCDDfdkb1LSVW96ZqwRR5fvF0qTebgQJR9poWhBgIpCCCAAAIIIIAAAggEW0DHp95bEprvom+szZOnl+8Uz9CfyKljMuXHc0eHLOFGf9MAYaT5DULwL9ifRo6HQOAFHBkRGz9+vLz99tuSmJgoa9eulfPPP19qa2stnaamJvnOd74j//znP635a6+9Vv7yl7/wD1bgPzvUGCKB5LhomTQkVbIzEoLyFHDcwBS598JJMn9WtsS2kp142bYi0xpwrazaVRIQEe0KvC2/QjTrGgUBBBBAAAEEEEAAAbsFmk2mDe2JkhuC5HTaW+2FVXvkH5/v8bnMcyYOlBtnjwjKd36fg5sFg8zY4AT/WpNhGQLOFHBkAFCpZ8yYIa+99prExMTIJ598IhdffLHU1NTIpZdeKi+99JJ1N2644QYr+QdPK5z54eSs2xbQz/Sg1HiZkpUqOh6H3UXH+vjGMQPlgUsmW8FH7+OV1jTIg2askkcXbZNyM+1vKaqsl7V7y6zWgPqFjIIAAggggAACCCCAgB0ClXWNsn5/mWhPFK+et3YczqPOZnPAZ1fsktfW5Hks15mLTcKP784cLhEh6nY7JC3eNDhI9DkvFiCAgHMF+pgnDo7+da1BwG9/+9ui3X4zMzOtBCF6Sd///vfl8ccfd+6d6eFnrmM4ZmVlWVepyVyGDh3aw6/Y3ssrra4XHay4zitTmB1H1f+/Fm8plL99ultqGnxb6SXHRck1prXgzBH9AtLyVhOhZPdLkLQE+wOddnhRJwIIIIAAAggggED4CehDZg365ZUFP/CnGk3m+E8szRXtTeNdNPB37qRB3ouDNp+VHi9D+/oOARS0E+BAARfg93fASR1ZoWNbALq0582bJ0899ZQ1qwlBNDjxwx/+kOCfC4j3XiGgwbHJpluwjttnd9HWh6eN62+1BtRsZN6lwowH+OhH2+WhD7ea7Gn13qu7PF9T3ySbDlTIloMVUttKwLHLFbIDAggggAACCCCAQK8WcLX607Gnzc/HoJeGpmZ5ZNFWn+CfNva78ZQRIQ3+DTcP3gn+Bf0jwQERCIpA2LYA3LPHdwyE9kQeeugheeSRR+SSSy6RBQsWtLnpsGHD2lzHiuAJ8ATCPus880VmjxnDJBhfZjTgvnx7kTy3cpdU1fm2BtRkJVfNzJZTRmcEpDWg6Yksg013BO2SoN2SKQgggAACCCCAAAIIdFYg1K3+9Dz1gbYOnaPdjt1LpPlu+yOTgE970YSq5JguvwPNuH+UnifA7++ed0+7c0VhGwCMjAx8llNtudTYGJhspd3BZp+jAvwDdNTCjqmK2gbZVlAZlC7Bev7a0u+ZT3bJ520kAjk+O93KKBzTShKR7lx/bHSE6RacGJQWj905P/ZBAAEEEEAAAQQQCC8B/X6cW1gl2rskVKXKjDd4/7ubre/p7ucQHdlHfnrmGJma1dd9cdCmteXhiMxE6Z9M8C9o6EE+EL+/gwwepocL2y7A2rLIjleY3gdOC4GACmimYO0S3DcxOqD1tlWZdkH+yRmj5ZbTR0uKGQPQu2hgULs5NJruDoEoOtahdgnefLCcbsGBAKUOBBBAAAEEEECghwpoq7/dxVXydV55SIN/+eW18j9vbvQJ/sVHR8rPzxkfsuCftjwcNzCZ4F8P/fxzWQi4C/j+UndfG8LpZ555JoRH59AIOF8gKjLC/DFPkWB1CdYWttplYcKgFKtL8IrcYg/EL/eUyh/M2IA/Pn2UREUE5tnDoaoGKasutboFZ6UzULEHODMIIIAAAggggEAvFwiHVn96Cz7dUSxPLt3hk0AvKTbKBP/GmdZ3SSG5U9rycKwJ/mnjAQoCCPR8gbDtAtzz6Xv3FdIEObj3X7/8bM2vlPrGwLTA68zZrzat/h5fnOvzRefEkf3kR3NGBXwMv+yMBBmUGt+ZU2MbBBBAAAEEEEAAgR4usNeMiR2qJB8uWv3u/ffPdssHG/Ndi1re+yZEyy/OHR+yhBs6pM5401ggPibwQ2+1XCQTYSPA7++wuRUhPZHANMMJ6SVwcAQQ6EjA6hI8NHhdgvV8pptx/35mnmjGeo37t9K0DPzz0lxpDnCWkt3F1QHJOtyRJesRQAABBBBAAAEEwltgR2Gl7DsUmgy/LpmDZbVy9xsbWg3+DU6Lk7u/eUzIgn+aqG/i4FSCf66bxTsCvUSAAGAvudFcJgLR/+kSPLxfgsnIGxyPMQOS5c6zx0mMObZ7WbatSBYu2xnQIKDGEzXxiWZWoyCAAAIIIIAAAgj0PgEdQ367+T6YX14X0otfmVskv3h1vewyD6i9yymjMuR38ybJgJTQJNxIiY+yhuwJVHI+7+tjHgEEwlfA81d5+J4nZ4YAAgESGJwWLxMGp0iw/uiPN2MC3v6NsaJjjLiXj7cUyLMrdlnJftyX+zPd2HTYJAapCFiyEX/OhX0RQAABBBBAAAEEgiegwT99GFxYEbrgn3b5fXr5TnnUjHtd4/VQWh+If2/2CLlpzkiJM4k/QlH6JcVY3X51rHAKAgj0PoGwTQLS0a1obGyUt956S5YtWyY7duyQiooKaWpqv+WPJilYtGhRR1WzHoEeL5CiWYJNl+D9pmuEZiQzydFsLZNMRuKfnjlGfv++yQTsdjAdDyXaZB67cuZw0yrRM0DY3ROqqW+yvvxpNrNA1dndc2E/BBBAAAEEEEAAAfsFNNPv1oIK0QRxoSoHymrkkUXbTMZh31Z/Q8wD+FtOHy2hTFo3MDVOsq2eQIH5zh0qZ46LAALdF3BkAHDJkiUyf/582bNnT8uV6xOftooGAXQ9wYC2hFjeGwW0S3B2RqIMMmOQ5JXWSoHNgcCpWX2tLz4Pf7hNmtz+f317w0HRp5CXz8gK2P+jpdUNsscM/Dy8X2JvvLVcMwIIIIAAAggg0GsEmkzwb/PBcimvaQzZNa8wXX6fWrbDDEXjm3Bv9ugMueaknJC1+lOUrPT4kI03GLKbwoERQMBHwHEBwDVr1sjZZ58t9fX1VlAvLi5ORo8eLWlpaSarKE2Zfe4wCxDoQCA2KlJyNBBongpqpjTtNuEWn+tg766t1sQgP5o7ynSL2OZxjDfW5lldhC85LqtrFbaztQY1NatZ/+TQjK/SzqmxCgEEEEAAAQQQQCAAAo1NzdbwLxW1oQn+aZffv67cJYs2F/hcjXb5vfbkbDl1TH+fdcFaoB1sRmQm8n04WOAcB4EwF3BcAPDXv/611NXVSWxsrDz44INyzTXXiAYBKQgg4J+AjkUyMjNJtIuCZk0rqrQnEDhzRD/RJ7WPfbxd3NvtvvLlfqsl4LypQ/y7ELe9dxZWSby5Ls2CTEEAAQQQQAABBBDoOQINJvi36UC5VNW1PwyUXVesXX61Z4v2OvEu4dDlN9IMszO6f5L0TYzxPj3mEUCglwo4LgC4fPlyq5vgXXfdJTfddFMvvW1cNgL2CWggcJT5sjC0rwYCq00gUFvbBvZ4J5nsZ/ql7YmlOzwqfmHVXjMmYIScN3mQx/Luzuhwg1vzK2SiGYNQWzpSEEAAAQQQQAABBJwvoC3vNPhXbcZ+DkXZWVQl9769SSrrfFsenjomU+bPyg5pl98ok3xPx8PmIXgoPh0cE4HwFXBcALC2ttbS1G7AFAQQsE/gSCAw2bQIbGoJBAbyaHPG9rcSgmimNPfy9892m5aAfeQbxwx0X9zt6fpGMyj0wUo5xmQ+jjBPQikIIIAAAggggAACzhWoNdl1NfjX2nh7wbiqbebh8n3vbvYJPsZGmS6/Zqy/2SYAGMoSbb5Hjx+UIomxjvupH0o2jo1ArxBw3KB52dnZ1o1paAhdhqde8cngIhH4j4COozd6QLJMyUqV9AB3IThj/AC5+sRsH+tnV+ySV77cJzquSyCKPp3NLawMRFXUgQACCCCAAAIIIBAiAQ3+fZ0XuuCfBh7vfWeTT/BPe878bt6kkAf/YqL6yATz0JvgX4g+oBwWgTAXcFwAcN68eRbp0qVLw5yW00OgZwkkxETJWNOVQF/65SJQ5eyJA+WKE4b5VPfyF/vkZ/9aLxvzynzWdWeBdmXWLs0UBBBAAAEEEEAAAecJVNc3muBfmWj331CU9fvL5L53Nvu0PJxgWtv99oKJMsQEAUNZYkwLxGMGp4p+Z6cggAACrQk4LgB4yy23yKBBg2TBggWya9eu1q6JZQggYKOAtgKcMjRNMpNjA3aU8ycPlkun+2YA1qzEv31rkzxuEoaU1fjf6ndvSY2UVNUH7LypCAEEEEAAAQQQQMB+gSrTm2OjafmnQ7uEony555A88N5mqffqnTJlaKrcefa4kI73px6x0Rr8Swn5eYTi3nBMBBDovIDjAoCZmZny9ttvS3x8vJxwwgny1FNPSVlZYFoIdZ6NLRHo3QJRkRFWopDxg7Q1YGD+Gbnw2CFymQkCtta2cNn2IrntxTXywcZ8adbMHn6U7QWVptuG74DNflTJrggggAACCCCAAAI2CWiLvy1m3L2GJv++A3b39D7fWSIPfrDV5/jTh/eV284aG7Dvwt09Px2uh+Bfd/XYD4HeJdDnsClOvGRt/acBwKKiIisrcEZGhiQkJLR7KX369JHc3Nx2t2FlcAT27dsnWVlHWnzt3btXhg4dGpwDc5SACzSZgNzu4irJL68LSN06Vp8mBtHsaq2VkZmJct3JIyQnI7G11Z1apk9JJ5nMwNEmkElBAAEEEEAAAQQQCE8BffC70Yy7V1Ebmoe3n5iH0I8v3i7ez59PHNFPfnDaSImKCO13yQQT/NOEH4F6IB+enwLOKhAC/P4OhKLz63BkAPCVV16R6667TioqKqQr8UsNADY1hSZVvPM/KoG9Av4BCqxnONSmXXR3mOBdIDKy6Ze9Dzblywur9kqNGezZu5j/leWsCQNNt+Gh3R7nJM4EAYf3Swx4YhPvc2UeAQQQQAABBBBAoHsC2nOjsCIwD5m7egYfbymQp5buEO/WMrNHZ8j3Zo+UiIjW+q109Sjd3z4x9kjwjwfa3TfsTXvy+7s33e22r9VxI4SuXLlSLr/88pZA3vDhw2Xy5MmSlpZm/hEO7ROYtplZg0DPF0iNj5bJZmzAvSXVcrC81gTnu3/N+oXqG8cMlBNy0uXvn+6WT3KLPSrTut/7+qB8trNYvjtzuOhTWA3wd6VooHLLwQrRhYwiQgAAQABJREFU8x7eL4FsaV3BY1sEEEAAAQQQQMBmgTwzFnSogn/vm++Zz6zY5XOFZ4zvL9eclCMRXfze6VORnwuS46JknEnMp8PyUBBAAIHOCjguAHjPPfdYwb/U1FR5/vnn5dxzz+3stbIdAgjYLBBpAnfZpmtuv6QYyS2skpp639Z7XTmFtIQY+dHc0TJnbH/5yyc75UBZrcfupdUN8oePtsvHWwrl2lnZMiit69nXtOWiZnXrb5KaZKUn0C3YQ5gZBBBAAAEEEEAg+AKl1fWyxzxUDkV5c12ePP/ZHp9DnzNxoPXguasPnX0q8nMBwT8/AdkdgV4s4LhHBqtXr7Za+vzmN78h+NeLP7hcengLJMeZ1oBmjL0hJiAXiAekE01d9188Wb593FAToPNt6bfBBPDu/Nc6+XL3oW7BaItCHcNwzd5S0afNXRlaoFsHZCcEEEAAAQQQQACBVgX0AfI20/XXn94krVbciYX/+nJfq8G/C6YODovgn/Zc0TH/aPnXiZvJJggg4CPguABgdfWRJ0Enn3yyz8WwAAEEwkdAu/EOM11rtXtCIIZI0fFNLpo2VB64ZIpMzUrzuVDNDPfoR9ushCQ+Kzu5oNHUsbu42goEllTVd3IvNkMAAQQQQAABBBAIhEBjU7NsPlgu+p0smEUf/r6wao+89MU+n8PqA+jLZwzr8nAzPhX5uSAtIdr6Xq09bigIIIBAdwQcFwDMycmxrtMVCOzORbMPAggET0C78Y4ekByQloB61gNS4uS/vzFWbj1jjE8Cj7rGZlnw/hbRbiP+FNf4gBvzyqW6PjRZ5/w5f/ZFAAEEEEAAAQScJqBBuK35gUko15Vr1+FgHlm0TV5bk+ez2xUnDLMeQPusCPKC9MQYGWu+T4c68UiQL5vDIYBAgAUcFwC86KKLrO557733XoApqA4BBOwS0C8to/snBSwIqGOvHG8ShPz+21OsBCDu511UWS8PfrBV6k0w0N+iXwjX7Suzshs3mCfSFAQQQAABBBBAAAF7BHaZXhj63StYRQOOK3KL5PaX1prEciU+h73GjC99/uTBPsuDvWBASqyMGZBE8C/Y8BwPgR4o4LgA4G233SajR4+Whx9+WHQ8QAoCCDhDoF9SrIwKYBBQrzouOlJumjPSeiLqrqDjxixctiMgY/np+DOu8QEPlDE+oLsz0wgggAACCCCAQCAECspr5aBXsrdA1NtWHdpbRB8YazK5yjrP3h7awfbGU0bIWccMbGv3oC3X4XRGZOpDdLr9Bg2dAyHQgwUcFwBMTk6WRYsWycSJE2X27Nly1113ybp166S21jM7aA++Z1waAo4VyDBBwBGZiQFrCagQOjbgrWeOkQyTedi9LNteJP9e69uVw32brkzrWDS7iqqtFoHBfDrdlXNkWwQQQAABBBBAwGkC+r1qR1FVUE5bW/0tN98Rb395raxuJXlcUmyUNczMaeP6B+V82jqIDvM32rT604R6FAQQQCBQAn3MP4LBHWHVzzOPjIxsqUFPvStPQ3TbxkbPJzwtlTERVIF9+/ZJVlaWdcy9e/fK0KFDg3p8DhZaAX3Km1sY2C96u4ur5O43vhYdB9BV9FnpT01wcHp2umtRwN414KhPZWOjjv6bFLDKqQgBBBBAAAEEEOgFArUNTbJhf5loMje7iyZ4e3r5Tvlyz6FWD3WCGV5mvun2q+NXh7JERfb5/9k7D/A4ymvvH0urttKqWL25qFmW5UKxMTHGNgbTTU0ggYSEAElIKDdfEkICwUAgkH5DbgK5JCEhBLihBYwdsI0xzYAprrKqi3rvXSvxnTNGYmd3ZW2ZmS36n+eRtTM785bfyLsz5z3nfzjl10JS8RcGAloRwPO3ViQDu52AiwAUp9/4j6Aff+3q78C+XBg9CAQHgRQu5CGRgFra7MRo+s4ZeWSbICG3kr/fXulVZeDJxihag3tquqi2o5/GxvS/aZ1sHNgPAiAAAiAAAiAAAoFIYJTvn8oae3R3/slz4o7yFvoBR/05c/7FRprolrX5dCsXmPO18y8iLISKM+Lg/AvEP2iMGQQCgIApAMaoGuJdd92l2sYGCIBAYBKQar5jfEMmabVa2cmzZ9KVy2bRk+9XTzQ5Xhn43ouKNb+pkxvXmvYBaukZojnsgEzgYicwEAABEAABEAABEACBqQlUsmZz//Do1Ad6cURb7xA9ylF/u2s6nbZyak6iEvUX6wfRdpJ+PC/NQuGmgIvRccoWO0EABPyPAByA/ndNMCIQmDYE0uOiOIqXOEJPOyfghYvSlai8NytaJzhKtN5vtpbTHecXKZqBE29o9GJwZIxKeQU7ITpMcQRKcRIYCIAACIAACIAACICAcwI17f0kKbl6mUT9vV7WQo+/e5QGOM3Y3sTh9/UVc2kZp/36g8k9ZH6KhUJF/A8GAiAAAjoRCDgHoE4c0CwIgICPCGSwuLFEAkoknRYmWp/Xc+W2JtYZLG/qnWhSXv8vVwb+1qpct7RDJxpw4UVH3wh19XeSzEl+cBPnAjQcAgIgAAIgAAIgMG0IiGzKEdZtbuoe0m3OvYNWloCpoD21XU77WJGXRNecOpsskf6hsZcWF8kLyGbd7k+dQsBOEACBaUkA8cXT8rJj0iDgXwSyEsyUlaBdlTOpDPzds+Y5VgbmqMCX9jboOnm+r+UIxAEl1aSV005gIAACIAACIAACIAACxOm+VtrHBT/0dP5Jobm7Xtzv1PkXz1F//29dAX1nTZ7fOP9ms+NvblI0nH/4DwICIGAIATgADcGMTkAABKYikD3TTJkcNaeVSeW0762bx1V61R9zT7E+4AdH27XqZtJ2hrkacQVHHUplu+7BkUmPwxsgAAIgAAIgAAIgEOwEJDNjH0fk6an5V9XSS3e+eIDquwYdcK7MT6JfXL6YRC/aH0wyffNTY5SMEX8YD8YAAiAwPQion4ynx5wxSxAAAT8lMItXQdM5DUIrm7Qy8Gv6VAZ2Nu4eTkM5UNetVLkb0Fno2ln/2AcCIAACIAACIAACviJgHZUF0R461NLHki/6jUIWd+/dWELdA+pFV4n6+/7Z8+jG1XkUw9V+/cHCQmfQ/IxYzlSJ8IfhYAwgAALTiAAcgNPoYmOqIBAIBOZwGkRucjQX69BGBFmpDLw0WzX18crAXXY3iaqDNN4Qoes9tZ18A9xLEh0IAwEQAAEQAAEQAIFgJtDDGRB7ORNCirHpaa8eaKRfbyknub+ztWyWl/npxcV04qwE290+fS2Vfosz4yjWT/QHfQoDnYMACBhOAA5Aw5GjQxAAgakIpMRG0qKseEq2hE91qEvvX7g4gyT1w9bkZvQXr5RSQ6c2xUds257stVQ8Ft2b3TWdXPSkn0b1XAqfbBDYDwIgAAIgAAIgAAI6E6jj+6sD9d00NKJ2ymnZrRSRe+K9o/TXd46Q3GPZmjjZNqxfQIl+FGUnWS4LOPIvMizUdqh4DQIgAAKGEYAD0DDU6AgEQMAdAuGs3ZeXYqH56RaKCPPuo0oqA193Wg7lp8SohlDF6Sjff2Yv/W3nEZKKcUaZOP6OFQrpUKoVf2J/12rUQNAPCIAACIAACIAACGhIQLIcDjZ0U3Vbv4NTTsNulGyK322roI1Oirudzou+t3HarzncP1J+TZzVMi/NQpLlEiLifzAQAAEQ8BEB756qfTRodAsCIDB9CMSbw2kxRwNKgRD243ls4lD87lkFDpWBR9n59p/9jXTr/31Mm/Y1kGjVGGXD1k8UTZw9LIotKcIwEAABEAABEAABEAhUAl39I1zlt5M6+beeJqnF9286SO8ddizqdtmJWfTNVblkCvWPx1wL6w4u5GjEmdHaZLXoyRVtgwAIBD8B//hkDH7OmCEIgIAXBEJ5tVQKhMgNlGineGriTPz+2YWUYA5zaKJvaJQef/eoEhG460g7r1rb5ZI4nKHdDikOUtbYo1QM7h0yLhJRuxmgJRAAARAAARAAgelKQO6ZJOKvhCP/ZHFTT5Nqwndxpd8yLixia6G8SvzNVTl0+UlZvGDsxYqxbaNevkbKr5cAcToIgIDmBOAA1BwpGgQBENCLQLQinBzLKRRmEqegJzZrppl++fnFdOGidDI5aaORbyxFSPrel0vocGufJ114fI5UDC5hvZw+OAE9ZogTQQAEQAAEQAAEjCMwODKqaP2J5p/eVtncQz/5935q6BpUdRXFmnq3nVtIqwpSVPt9tWGb8usvzkhfsUC/IAAC/kVgBq/Y6LtM41/zxWj8hEBtbS1lZx+rzFpTU0NZWVl+MjIMI1AIDFlH6Uhrv1eps83s7HtyVzW9e8gxhUQ4iItRiodcsXSWoakbkq5cnBlLESaIRAfK3yPGCQIgAAIgAALTjYBE4x3lyD8jipp9wNkZD71WScN2Ui2SWvsD1vubnRjtF/gl5TePNadR6MMvLgcGYUMAz982MKbxSzgAp/HF9+XU8QHkS/rB1bdo50mknohOe2rlnEYi6b+Vzb1Om4hgh9wFHDF4waIMw27ozOGhSqU4f9GwcQoGO0EABEAABEAABKYdAVmEPcSF1PTW+hsH+2pJIz32Nlf6Hd/x6W/J6hDnn79U+pWU39ksWYOoP7sLhU2/IIDnb7+4DD4fBFKAfX4JMAAQAAFvCMjK76KsOHbMef5xVpBqoXvWL6CbzshzKBIiYxti5+KzH9XRd/9vN+0ob6YxAwKn+1kXsLyp11AtQm+uA84FARAAARAAARAIfgKtvUO0j4uXGeX8e7uylf7qxPm3iHWh77qwyC+cf0j5Df6/e8wQBIKFgOdPzMFCwI/msXr1amXFSFaNXP15/fXX3Z7Bhg0bdG3f7QHhBBDwkkAYV3orTIvlim+e6QJK9/J/7nO5SfSrzy+hK5dmk+jJ2FsHV7V7eMchuuOF/YrQtf37Wm93DYxQFa+ww0AABEAABEAABEDAlwSsnHpbwRkTFbw4OTJqH4unz8ikQNrDO6ocGl9dkEzfP2cemcM9Lwzn0KiHO1Dl10NwOA0EQMAnBOAA9Al2bToNCQmh/Px8bRpDKyAQ4ASiOGW2IMXCjjzvJiL6exctyaRff2ExrS1McdqepBzfu7GEi4WUUaOdELV3vTue3dIzRDXt/Y5vYA8IgAAIgAAIgAAIGECgs3+Y9nDUX2vvsAG9HetC9AV/xfdZ1jG1s/GyEzPphtNzuJCb7x9jU2MjqCg91jB5GMPgoyMQAIGgJeD7ZZOgRev+xP76179SX9/xo31KSkroiiuuUBpfu3YtZWZmut+RzRn79u2z2XJ8OXfuXMed2AMCfkogzhxGc5OiFV0ab4cYbw6n61bm0LoFafQP1gfcV9fl0OSuIx30UXUnncPHXHJCJkmVYj2stmOAC4KEUEpspB7No00QAAEQAAEQAAEQcCAgxT2qeRFS78VO+457h6z081dKqWfQqnprXVEqXXZilpK1oXrD4I0QXmyemxxNKRbclxmMHt2BAAh4SUCfp1UvBzVdT3fF2fb4449P4PnKV74y8drTF8XFxZ6eivNAwC8JpLKTbID18xo0iswTgenbzy3kle9OdgRWU13ngGrecnP88r4G1gZsoc+flEVr56dSqNwZamyHOOpQohPFMQkDARAAARAAARAAAT0J9AwekyGReyojzTo2Rr/dWk71nYOqbpdkx9NXTp3jc+efaE6LdrRei76qSWMDBEAABDQm4PvYaY0nFMzNjfEX4hNPPKFMMSYmhi699NJgni7mBgIeE5AKbAnRYR6fb3+i6AMuyU6gBy9bRF9bMYdinET6yWr1X985Qrc9u5c+ru7QvHiH1B2RoiB93A8MBEAABEAABEAABPQg8AnfcIj0yIH6bmVBVY8+JmtT+v7LW4eVvm2PyebFWCnUpscCq20/U72We8uFXHwEzr+pSOF9EAABfyUAB6C/Xhkn49q2bRvV1dUp71x++eVkNpudHIVdIAAC4rDLZz1AM+sCamly47muKI1+e8USumBRutMbUYkQ/PkrZfTA5lLNtfsk2rCUBbGHrMauxmvJEG2BAAiAAAiAAAj4J4GxT+8zRHpEFh6Ntpf2NtD2shZVt3FRYfSDs31b8EP0pbNnRn1acA6Pz6oLhA0QAIGAIoBPsAC6XH//+98nRqtF+u9EY3gBAkFIQJx189IsnDarfTqurPxedcpsrhi8mJbNmemU3l7WDLztub306JuHqJvTaLSyYesYlTb0kFTjg4EACIAACIAACICAFgTE+VfGVX47+7W7Z3FnXO8dbqMn369WnRIeGkLfZ+dfUkyEar+RG2GhM9jxZ6GsBAReGMkdfYEACOhDAA5Afbhq3mpvby89//zzSruzZ8+m1atXa9LHunXrKCUlhcLDw5Xf0u4DDzxAHR0dmrSPRkDAlwQiw7gyMOu06CDJp0xL9Ab/66wC+skFRUrxEfu5yur5ttJm+u7Tu+mVA40kEXxaWD/r8Ug6sNysw0AABEAABEAABEDAGwJyP1He7DvnX2VzL/3P9kqHKXx7TR7lJsc47Ddqh0i+FHPKL/SXjSKOfkAABPQmgCIgehPWqP1nn312okLw1VdfrZkA7pYtWyZG2NLSQjt27FB+HnzwQXrsscfooosumnjfnRe1tbXHPbyhoeG47+NNENCKgCUyjHJTYqiCHWZ62fz0WPrpxcX0ZkUrPb2rmjrsVs/72GH3GOsDvsbOwK9+bg7J8d5a18AIHWrtpTxOdYaBAAiAAAiAAAiAgCcERHevgh1wHX2+ifxr6RmiX75aRiOj6kXNLy6bRcvmOs+y8GSe7p6TGhtBcxKjKUSvVWR3B4TjQQAEQEADAnAAagDRiCa0Tv9duHAhXXzxxbRs2TLKyMigkZERKisrU4qMvPrqq9TZ2UmXXXYZvfTSS3Tuuee6PcXs7Gy3z8EJIKAXAUkdkSp2ommjl4WwQMyqgmQ6hW9WN+6tp5f2NNCwXZpuNYtq37OxhD6Xm6ikEM+M9q6ib0vPMEWY+lmXBmkpel1XtAsCIAACIAACwUpg3PnX3jfskyn2D1vpF6+Ukixq2tqaeSl0IWst+8LE3zc3OZpSLJG+6B59ggAIgICuBGbwB796uUXX7tC4JwQkmk7SfqUK8PLly2nnzp2eNDNxjjj34uPjJ7btXzzyyCP0zW9+U9ktzsGqqiqKjHTvS1CKMLhqNTU1lJWV5erhOA4EPCZQyekt4jQzwtp6h+ifrGXzTlWb0+4iTCF0yQmZdN7CdApjjRtvLCshCk5AbwDiXBAAARAAARCYZgTGnX9tvcbcF9njFVkUcf7tqe1SvVWcEUu3nVtIphDv7o1Ujbq4IcXj8jhrBFV+XQSGwwKKgPgUxoN08PwdUJdO08Ea/8mq6fCnR2P/+Mc/FOefzPaaa67xetLHc/5J49/4xjfo61//utJPfX09SfqxuyYfKsf7ef/9991tEseDgNcEcpJiyBJpTOBzIkcd3nRGPt3J+oDOIvSGuJjHU7tq6AfP7KWPq73T3JTIxtLGbk6fQWEQr/9I0AAIgAAIgAAIBDkBcf6J7p6vnH/S/992HnFw/mXER9KtZxYY7vyTuAVZTF3Ien9w/gX5Hz+mBwLTnAAiAAPgD2DBggVUUlJCERERJNp5CQkJuo96165dSnqwdHT99dfTn/70J037xAqEpjjRmBsEpIru/vouGhoxzlkmq9xbDzbRvz6oIdEDdGYnzoqnr5w6h6SwiKcWERaiFD0R0WoYCIAACIAACIAACNgTEOdbVUuvYRkR9v3L9ub9DfT3nUdVb8kC7b0XFXt1H6Rq0MWN6IhQpdAIHH8uAsNhAUsAz98Be+k0HTgiADXFqX1jH3zwgeL8k5YvuOACQ5x/0ldRUZH8Uqyurm78JX6DQMATCOfU28I0C4UaKOosfZ29II1+/YUldEZhCjlLkP+oupO+9689ShGRwRHnTsKp4ItT80BdFzV2DU51KN4HARAAARAAARCYZgSOOf/6fOr8e6uylR63c/6Fhc6g762bZ6jzD1F/0+yPH9MFARBQCMAB6Od/CLbFP7RI/3V1uu5o+LnaJo4DAX8hYA43UXFmLMWbwwwdUmxUGF2/Mofu5YrBojFjb1aOFHxhdz3d9uxeaur2zInHTdDh1j5O7ekhiTyEgQAIgAAIgAAIgIAQOMT3B1J11xcmBT/+8Hol/c/2SrK/O/nG6blKBoNR45JMiUVZcYpEC555jKKOfkAABPyBAByA/nAVJhmDVOZ96qmnlHeTk5M9qsY7SdNT7paU43GTQiAwEAg2AuIEnJ8eyz8W1nsJNXR6uckxdPf6BfTNVTkkTkF7a+ab8/s3HaSOfs+FuaXYyT6OBpTqxzAQAAEQAAEQAIHpTeAQp/02d/vG+Sc6xT98dh+9WdHqcBE+f1IWrchLctivxw5J/piVaFYWgeU+EAYCIAAC040AHIB+fMU3b95MLS0tygi/9KUvkclk3BeVVAIet1WrVo2/xG8QCDoC8eZwRfQ5NyWaJD3YKAvh3JNVBSn0my8spvOK08g+I1mcgD/bXEq9Q1aPhyTOP3ECtnJFYhgIgAAIgAAIgMD0JCCZAU0+cP5Zx8ZY2qSG7tlYQi1O7kXOnJ9Cl5yQachFEY3BhRz1lxkfRYj6MwQ5OgEBEPBDAsY97frh5P19SLbpv1/5yldcGu5jjz2mfKnJF9uGDRscztm3bx9VVlY67LfdIQU/Hn30UWVXWloaXXLJJbZv4zUIBB0B+f+SYomkJdnxnA4SZag+oKxAf5mLfzx42SKaNdOsYlvT3k+/eKWUhqyeR/FJGnBFU6+SFjyGlGAVX2yAAAiAAAiAQLATOMLOP19oAzd0DtBd/z7A0iZ1xHVHVBYVFko3rs6la1fM1d0ZJwussznqb0FGLCHqT3UZsAECIDANCRgXUjYN4Xoz5Y6ODtq4caPSRHFxMZ144oneNDdx7ocffkjXXXcdrVmzRkkpXrhwISUmJpLVaqXS0lJ64okn6NVXX1WODw0NVar/RkdHT5yPFyAQzASkWEdWgllxBtZ29JNE4dnftOo1f+n39nML6e6XSqjRRv+vnJ13v91aQf9vXQGZQjxfs5Gb/z6OJhTtwUi+8YaBAAiAAAiAAAgENwG5l2kwuDCYFBrZVtpM/3j3KC9gjjkAlkJs4vxL5oVXvU2i/kR2JSoc9z16s0b7IAACgUEADkA/vU5PP/00DQ0dS9tzNfrP1amMjo7S1q1blZ/JzhGn4J///Ge68MILJzsE+0EgaAlIKnAO3zCmx0VRNUfhtfd5rsXnDiRJR/7ReYV014sHWP9vZOLU3TWd9PDrVXTjmjxOFXZWQ3ji0OO+6Bm00n5OCRYnoPQFAwEQAAEQAAEQCE4CIv9R0z5g6OS6BkboT28coo+qOxz6DeX7l8+fnEUXLsqgEHvdE4ejvd+RHhepRP4h3dd7lmgBBEAgeAjAAein1/Lxxx9XRiZReFdddZVmozzvvPMUx97OnTvp448/pqamJmpra+Mop09o5syZtHjxYjrnnHPoq1/9KsXGxmrWLxoCgUAkICvG83ilWm5oq9v6vdLjc3X+siJ++7nz6e6NBzhi77PU37er2igmMoyuOXW2V+kyI6Of0MGGHkUEW3RwYCAAAiAAAiAAAsFFoGdwhKqaew2dlDj9HmHnXzffM9lbBjvjvs2LmLK4qreJb1H6SbZE6N0V2gcBEACBgCMwgx0/dqoMATcHDDgACdTW1lJ2drYy8pqaGsrKygrAWWDI043AsdX0fhoccUxp0ZpFeVOPUgnYPn1GquVdeqI2/1/k5jgnKdqQlXit+aA9EAABEAABEAABRwKDI6N0oL6Lhq3GPOKJTvE/3q2mrQebHAfDe84qSqWrTplFESb903AjwkJoXqqFoiMQ4+L0YmDntCaA5+9pffknJo9PxwkUeAECIAACxyeQFBNBidHhSiW9us5+XW+uC/gG9tYzC+iXr5TRqM06zb8+rOVIQBOtK0o7/mBdeLeFNQ7lQUGiHMNCPdcXdKErHAICIAACIAACIKAzAevoGJU19uh6f2I7BSlW9ttt5VTfOWi7W3kdGxVG3zg9h06cleDwnh474s1hisQJ7mf0oIs2QQAEgoUAnviC5UpiHiAAAoYQEC2ZNE5lWZKdwAVD9K0YLFWJv8VC2faqf4+9fYTeqWrVZL7juoADw5+lG2vSMBoBARAAARAAARAwjIAkdVVw2m+/Qd/n7x5qozv/vd+p80+cfj+/bJFhzj+RNJHiInD+Gfbnho5AAAQClAAiAAP0wmHYIAACviUgFYOzZ5opNTaS6joHOCpwUJeKwSvykqhv2Ep/ZaffuElSzx+2V5E53MSOyPjx3R7/lpTm/ZwulI/iIB4zxIkgAAIgAAIg4EsCh1v7qNOmgJheYxkd+4Se2lVNG/c2OHQRztkEX2at4rWFKV7pFTs0PMkOuRfLTY6mRM7QgIEACIAACExNABGAUzPCESAAAiAwKQGpGDyXdfTEEZcUo09lXUn3vZy1/2xN0oJ/u7WcRCtQC7NycZBSThtq7HJM49GifbQBAiAAAiAAAiCgD4GGLlmIHNKncZtWu7m4yAObDzp1/sm90M8uXUhnzk81xPknhdoWZsbB+WdzffASBEAABKYiAAfgVITwPgiAAAi4QCAyLJTyWbdvYVYcxbHujdZ26QmZdPYCte6fFAj5+SulJBo8WphIDUoEgfygPpQWRNEGCIAACIAACOhLoKNvmI62aXMfcLyRyr3Bj5/fxxkD3Q6HrSpIpg0XLqAMTsU1wmayHnNxRiyJExAGAiAAAiDgOgE4AF1nhSNBAARAYEoCMVx5rohvSuenSxU67W5MRXvwK5xWsyI3UTWGvqFR+hmvxrf0aBe5J1GAEg0oYuIwEAABEAABEAAB/yTQN2RVdP9saoXpMtAd5S1014v7qbV3WNW+pOBeu2KuUuxDMiKMMNFfluJlJhQvMwI3+gABEAgyAsZ8UgcZNEwHBEAABKYiEG8OV1JTJCXGFGpfxmOqs52/H8JOwG+uynXQ/etgzZ+7XyrRLB1YehcdoQO8yi9VgmEgAAIgAAIgAAL+RWDIOqos1okmn14mC4F/ffswPbyjikZYKsTWErjq7k8uKKKzioxJ+ZV7KSn0IfrLMBAAARAAAc8IwAHoGTecBQIgAAJTEhivGLyI04IlXUULkxXvW8/Mp4LUGFVzbZwCdPdLB+iF3XU0plEogFQS3F/XRaL5AwMBEAABEAABEPAPAuL0K2/spWGWAtHLOvqH6acvH6RXS5ocupjHkif3X7KQ70UsDu/psYMDDZW+EjS6l9JjjGgTBEAABAKBAByAgXCVMEYQAIGAJhBhClXSVcRpp0WKjLT3/bMLKZvTYGxNggCe3lXDKcGlHMGnTtOxPc6d17Lif5AjAZs1TDF2p38cCwIgAAIgAAIg8BkB0eitbO6lXk7/1cukwNiPWO+vzEmhsXUc8XfH+fNJMh2MstyUGF30lY0aP/oBARAAAX8hAAegv1wJjAMEQCDoCSTGRNBijgZMjY3weq6iNfij8+Yr6TD2jUnU3m3P7aM9NZ32b3m0LY7FquY+5YFDz1QjjwaHk0AABEAABEBgGhGo5sJf7Rz1r4eJc3FLSSPds7FEkQKx7SOMU3C/xTIkX2PNPyP192YnmimJ759gIAACIAAC3hOAA9B7hmgBBEAABFwmIDfNOckxtCAzlsxeVq+T1fc7zy+iy07MIpYHVFn3wAg98J9SeuK9o5oV82jpGaJ97FzUM+pANQlsgAAIgAAIgAAITBBo6h6k+k7tin5NNMwvxni173/fPEx/efsI2S/2JcWE093ri+l0rvZrpKXHRRpWWdjIeaEvEAABEPAVATgAfUUe/YIACExrArGRYUqRkOyZUSTaNp5aCJ98+UlZnI5T5FRncOPeBtrA2oDy0KCFDbAu4AF2AtZ3DmjRHNoAARAAARAAARCYgoA454609tGhlr4pjvTsbdEO/tObh2h7WbNDA8WZcYrenxQ1M9IS2eko0X8wEAABEAAB7QjAAagdS7QEAiAAAm4REOddVoKZFmXFU2yUya1z7Q8uSo+ln126kE6clWD/FlXxA8PtnBK8s6rV4T1PdkhK8NG2fjrY0M1VAfUTIPdkbDgHBEAABEAABIKJgCy87a/vooYubRby7NlI2u/f3jlCO8pb7N+i9Ysz6PZzCsnCi5ZGmiXSRHmcLSHF1GAgAAIgAALaEYADUDuWaAkEQAAEPCIQxanACzLiKDc5mnV1PL/ZlajC760roGtOnU0mu7DCgZFR+t1rlfSnNw7RkHXUo3Han9TZP0J7azupi3/DQAAEQAAEQAAEtCUg0fvyPds3pM33tv3oxPn3FBcPs6/0K3p/t67Npy8um0WyWGmkyT3RvDSL4f0aOUf0BQIgAAK+IgAHoK/Io18QAAEQsCOQEhuppAV7UylYVsvPKU6ney4qpjRuz94kvefHz+8nERHXwoatn1AJRwJWc0SgPEjAQAAEQAAEQAAEvCMg0fVljT1Kyq9E3etlL+yupxf31KuaD+X7iFvWFtApOYmq/UZshJtmKMXNwlgvGQYCIAACIKA9AXy6as8ULYIACICAxwQiw0JJ0nnlJtgbE62e+y9ZSCvzkxyaqWP9vjte2EfbDjZp5rSTNg/Ud9MgRxrCQAAEQAAEQAAEPCMgUfV7a7t0q/Q7PqpN+xro/z6oGd9UfkvG7bfX5NJJsx3lRFQH6rARypGG89JiSe6DYCAAAiAAAvoQgANQH65oFQRAAAQ8JiDpL/PZCSgpON6YtHPj6jz61qpcijCpP+5HRj+hR986TP+zvVIzp13PoFWpEtzaO+TNsHEuCIAACIAACEw7AhJFL9H0Bxu7adiqr77uttImevzdow6Mb1iZQ6fmOi4cOhyo8Q5xPBakxlBMhHd6yBoPC82BAAiAQNARUD8RBt30MCEQAAEQCEwC5nCT4gT0RhNwfOanFyTTzzgacI6TanpvV7VxSvA+zVKCrexYrGjqpcrmXhrVM29pfHL4DQIgAAIgAAIBTkCi5/fXdZNE0+utpvF2ZSv9+c3DDsS++rk5tHpeisN+I3bkcNZCvDnciK7QBwiAAAhMawJwAE7ry4/JgwAI+DOBaF4Jl0hALZyA6fFRii7gOQvSHKZcz5UFJSVY9AG10vFr6Rmi3TUdXLVwgMbgCHRgjh0gAAIgAAIgIASalUIfXdQ7ZNUdyK4j7fSH1yvJXlbwi0uz6Wwn9we6D4g7yEqIItFAhoEACIAACOhPAA5A/RmjBxAAARDwmICkw0g1PNHG8dZEVPsaXuH/7pkFZOb0YFuTlGCpEPzHHVWapQRLgZAjrf30MTsC6zmqARGBtsTxGgRAAARAYDoTsHKhj4qmHqpq6TPk+3FPTSf9blsF2a/JXXpCJq1fkumTS5ESG0HZM80+6RudggAIgMB0JAAH4HS86pgzCIBAQBGIjQxTnIAa+ACVeS+dO1MpECKFQuztzYpWjgbcT7Ud2lQJlvbFEXiUdY0+ru5Q0pvgCLSnjm0QAAEQAIHpQkAi7SXqb09tJ7X2Dhsy7YMN3fTrLeVktfP+nVecRpeflGXIGOw7iTeHkaT+wkAABEAABIwjAAegcazREwiAAAh4TCAuKowKuTqeVk7AVE63uXv9AlpXlOowpmNVgvfTG+UtDu95s0OiDEXgXByB4mCU6AcYCIAACIAACEwXAt2DI4rWn0T9yeKYESaavL94pYyG7b5z1xam0NXLZ9MMqcBhsCVbIrjoh8UnfRs8VXQHAiAAAn5FAA5Av7ocGAwIgAAITE4gjlfL5YZZKyegpAR/bcVcumVtPkWFqVOCh7gCoaQDP8I/Q9bRyQflwTviCKxpH+DU4E7+DUegBwhxCgiAAAiAQAARkCIfku57gAt9GKH1N47maFsfPfCfgzTA/dvairwkupa//412/oWbZvBipoXyUmI0kTaxnRNegwAIgAAITE0AtdanZoQjQAAEQMBvCCREh1M+OwHL+UFCq0qBy3MSuUJwNP12W7mSqms72dc5CrCqpZduYd3ATC4koqVJxeDajgFq5FSoNI5ITI+L5IInWJfSkjHaAgEQAAEQ8B0BkbwQDVz5scu+1X1Q0uf9m0upb0jt/Fs6J4G+tSqXQrRaTXRxJkkx4TSHU35l8REGAiAAAiDgGwL4BPYNd/QKAiAAAh4TmMlOQFk91zJrJ42db/esL6Yz56c4jKuGnXQ/fn4fvVXZ6vCeFjvGHYEfVXdSI1ckhoEACIAACIBAoBNo6Rmi3RzpLgtdRjv/Kpt76O6XDlD3wIgK46KsOLrpjHxDo+/CQmdw9kKMsngJ55/qcmADBEAABAwngAhAw5GjQxAAARDwnkBSTIQSASjReVpFAoabQujrp+UoWoOPvnWIqwF/ptEnKcH/s72SqlhL6Krls8gUov36kURKHG7to47+YcpNjiEZDwwEQAAEQAAEAolAD+v8SeGrnkGrT4a960g7/f61SgfNP0m9/e5ZBYZG4CVy1J8UHIPjzyd/CugUBEAABBwIwAHogAQ7QAAEQCAwCIiItlQTFDFxLU20geSG/b+3VVA1a/TZ2n8ONCr7bjkzn6Q6sR7W2T9Ce7k6Yg47ASXaEQYCIAACIAAC/k5A9HJF17alx5jKvs54bN7fQI/vPEr25UVyk6Pp+2fPowiTWu/XWRta7JOoP0n3lcVKGAiAAAiAgP8QQHiF/1wLjAQEQAAE3CaQwtp583hVX262tbQM1vu796JiWjPPMSW4pKGb7nh+P0c4aOt4tB2/FAopa+xR9AclMhAGAiAAAiAAAv5IQCL+JHp9T02Xz5x/Y/w9+fedR/jH0fm3MDOOfnTefDKHGxP3kRAdRouy4uH888c/VowJBEBg2hMw5ptg2mMGABAAARDQj4BEycVExNOh1l7q6FPr/XjTq6Tg3nB6juJg/DOnBItTbtxaeoforhcP0DdZSFyKiOhlzd1DioaRaB5adIo41GvsaBcEQAAEQCA4CQwMj1Irfw/Kj61chi9mK5GHItGx60iHQ/erC5Lp6yvn6iLbYd+ZiRciZyeaKcUSaf8WtkEABEAABPyEAByAfnIhMAwQAAEQ8IaAOOsK02KpiSvqivaQllFzq/gBQioA/3pLGevzfeZgFF1ASROWSMDPn5xNIVpWJbGBIQ9XB+q7lTFkJURx8RNtox1tusJLEAABEAABEHBKQBxtbb3DitPPvrKu0xMM2ClFPn7xahlVsj6vvX3+pCy65IRMQ74z481hLNsRbViKsf1csQ0CIAACIOAaATgAXeOEo0AABEAgIAikckqwaPPJw0DvkHYC5BKBd98lC+k3W8qpwu5B44Xd9You4LfX5OmWYiSFTqSSYhc/7MhYIsOM0TEKiIuOQYIACIAACOhCYGR0jNr7jjn9uge0+07VYrANnQP04CulvPA3pGouNGQGfYOj91fmJ6v267WRFhdJczjyD4tzehFGuyAAAiCgHQFoAGrHEi2BAAiAgF8QiAoPpeLMWDoWLafdkBLM4XTnBUVOdQE/qu6kO1/YT/X8QKKnSVXFvbVd1MyRjjAQAAEQAAEQ0INAG6f2ljZ200dHO+gQF9ryN+efaOT+hGU47J1/Zv7+v/3cQsOcf9kzo5SiYXD+6fFXiDZBAARAQHsCcABqzxQtggAIgIDPCcjNePZMMy3IiOVoOe0+6sNCQ+h61hP62oo5FGqXilvfNUh3sBPw42pHHSItgUh6s1Q+Lm/qYV3CMS2bRlsgAAIgAALTnIAsZJU3HdPU9ccaVO8eaqP7NpU4RPknxYTThgsX8Pd+nO5XUL7+c1OieaHRrHtf6AAEQAAEQEA7Ato9FWo3JrQEAiAAAiCgEQEpnCHV+FJiIzRqkZQ0n3VFafSj8+dzYQ61ksTAyCj94pUy+vfuOvpE8nZ1NNFi2lfXRVY4AXWkjKZBAARAYPoQkHTf6vZ+v5ywfKdu3FuvaO/aFuWSwc5NiqZ7LipWFv70HrykGBemWVDsQ2/QaB8EQAAEdCAAB6AOUNEkCIAACPgTAblZz02OUar5hnGVPq2sKD2W7rt4oVL1z7ZNcfs9tauGHnqtkqsjjtq+pfnrIS4QcoSLnsBAAARAAARAwBsCopsr+rk6r115NESJfH/snSP0xHvVDucvyY6nn7A8h8h06G1yD1HEmQXxBvSl91zQPgiAAAhMRwJwAE7Hq445gwAITEsCM6PDlWjAhOgwzeafbImgu9cvoFNzEh3a3MlpSj9+fp+SEqxnNGBLz5Ai0u4wAOwAARAAARAAARcISIXfMtb8E0ebv1kdF8C668X99GpJk8PQzpyfQt9bN8+QwlgiJ1KcGUcxEerIf4dBYQcIgAAIgIDfEsAnuN9eGgwMBEAABLQnEG4K4dSdWGruGaSjHDlnHfX+YSfCFEo3nZGnVAGUyD/bFkUX8OecEryIHxquXj5bt/SkQy29/FASTzI/GAiAAAiAAAi4SkCcflJUY9hq++3l6tn6HTfGoYj/2d/IEfXVrHfrOLYvLptFFy5KN6T6rjj95nHaL75j9bveaBkEQAAEjCCAJyUjKKMPEAABEPAzAimWSI4GjOM0Hm2iAaXoyPolmfSDc+aRVCG0t72s1Xfbc3vp0TcPUdfAiP3bXm/Lw9Hh1j6v20EDIAACIAAC04eARKdXNPdQ35C+chXuEpVK9z99uYQef/eog/PPxLIeN/Oi2/rFGYY4/+Q+QdJ+4fxz9yrieBAAARDwPwJwAPrfNcGIQAAEQMAQAhK5N591/KSSn0kjbcAl2Qn0UxYiF0FyexNdpW2lzfRfT++mF7lIyLBV2wq+It4uD00wEAABEAABEHCFgGjIdvRpvyjlSt/OjhGH5LbSJmXB7GBDj8MhGfGRtEFkN3KTHN7TY4fIfEjBD9EShoEACIAACAQ+AaQAB/41xAxAAARAwCsCEg0YFxWmRNBp8SCUHh9FP724mN6qaFVSlzr61Q9XUin4SU4V3nqwmSSFaXnOTM2iGORhLpbnEhnmGIXoFSScDAIgAAIgEFQEGroGqJFlKvzFZBHrfzlKfndNp9MhnVucRlcunWVYJF4mf5fPSjQ7HQt2ggAIgAAIBCYBOAAD87ph1CAAAiCgKQGJBtRSGzCEU4JPL0imZXNn0sa99fTSngYaHlVH/LX0DtHvXqug/xyIoS8vn0N5KTFez0m0nKpYD3BBRpzXbaEBEAABEACB4CTQwc420cH1B5OoPyma9Ze3DztNRU6KCadvrcrlNFxjvtf465s1faMpLS7SH/BgDCAAAiAAAhoSgANQQ5hoCgRAAAQCnYDW0YASiXf5Sdl0RmEqPc1C5m9wVKC9lTf10p3/3k8rchPpSo4ITIqJsD/Ere3uASvVdw5QBkcvwEAABEAABEDAlkDfkJV1/3pJZCl8bd2DI/SXtw7Te4fbnQ5lzbxkpYCWOdy4R7ac5GiSewEYCIAACIBA8BEw7tsk+NhhRiAAAiAQlAS0jgYUSDOjOYJhdR6dvSBNETUv5YqL9vZ2VRu9f6SdvnByNp23MJ0kitBTq2nvVwqcGPnQ5OlYcR4IgAAIgIAxBIasoyTfPxIt7mv78GiHkvLrrDBWPEtZXH96Dp04K8HQYc5JMsP5ZyhxdAYCIAACxhKAA9BY3ugNBEAABAKGgNbRgDLxnOQY+skFRbTrSAc98d5Rau4ZUvGQar5PvFdNB+q72WGYS7GRnlUplme7quY+Ks6M1UxfUDVQbIAACIAACAQUAXH6lTf2al6Ayl0IEvX3T/6e21He4vTUU3MS6Wsr5pDFw+8/p426sDMrIYrS4xA57wIqHAICIAACAUsADsCAvXQYOAiAAAjoT2AiGpCr6x5u7SMtgiZmcGSfaAOeMCueXjnQSM9/XEf9w6OqyYgI+u3P7aPvrMlTKhWr3nRxo5fTvGo7Bih7JkTMXUSGw0AABEAgKAmIzl4lp/3K94KvbIzHIE4/cf45G0dMhImuZcefURV+bTmkxkbgu9IWCF6DAAiAQJASgAMwSC8spgUCIAACWhJIiY0kcQaWN/eQlaP0tLCw0BC6YFGGUizk2Q9raUtJE9m2LBUR7325hC4/MYsuXpJJISHupwTXsRZgvDnM8EgKLfigDRAAARAAAW0ISMEP+U7xlYksxZ9Z66+syVH+QsZ0Qna8kvKbYA43fIhSZGRuUrTh/aJDEAABEAAB4wnAAWg8c/QIAiAAAgFJII4daUXpsayf1M0pVLauOu+mI2m+X1sxl5bOmUm/315JtnpIItL+L3YOljR007c5GtDdhyM5v6qljxZmxlGoBw5E72aGs0EABEAABHxNoJkj2Bu6Bn0yjMGRUXr2o1ravK+RRuULyc6iuFDWl5fPptVc7EOi4402WSDLS4nxSd9GzxX9gQAIgAAIEIUAAgiAAAiAAAi4SiCaU5QWZMRRVHioq6e4fFwxO+keuHQh6/bFOZwjmoA/5JTgvbWdDu9NtWOA04urOfoCBgIgAAIgML0IWEfHfPb5/wEXtfr+M3to494Gp86/U1gK4xeXL6I1hSk+ccBZIk1UkGrxSd/T668QswUBEAAB/yGACED/uRYYCQiAAAgEBIFIjlhYkBFLZVxJsWdQWz2leE5/uv3cQnpxdz1H/tWoNAe7B0bogc2ldNGSDLr8pGy3IvoaOfpjJrctUYwwEAABEACB6UFAIv+kuJSR1sLFrR575wh9VN3htNsUS4RS5GNJtrEVfm0HY+ZFvMI0i1vfo7bn4zUIgAAIgEBgEoADMDCvG0YNAiAAAj4lIPp9kg5cwaLqWusqhXAa1MUnZFJhuoUeeq1S1b48xr3AzsGDDT100xl5lBgT4TKHypZeWpwVRyYeOwwEQAAEQCC4CQxbxwxN/bWOjdEmjvZ7jgtbDXHf9iYyFOsXZyiatuEm330PRYSFKMW18F1of4WwDQIgAALBT8B33z7BzxYzBAEQAIGgJiBFOQpSY0iqB+phhWmxSkrwiVwt2N5ESF1Sgj886jzCwv542ZaHwSNtfc7ewj4QAAEQAIEgI1DPRaBGtShd7wKXUtaplcr1T+6qcer8kwWzBy9bRF84OZt86fwLN81QFu98OQYXcOIQEAABEAABnQggAlAnsGgWBEAABKYDAREtz0mOUR5oatoHNJ+yhQuEfG/dPNq8v5H++X616mGud8hKv3y1jM4tTqOrTpntUipTS88wVwQeZKdlpOZjRYMgAAIgAAL+QUCKbzRx8Q89ra13iHaxzt97h9u5OJbz6r6xUWFKkY8VuYk+19ozhc7gtN9YEhkPGAiAAAiAwPQkAAfg9LzumDUIgAAIaEogK8GsOAEPccVdJ4UOvepLnIznLUxXxMofeq2CmllfydbEOSjpVtevzLHdPelrGWMrP7jNSYwmKWoCAwEQAAEQCC4CtR0DKg1ZrWYn+n7vs8PvvcNtigTGZO1KPd+181PoiqWzKMYPvmc4YJ/mseYfvvMmu2LYDwIgAALTgwCefKbHdcYsQQAEQEB3AimWSApnfb3ypl5VpJ5WHeelxNDPuErwn944pERc2Lb7Wmmzkta0Ii/Jdvekr7sHrLSvrouSWYw9+1Pn5aQH4w0QAAEQAIGAISCV32WRRyuTSEKJ8nufnX5VvIA0lc1ONNN1p82lvBTLVIca8j6voSkLaLEcUQ8DARAAARCY3gTgAJze1x+zBwEQAAFNCUgV3yKlQnA3a+5pX3nRHG6iW9bm09aDzfT4u0dU1R0ffesQpyNHU3pclEtzkkjF5u4hpchIZnwUpXFasOgawkAABEAABAKXQE1Hv9eR6A1dA586/drpcOvUTj+hFc2VdS87KYvWFaW5JElhBGFx/uWyTEdCdLgR3aEPEAABEAABPycAB6CfXyAMDwRAAAQCjYCkOy3IiFM0kSQSQ2uTlOCzilJZxyiE/vB61UTzgyNj9N/bKuie9cVuiaxbRz+ho239il7UbE4LnokHpQmmeAECIAACgURAtGHbeoc9HnJj1yA9vKOKpNCUK2Zmp99JsxPolLmJtDAzzq3vHlfa9+aYONYflGhEpP16QxHnggAIgEBwEYADMLiuJ2YDAiAAAn5BQETGF/HDkOgw1XMkhda6gDLJlfnJdKC+m3aUt0zMWRx5T7x3lL62Yu7EPldfiAOxjIXc5aFpTpKZJNoQBgIgAAIgEDgEatr7PR5sz+AI3bephNOHj+9AlEWuk8XplzOTinmxy8TSF/5ksjiGxSx/uiIYCwiAAAj4DwE83fjPtcBIQAAEQCCoCEg67SyOPkiyhJMU3ugZtGo+v69+bg4LsfdQfedn1R5fLWlSIhCXzZ3pUX9dAyO0t7ZLqRSclRBFYX72cOfRpHASCIAACAQ5Afns7uwf8WiWY7xKJRHlkzn/LJEmWjZnJsn3ishcmEL8y+knk5Yqv5Cz8Ojy4yQQAAEQmDYE4ACcNpcaEwUBEAAB3xCQSLoF/MDUyELqNe0DmhYIkUjDW9YW0B0v7FPpAf7pjSqay1F8yVyYxBOTiEVJBRMheXECij6gpB7DQAAEQAAE/JOAN9F/L3xcR7trOlUTi+VocHH6ncJOv/npsX6j66caJG/IV1OKFLSaacaClT0cbIMACIAACKgI+N/ylWp42AABEAABEAgGAuI8k+Ici7PjWIxc20qEs/ih5xqOBLS1PtYe/N1rlWQdG7Pd7fZr0Qc80tqvVDb+RI88ZrdHhBNAAARAAATsCXT0DXscZS4V4Z/5sFbVpEhBPMBV57/O1XyLWc4i1E8LRMWbw2hRVhwXwIqB8091BbEBAiAAAiDgjAAcgM6oYB8IgAAIgIAuBCJMoVSYFkv5qTEslq5dRN0Z81Lo1NxE1Zgrm3vp6V01qn2ebrTzw2V5Uy+NjWlf2djTMeE8EAABEAABYo3ZT6jaQ+2/No7yfui1CrL9ZJeIupvPyKMErmrvrxbFxUcK0yxKZCL0av31KmFcIAACIOB/BOAA9L9rghGBAAiAQNATSIqJoMVZ8ZQSG6HJXCXC8DqO1JBUXVvbuLeBPq7usN3l8WvFCch6g3ACeowQJ4IACICA5gREt6/fg4rzEiH+O3b+2evTXnlyNuv8xWk+Ti0aDGOdv7lJ0fz9KdH0/uug1GKuaAMEQAAEQEB7AnAAas8ULYIACIAACLhAQCon5nLakgiqSzSDtyZREDevzWdxdnVkoQi7S5SHFtbRN0LlcAJqgRJtgAAIgIDXBCT6r7bDs8q/T75XrUR22w7ixFkJdMHiDNtdfvM6mXX+FmfHU1ocNGn95qJgICAAAiAQYATgAAywC4bhggAIgECwERCtpUWssSTFNiT1yhuTyIirl89WNdE7ZKXfb6/UrPiIOAHLmhAJqIKMDRAAARDwAYHmniEaHHFf6/W9w220aX+jasRSSONbq3MpxNsvIlWr3m9EhIVwqq+F8lKg8+c9TbQAAiAAAtObAByAfnT9JYXNlZ/Vq1drMuonn3yS1q1bR2lpaRQZGUmzZ8+mq6++mnbu3KlJ+2gEBEAABFwlEMJRe1LBUKIBvdUGXFeUSkvnJKi6Lm3soec+Uou8qw5wc6OzH05AN5HhcBAAARDQlIDIMdR2DLjdZkPnAD2y45DqPEmtvfXMAoqJMKn2+3JD/JDpHO0nchnxfqxH6EtG6BsEQAAEQMA9AnAAuscrKI4eGBig888/n770pS/Rli1bqKmpiYaGhqi6upqeeOIJOu200+juu+8OirliEiAAAoFFIDYyjBZmxlNslOcPYbKQcsPpuZQUo9ZHev7jOtrP1R61MnECimMRmoBaEUU7IAACIOA6gYbuQRq2uhf9N2Qdpd9sq6CBkVFVR9ecOkfR1lPt9OGGyGIs4AWxORzV7q8ViH2IB12DAAiAAAh4SMDzJywPO8RpUxP41re+RTfeeOOkB0ZHR0/6nitvXHhaPAMAAEAASURBVHvttbRp0ybl0DVr1tAtt9xCGRkZtG/fPrr//vupqqqKNmzYQOnp6XTDDTe40iSOAQEQAAHNCISbQqgoPVap6ljfOehRuxLFcfMZ+XT3SyU0yhpRYvKvpAI/cOlCzaIpugaOOQGlGqNEMcJAAARAAAT0J2AdHSOJ5HPHRC/wL28dphq7isEr85PojMIUd5rS7ViJ+suMj1J+8J2iG2Y0DAIgAALTlgAcgH546VNSUqi4uFiXkb322mv01FNPKW1feOGF9Pzzz1No6DHx/aVLl9L69evppJNOUqIBb7vtNvr85z9PCQnqVDpdBoZGQQAEQMCGgETxzU6MVtKxqlr6PNLvy0+10JXLsukJFnofN3HY/Q8XBbn93ELNdJ6kzYON3VSYFotIjXHQ+A0CIAACOhJo6BqkkdFjizuudrO9rIXeqGhVHZ7N2rPXrpirSPCo3vDBhixc5SRHU7QfpSH7AAO6BAEQAAEQ0JEAUoB1hOuPTf/yl79UhmUymegPf/jDhPNvfKxJSUn04IMPKpudnZ306KOPjr+F3yAAAiBgOIHEmAhOCY7zuErweQvTaQlXTbQ1SQN+gdOBtbTuASunA3d75KjUchxoCwRAAASCnYCk/YoD0B073NpHj71zWHVKVFioovsXyb99aRI8PjvRTMWZsXD++fJCoG8QAAEQmAYE4ACcBhd5fIo9PT20bds2ZfPMM8+krKys8bdUvy+99FKKjY1V9kmEIAwEQAAEfElAtJDECZhop+nnypikmqNUdUwwh6kO/9eHtbThxQP0wdF2Gvs0RVh1gAcbcAJ6AA2ngAAIgICbBOo59XeUC4C4alIJ/rdbyx0iBm84PYcyON3WlyZ6t4t5kUrGIZHvMBAAARAAARDQkwAcgHrS9bO2d+3aRcPDw8qoVq1aNenowsPDafny5cr7cs7IyMikx+INEAABEDCCgIigF3BK75wkMz8kudejFBa5ifUA7c8ra+qhX71aTj94Zi+9XtbMD4fuick7G4U4AQ82IBLQGRvsAwEQAAFvCQwMj1ITF/9w1UT37+EdVdTcM6Q65ZziNFqek6jaZ+SGiasOS7rvgow48nUEopHzRl8gAAIgAAK+JQAHoG/5O+39X//6FxUVFZHZbCaLxUL5+fl0zTXX0Pbt250e7+rOkpKSiUMLCwsnXjt7Mf6+1WqliooKZ4dgHwiAAAgYTiA9LoqKuDJiuMk9L+B8LiryhZOynY63jqNJHnnjEN3y1Mf00p566h+2Oj3O1Z09g8ecgFo4FF3tE8eBAAiAQDATEEeeRP7tYwkHN4L/6KW9DfTh0Q4VmvyUGLpq2SzVPiM3EqLDaFFWHKXGRhrZLfoCARAAARAAAUIRED/8I7B11MnwKisrlZ+///3vdPHFF9Njjz1GcXFxbo+8trZ24pzJ0n/HD8jO/uxBuaamRnFIjr/nym/bvpwd39DQ4Gw39oEACIDAlAQkom9hZjyVcwSfONtctYuWZFBcVBg9+1EttfUdi4a2Pbejf4T++X41Pc/6gGcVpZJEiCSYw20Pcfm1jEseVOdx1CIE3V3GhgNBAARAwIFAz+AIiYZf39Cow3vH2yHfEU/vqlYdYok00S1r88kUanwMRBhH/c1irb8UCxx/qouCDRAAARAAAcMIwAFoGOqpO5KIP6nCu3btWpIIvJiYGGppaaEdO3bQww8/TG1tbfTCCy/QRRddRFu2bKGwMLWm1VQ9iAbguEnbx7Po6OiJt3t7eydeu/rC1oHo6jk4DgRAAARcJRBuCuHUqVg60tZPjS6KwYu+0prCFFpZkEQ7q9qUaL+ajgGHLgdGRulFjgTctK+BVuYn0wWL0j3SiRoaGaMD9d2Uy2leUswEBgIgAAIg4DoBK8syHG3vp+ZudfquKy3Iuf/75iFVtKDEjX9nTZ5PPo9Fw3YOV7aX7y4YCIAACIAACPiKAByAviLvpN+6ujqKj1dXq5TDzjrrLLrpppvo3HPPpY8//lhxCP7xj3+km2++2Ukrk+8aHPxMM0V0/o5nERGfPawODDg+IB/vXLwHAiAAAkYQEIfe3KRokge91l7HiL7JxmAKCVEce6flJdHumk5OEatn3b7PFkjGz7Nyntl21gYUfcCTZifQFzllzF3BeBGqL2/qpSzWrcqeaR5vGr9BAARAAASOQ6C5Z5Bq2Pk3bHW92Idtc7KAU2u3wHPpiVmceut4n217ntavRa5CHH9YBNKaLNoDARAAARDwhAAcgJ5Q0+kcZ86/8a5SU1PpmWeeUSIDpSjHQw895LYDMDLys5SD8WIg4+3b/x4a+my1NSrK/QppkjZ8PJMU4GXLlh3vELwHAiAAAi4RyEmOoYGRLrfTw8SBeMKsBOWnsrlH0Yradbid7B83ZfsD1pAq4eIePzynkPI5rdddkwfRfnYC5rH2lBQ0gYEACIAACDgSkCIfh1p7SQoqeWot7Dx89qM61elzOPX2khMyVfv03ki2hNNsdv6F+SDdWO+5oX0QAAEQAIHAJAAHYABdt5ycHCUacNOmTYomYH19PWVkZLg8AykoMm5TpfX29fWNH6qkIk9suPhiKo1BF5vBYSAAAiAwJYHxCsH7WXNvZNTefTfl6coBeSkW+q8zLdTQNUAvs2j8GxUtDm2JA+/+zQfp+2cXUhEXFXHX2ll3UMY4L82Cqo/uwsPxIAACQU1gjKOlpSCTFPpwp8iHPRQpFvLYO0do2Kaquyy5fP20HMMWXyTNN4ej0xOij59tYz92bIMACIAACICA3gQgRKE3YY3bl+rA4yYpw+6YrVNuqiIdthF80PNzhzKOBQEQ8AWByLBQymcnHgf1eWVSZfi6lTn0uytPoIu5aEh0eKiqvUHW9Xtwcynt4dRhT0yciFIcpLPf9ZRlT/rBOSAAAiAQKATk83BPbaeSsuuN80/mK9HaH1WrP5/P5KJOEn1thKXERtBirvAL558RtNEHCIAACICAuwTgAHSXmI+Pl5Q1T83WeVhaWnrcZsbfN5lMlJ+ff9xj8SYIgAAI+AOBOHMYp1tpo7MXz9V/r1g6i373xRNoSbZaM0oiS375ahk/aLZ7NG0rRymWNvYokS4eNYCTQAAEQCAICIzwZ2kFV+oVDVZZXPHWBrmAk0T/2ZpUfr/i5GzbXbq8lurCEhmey5IUvqgwrMuk0CgIgAAIgEDQEYADMMAuaUlJycSI3Un/lZOWLl1K48U/pLLwZCb6gO+++67ytpzjbrXhydrFfhAAARDQm4BE8InuklZmDjfR/zurgJbNmalqUgqE/HZLhVJNWPWGixucpUZHuYKxaA9K6hsMBEAABKYTAYn628tRf+4UcJqKzzMf1pJILdjal5fPpugI7RWPRMo1ITpMqfIuRaKKM+NIFqFgIAACIAACIODPBOAA9OerYze2w4cP05YtW5S9ubm5lJnpnpixaACuXbtWOX/r1q00WRrwc889R93d3cpxl1xyid0osAkCIAAC/k0gJymGYjR84JNojpvX5tOK3ETVxEfZi/fQ9gp6o7xFtd+djZaeYTpQ301D1lF3TsOxIAACIBCQBGTB40hrnxL152mFX2cTP9rWR5v3N6jeKs6Ipc/ZfW6rDnBzQyr6Soqv6LiezItChWmxvB1JovkHAwEQAAEQAIFAIIBvLD+5Si+99BJZrZNXPGtqaqLLLruMxqv33njjjQ4jf+yxx1j/aobys2HDBof3Zcf3vvc9Zb/09e1vf5tGR9UPna2trXTbbbcpx0hV4uuuu055jX9AAARAIFAIhHBoRkFaDD+UeS6ZYD9XKTRy4+o8Wl2QrHpLIvn+uKOKth5sUu13Z6N3yKoUB+keHHHnNBwLAiAAAgFFQCr87q/v4mJLg5qOe4w/iP/81mFV8RATf2Zfu2Kuck/sTWdm1oHNjI/iCL9YOpGrxkuK70wu7oFq7t5QxbkgAAIgAAK+IqB9TLyvZhLg/d500000MjKiOPlOPfVUmjNnDkVFRZE45F5//XV65JFHlNcyzdNOO01x3nky5TPOOIOuvPJKeuqpp+jFF19UqgrfeuutSjXhffv20X333UfV1dVK0w8++CAlJCR40g3OAQEQAAGfEogwcVGQVAuVcHSdOOm0MHEsXn96jhLt8WqJ2uEnD5/D1jE6b2G6R11JJIyMVR40sxKivH5o9WgQOAkEQAAEdCLQ1D2oRP7poXiwvayZKpp7VSO/iIs4pfPnqScmctuzZpoVR58UmIKBAAiAAAiAQLAQgAPQj65kfX09PfTQQ8rPZMOSKMBHH32UIiIiJjtkyv1/+ctflBTfTZs20fbt25Uf25NCQkLozjvvpBtuuMF2N16DAAiAQEARiI0MozmJ0XSY0820shB+Mvzq5+YoTsCNe9XpZo+/e1RxAl58gnvyDONjE0dlbccAVwgeodyUaBL9QRgIgAAIBDIBKfRxqKXPQZtPqzl1DYzQk+8fW7gebzON03LXL/bsc1jakIWYDA+dh+NjwG8QAAEQAAEQ8EcCeLrwk6vyt7/9jaQwx86dO+nQoUNKtJ/o8MXExFB2djZ97nOfo2uuuYYkOtBbk8jCl19+mf75z3+SpA3v2bOHOjs7KTU1lVauXEnf+c53NOnH23HifBAAARDwlkBaXCT1DVupuXvI26YmzhephS8tm6U4AZ/7qG5iv7x4+oMa1vMboy+cnOVxFJ+kBO+r7aJsjkBJ5/F7U/1dNThsgAAIgICBBLp4MaOypYcXRjQKw3Yy9ifeO0p9Q2o5m6+tOLZI4+TwKXeJfqxEYcNAAARAAARAIBgJzPiELRgnhjn5NwEpQCKOTbGamhrKysry7wFjdCAAAgFLQETnSxq6qWdwcp1VTyf379119NSuGofTJRX46lNmee28s0SaKC8lhpCG5oAYO0AABPyUgHzm1nT0U32ntlp/9tMtYT3Be18+qNp9Khf9uPmMfNU+VzdE128hV/ONYt0/GAiAAAgEGwE8fwfbFfVsPigC4hk3nAUCIAACIBAgBES7Lz9V26Ig41O/aEkmXXPq7PHNid+b9jXQX94+wqL03q2xidNyL0cDin4WDARAAAT8ncB4oQ+9nX9WTi3+89uHVTiiWK/vy8sdP49VBx1nQ3T/4Pw7DiC8BQIgAAIgEPAE4AAM+EuICYAACIAACExFYLwoCPsCNbdzitPpupVcbdKuZakM/MfXq8g6Nmb3jnuboxxNIxpaBzmKcciqTnVzryUcDQIgAAL6EJAiSA1dA7xg0emQkqtHj6LBau9kvHJpNiWYwz3qLt4cRiIZAQMBEAABEACBYCYADcBgvrqYGwiAAAiAwAQBKQoyNymaqtiZprWtLUyl8NAQ+uOOKlXV4bcqWzn1eIRuPbPA6zReKQ4i0YBS2CTZ4nkhKK3njvZAAASmHwFZjJAI5W4uwtHNvyXyzyiTiOjnPq5VdZfDn+1nzk9V7XN1Iyx0BuUmx7h6OI4DARAAARAAgYAlAAdgwF46DBwEQAAEQMBdAilcHXKEI+oauwaVir3unn+841fmJytOwIdeq6RRm9TfPey0++nLJfSDcwpJnJDemHX0E6ps7qWO/mHFmRnGTkcYCIAACOhNQBx+3QPs8OMFDXH6DY54F9ns6XhFuvyxd47QCH8WjhvXZaKvnzaXRO7BE5OFoXATPks9YYdzQAAEQAAEAosAHICBdb0wWhAAARAAAS8JZMZHkfzIg2xrzxC19w2rHia9af6UnETlQfK3WytomDWqxk2iDu9+8QD98Nz5mkTvtfUOK5GF+akWr52K42PEbxAAARAYJzDCn18Sdexrh9/4eMZ/v3+knXbXdI5vKr/XFaVRjocRfMmWcEqMQUS1Cig2QAAEQAAEgpYAlruC9tJiYiAAAiAAAscjINF48tB40uwEmp9uYcdcOEkVSG/thFkJdMf58ykmQr3GVs9RhxteOkA17f3edqGcP2z9hMoae6h/WPvqxpoMEI2AAAgEJIG+oWPFhyTauLl7yGfRfvbwJM34bxz9Z2ui3feFk7Nsd7n8OiIsRJFUcPkEHAgCIAACIAACAU4ADsAAv4AYPgiAAAiAgHcEZnD+WDwLx+elWOhkdgYWcMXgmdHh5I0vUCLzNly4gBK5HVuTaMO72QlY2thtu9vj15ISfLChB8VBPCaIE0EABGwJdPBn1IH6bs0lEmz78OS1VFT/5/vVLH8wojr9K8vnkDlcvdiiOmCSDUkbFt0/E2QUJiGE3SAAAiAAAsFIAA7AYLyqmBMIgAAIgIBHBERDStLB5qVZlMjA3JRoiosKI3lYdNcyE6Lo7vULlHRj23P7OIrl/k0H6cOjHba7PX4t1TdL2QlotUk59rgxnAgCIDBtCUhxjbKmHpLK4/5k++q66EfP7yOprG5ri7LiaHnOTNtdLr9O54q/8tkOAwEQAAEQAIHpRAAOwOl0tTFXEAABEAABlwlIZEiKJZKKMmI5KtDiUUSgOBMlElCiCm1NBOx/vaWMXi9rtt3t8et+diqWcjrwmJ89uHs8IZwIAiBgKIGjbX10iLVKbeoXGdq/s86qWS7hgc0HlQWTo21q6QSp3Hvtirm8OOP+6ow5PJSyE8zOusQ+EAABEAABEAhqAnAABvXlxeRAAARAAAS0ICApwYVpsR5pBMZEmuhH582nE7LjVUMRX90jbxyif++u44du7yNuegatVNnSq0lbqoFiAwRAIGgJyKJBOUf91XcO+s0cRSrhkR1V9MPn9pJUUXdmVy+fTalc1d1dE2mHvJQYjysGu9sfjgcBEAABEAABfyLgvmiGP40eYwEBEAABEAABgwjEsdi8FAuRSDvR3nPHIkyh9N11BfTom4dpR3mL6tSndtVQ18AIyQNtiAfRLLaNSXXgsNB+mpsUbbsbr0EABEDAgYBU+pVCQrJ44A8mRT5e2ltPL+9tUFVRtx2bVHC/6pRZJMWWPLHsmWaKtivQ5Ek7OAcEQAAEQAAEApEAHICBeNUwZhAAARAAAZ8QsHDl4AWcEnywQUTy3XMCmkJC6Bun5yi6Uy/uqVeNf/P+RupmJ+A3V+V6LUrfyNWGw00hDtqDqg6xAQIgMK0JiLNNihENjoz5nIN1bIy2lzbTMx/VKZ+DzgYUz3p9l3O139UFKR5FYkubsVEmEu0/GAiAAAiAAAhMVwJwAE7XK495gwAIgAAIeERAKk4uyIijEnYCDrn58Cx6VV9cNktxAj7+7lFV/29XtSmROLeeWUBRrFHljVWzXpZoZImGIQwEQAAEbAl0D45QOUf+iRapL02kDz7gYkhPcXXfel64cGYRvJhxwaIM/kmnyDDPPxdN/HkoVX890Qx0Ni7sAwEQAAEQAIFAJAAHYCBeNYwZBEAABEDApwTkQVQiAaX6rhTgcNfOW5jO0Shh9PDrVTRqo/+3l6td3r3xAP3g7EIS3UFvTAT9w7mQSbzZu3a8GQPOBQEQ8C8Crb1DVNXcS76uFyS6g0+y408kFZyZqCGcMS+FLj8pS5PPsNmJZq8ciM7GiH0gAAIgAAIgEGgE4AAMtCuG8YIACIAACPgFAdH1kwrB4gTsHXJfQ+u0vCSysBbVb7aW05D1szQ8qXZ557/3023nFNIs1qvy1MSvWN7Uq4wxBppXnmLEeSAQNARqO/qppn3Ap/M5wtWG/491Tz+u6Zx0HCeyvt+XOFI6MyFq0mPceSMxJhzR0O4Aw7EgAAIgAAJBSwBVgIP20mJiIAACIAACehMI4wg7cQKKtpQntpgrA99xfhFZuFKwrUkVzA0vHqD9HBHojY1ymE+ZovPlfpSiN/3iXBAAAf8hIJV+q7hCuC+df/WdA/S7bRV0+3P7JnX+5XDxojvPn0/fP3ueZs6/iLAQknZhIAACIAACIAACROonDhABARAAARAAARBwi0BoyAyanxZLFZxWJ447dy0vJYbuWV9MD/6nlBq7P9PBGhgZpQc2l9L1XDhkVUGyu81OHC/FSqRoSXFmHOsCYt1vAgxegMA0ICCfSUc56s5XxT4k5fjZD2tpR0UL2agdqMgnx0TQFUuz6dTcRK8rods2LGnE+fz5asLnni0WvAYBEAABEJjGBOAAnMYXH1MHARAAARDQhkAIOwELUmOUKJuWHvedgGlcmfLuixbQr14tU9J2x0cl+oAP76iilp5BuuzELI8F7OXhX1KVJVpRHJYwEACB4CbQx7IEIifQxdXFfWGd/cP07931tPVgE1knERyM5cjni0/IpLWFqUrlcq3HKRIKUrkdBgIgAAIgAAIgcIwAHID4SwABEAABEAABDQhIdcm8FAs72PqocZKKlsfrJpYfVH98XhH94fVKeu9wu+rQZz+qYyfgEF2/MsfjaBbRKRThfamEGc6VNWEgAALBR2CY9URrWOtPPi8mi7jTc9byOfPy3nravL9RpW1q26eZq5xfyJV9zylO060wR0J0GGXEa6MhaDt2vAYBEAABEACBQCYAB2AgXz2MHQRAAARAwO8IzGW9KRNH2dV2uC+2L465m9fmK9UxN+5tUM3tjYpWJcX4v84qIHO4Z1/fnf0j9FF1h6I5mBgdoVQahjNQhRkbIBCQBETnr4ElBERrzzrKFYAMtkGWLPgPO/02svOvb5LK6BH8+SZOvwvY+adnYSL5TJOFDhgIgAAIgAAIgICagGdPEOo2sAUCIAACIAACIGBDIPvT6r2eOAFDOJLwqlNmU7Ilgh5754gqimd/fbdSHEQqBCeybpYnJlFB3QNW5UcqckoBEjgDPSGJc0DAPwi0sc5edXu/oTp/kuIruqcSVVzB1cYPtfbSyCSOR5EdOHN+Kl28JIPizeG6QlN0/1iOAXqnumJG4yAAAiAAAgFKAA7AAL1wGDYIgAAIgIB/E/DGCSgzW1eUpjjmHnqtQpVKV8ORhXf8ez/94OxCkmhDb8yZMzCJHYszo8PxAO0NWJwLAgYQEJ0/ceKLQ19Pk+jCWo4sLGsUZ18PlTf3UFP30JRdijNuVX4yXcr6pbKgYYRlJUSRyCnAQAAEQAAEQAAEHAnAAejIBHtAAARAAARAQBMC3joBT5qdQHdeUES/eKVMJeYvqbz3bDxAt3C68JLsBE3GausMPNzapzxEJ8aEwxmoCV00AgLaEdBb52+MPwxKONq49FOHn0T6SVVyd2x5zky6/KRsyjRQhy8uKszQ/tzhgWNBAARAAARAwB8IwAHoD1cBYwABEAABEAhaAt46AUXL6l6uEPzgf8qojqNwxk0q+4pj8NoVc2ktp9dpaeIMlOqh8lPDqYXFmXG6ifVrOW60BQLBTuAT/s+5v76Lhvj/vx7W3jdMv+Rq5LII4K5JffETZsUrjj9vo5Pd7TvcJEWYYjyulO5ufzgeBEAABEAABAKRAByAgXjVMGYQAAEQAIGAIuCtEzDZEkkb1i+gX28po4MNPRNz58w8evStw9TI4v9fXDaLRD9QaxNdL9H5Ks6IoxDW8oKBAAj4joCk++rl/OtgXb/7Xi6hehermEtRj3x2uhWkWihffvh1dITxjxbysZeXbEF1c9/9WaJnEAABEACBACFg/Ld0gIDBMEEABEAABEBASwLeOgGlaubt586nR3ZU0dtVbaqhScXg5p4hunF1LkWYQlXvabHRNzSqiPznpVi0aA5tgAAIeEiguWfQwzOPf5oU9fjpFM6/ZNYHLUizsMPvmNMvO8FMUuDD1yZpxnFm6P75+jqgfxAAARAAAf8nAAeg/18jjBAEQAAEQCBICHjrBJTKlt9ek8eC+pH0wu46FZX3D7eTVAP93rp5ulTabOkZ5uieAUqPi1L1iw0QAAFjCFhHx0hSdLW2Y86/g1TfqXYuJrEG6LK5iYrDL5+d/1IcyN8sNspEUvgDBgIgAAIgAAIgMDUBOACnZoQjQAAEQAAEQEAzAt46AWdwvtsVS7MpJTaC/vzmYRoVwb5Praqlj+78tELweD/j72nx+2hbP5nDTSRi+zAQAAFjCbSx80/S/rU00fm8b9NBlb6otJ8WG6kUIPJHp9/4/MNCofs3zgK/QQAEQAAEQMAVAiGuHIRjQAAEQAAEQAAEtCMgzjlvo1bWzEuhH55byA45dcpva+8w3fXiAdpb26ndgD9tSXyNFawHOOhmRVDNB4IGQWAaEmjuHtJ01t3i/OO039qOz4oLSQepvLgg1cf92fkn45SiH3pIHkjbMBAAARAAARAIRgJwAAbjVcWcQAAEQAAE/J6AFk5Aqc57z/piEm0uWxtgB92D/ymlbaVNtrs1eS1FQSqaemlM61AkTUaHRkAgOAn0D1upd8iq2eS6B0fopxz5V2Pn/EuxsPPvfP93/onuX7zZ/1KSNbtAaAgEQAAEQAAEdCAAB6AOUNEkCIAACIAACLhCQAsnYCbrX917cbFSgdO2T6VCMKcIP/HeUU4b1DZvUBwRh1p7bbvDaxAAAR0JtHCRH62sh51/97/Mzr/2flWTivOPI/8S7RYUVAf5wYYl0kTZM6H75weXAkMAARAAARAIMAJwAAbYBcNwQQAEQAAEgouAFk5A0eS7g6N2Tpk70wGOVAj+760VNGQddXjPmx1SFKShS5066E17OBcEQMA5gU/Ygd/KBX60sN5Bq6L5d9TO+SdRxPIZkuTnzj/R/cvnKsSihQoDARAAARAAARBwjwAcgO7xwtEgAAIgAAIgoDkBcQJKSps3Fm4KoZvX5tNFSzIcmnn/SDvdu7GEpNqnliZFQaSIAAwEQEA/Ah39IzRs9T6KVyJ379tUQvL/1tak2u+dF8zn6uJqKQHbY3z9Whx/EvW3JDseun++vhjoHwRAAARAIGAJwAEYsJcOAwcBEAABEAgmArMSzZQWF+nVlEI4KubKpbPohtNzKNQuQma8QrB92p83HUpmcWVzj+bRhd6MCeeCQLAR0CL9t4+df/ez5t8RO+dfYjQ7/zjyL9ni3WePXszDTTNoNn82njArgQsnmckUikcXvVijXRAAARAAgeAngG/R4L/GmCEIgAAIgECAEJibFK1JFM5UFYKf+6iW2jRKKZTIpPJGFAUJkD8xDDPACAxbx6jDy8hdcf79bPNBOtzap5q9VPmVar8psf7n/IsICyH5PDwhO4EyODo6NAQpv6qLhw0QAAEQAAEQ8IAAHIAeQMMpIAACIAACIKAXgdzkaBbh9766pVQIvnv9AqcVgv/1YS3d9NTH9AA7Bd471EYjo2NeTedYURC1c8GrBnEyCICAQkC0/7yp4SPVg8X5JxHAtqY4/zjyL9XPnH+R7PjLTRHHX7wSER0Cx5/tZcNrEAABEAABEPCKgMmrs3EyCIAACIAACICApgRE3D4vOYZGx3pYs887fT1JmZMKwb96tYwqmtVVe8WpsKe2S/mJiTDRyvwkWj0vhWaxHqEnJmmK0o63acye9I1zQCBYCXia/iuFQ9473K5UAW/tVWt/JpilaNB8v/q/ag4PJaloLinJKPARrH/NmBcIgAAIgICvCcAB6OsrgP5BAARAAARAwI6ARL0UpFroYEM39XDVTm9svELwn96oorer2pw2JRF8m/c3Kj8SgbiqIIVW5CWSOdy924QjbX1kjgil2Mgwp/1gJwiAgOsE5P9l/7D71buP8v/Dv+08wp8fPQ6dxSvOvyJKj/Ou6JBDwx7ukEUDcfxJRCIMBEAABEAABEBAXwLu3dnrOxa0DgIgAAIgAAIg8CkB0bwqTBMnYA+JI8AbkwrB3zkjn84pTqPXy1roHXYEDow4dyxIqmBVy2F6/N0jdMrcRFozL5kK02NJCoxMZRJVWNHUQ5J+HGEKnepwvA8CIHAcAu5G/3UPjtC/PqihbaXNTtOG46Mk8q9I0dQ7Tre6vyVpvonREYrUQTQ7AGEgAAIgAAIgAALGEMC3rjGc0QsIgAAIgAAIuE1AKl4WpluopL7bo0gg+w7zUiwkP1cvn03vc3rg9rJmKm10jBKS80ZGP6G3KluVH6nCecvafJeihqQoyF5OLc7iqJ401hdDOp/9VcA2CExNYGzsExL9P1fMOjZGW0ua6BnW9uybJGJwPn+OfOP0XJ9p/kVxiq+k90qkH5x+rlxVHAMCIAACIAAC2hOAA1B7pmgRBEAABEAABDQjEGbjBBwc8a5Yx/igIsNC6fSCZOWnoWuAdpS3KD+TaQ4ebeun+14+SBu4qEhSTMR4M5P+trLz8EhrPzV1D9Fs1hRMQHrfpKzwBgg4I9DOlX/l/9FUtre2k/6+8yjVdQ44PTSJCwpdfcpsWjZ3puHO+HGnnxQ1cldOwOlksBMEQAAEQAAEQMArAjNYJHjquwuvusDJIOBIoLa2lrKzs5U3ampqKCsry/Eg7AEBEAABEJggMMgpuwc4EnDYqo0TcKLhT1+McsTRHnYm7OAU4Q+PdtCok9sDiei768Iiije7p9clumNzEqNJHAIwEACBqQmI/udkDnk5u6l7kP7x7lH6gP+vOrNwXjhYvySDLlyUQSIBYJRJMQ+J8oPTzyji6AcEQAAEXCOA52/XOAX7UYgADPYrjPmBAAiAAAgEBQGJ2itiLb4D9V1Keq7WkxLNwRNnJSg/XQMj9FZFK71a0kjNXN133BrZ6XD/5lL6CeuIxUS6fgshjow9A51K+mE2pwZLajMMBEDAOYEh6yjJ/0FnNsApvi/srqNN+xrIyk57Z3ZqbiJdtWwWO+GmjtZ1dr6r+/gjQ4nsi+bCP1LMw8LFf+Dkd5UejgMBEAABEAAB4wm4fvdu/NjQIwiAAAiAAAiAgA0BebiWghwSHeRKeqDNqW69lMrB5y9Kp5UFSXTPSyWq9MKa9n568JVS+tG589162JeAwsauQWpjXbOsBDM7AyMMT0l0CwIOBgEfEZDiH04CcKmZHfB3byyh9r5hpyObw1qd15w6R/mMcHqAFzulBlAUL0KIfp+Fnf/y28zbUrEcBgIgAAIgAAIgEBgE4AAMjOuEUYIACIAACICAQkAibeZxdeBSrg4sabt6WixH9PzovPl090sHVJGAlc299MtXy+i2cwrdTi+U4iKHW/uUFEYpLuJuOrGe80XbIOAPBCar/vv77ZVOnX/ikLtiaTatKUjRzCEXbppBshAgjj7lJ9xEEiUMAwEQAAEQAAEQCFwCyMEJ3GuHkYMACIAACExTAuKYm5dqISOex0XP68fsBJTftlbCUYi/3VrOaYieaRL2cyrjQXZiljZ2k6Q1wkAABIi6B0fIWbGfyuYeqmDHu62FcljeecVp9JsvLKG1hamaOf/CQmfQgow4pWJ4elwUyecNnH+25PEaBEAABEAABAKTAByAgXndMGoQAAEQAIFpTiCOC2sszIpT0vH0RpHCxT8k5VcijWzt45pO+sP2KhrzIhKxo4/1Abn4iKQWe9OO7bjwGgQClUAzV852Zpv3N6p2i0P+wcsW0Zc55Vci9LQyWVQo4Ahj0RyFgQAIgAAIgAAIBBcBOACD63piNiAAAiAAAtOIgJnT8oozJVInhlNx9U3Py+TiHbezE1CqfNrazkNt9Ohbh1izzPN0ZDm1tmOA9tZ1KRFQtu3jNQhMFwKS0u9M30/2vXeoXYXh7AVpJP8ntbZc/iyRiD8YCIAACIAACIBA8BGAAzD4rilmBAIgAAIgMM0IJFsiaEl2AmXER3JhDf0mPzcpmn5wdiFFmNS3D9vLWujxd4965QSUUUsq8IG6bjrU0qu7vqF+lNAyCHhGoK1vyOnf/ZaSJhq1cbCHcxXtM+aleNbJcc6axZqcSTpXDj5O93gLBEAABEAABEBAZwLqO3idO0PzIAACIAACIAAC+hAQja7ZidG0iNOCRbxfL5MCJN89q4BMdgKEkqL47Ee1mnTbxGmQuzm9uGOSaqeadIJGQMDPCDhL/x22jtG20ibVSFfmJ1GMXTq+6gAPNqQqd2a89hGFHgwFp4AACIAACIAACOhEAA5AncCiWRAAARAAARDwBQFJCy7KiKX8VEkL1udrflFWPN28Nt+hCMmzH9XRy3sbNJm2OD5KG7nwQVMPyWsYCAQzgcGRUeoZtDpM8e2q/9/efcDJVdX9H/+lN9J7SA+pJAGkSA9BIARESOgovSs+yAukqBT9IxApKqi0J4D40HsVQpOOdEmAFBLSQ3rvm8z/fA/c8c7s9J3ZKfs5uszMveeee+77TGbv/uaUJdW2a/hvPlMbN5+oeveSEEAAAQQQQKCyBQrzl0Flm3F1CCCAAAIIlLyAhvLt2KON79UT11kvL3XftXc7O2dEP4sfcfx//55VrcdSTU64ZM0m+8wtErJo9YaaFMOxCJS0wOLV1Rf/0LyaL8Yt/qE5P3u0a563a2nRpIENcCuK1yvk3AF5qy0FIYAAAggggEBNBAgA1kSPYxFAAAEEEChhAQ0L1rxe6rGnXj75Tvv072in7tWnWrHj3/za3vpqSbXtuW7YvCVi0xettS8XrDL1lCIhUEkCCvQtShAA/NL1gJ3lVscOp9F57P2nHsIa0q/PCRICCCCAAAIIVL4AAcDKb2OuEAEEEECgjgs0cyv3Du7ayv+xrx4/+UwHDulsx+/WM6ZIrQf819e+sj+9PNWWrKnesykmcxYvVqzb7HoDrrQFK9fXeMGRLE5LVgQKKrBy/eaEw9xfmBQ7nL5Lq6a2Y882ealLwwb1bJAL/jVpmN/Pg7xUjkIQQAABBBBAoCACDQtSKoUigAACCCCAQMkJtGvR2PSjgMM3KzfY8nWbXCCt5tX80Q7d/Aq+T346L6awf3+9zD6ZvcKO2GlbO3RY17zMSbhla8RmLllny9wCIUNcUJOhizHkvChDgUTDfxe7Ie8fzloeczWjtu/s5t2seW89FdG/0zbWogl/BsQA8wIBBBBAAIEKF6AHYIU3MJeHAAIIIIBAvIBWCdbQP80R2LV1U1NvoJqmY3bpbgcnGJ64actWe/jDOfbLR/9jH8cFNGpyzlXrq2zOsvU1KYJjESi6QJX796Fgdnx68fOFMcH5Zo0a2L4DOsZny+l1X7fgR5vmjXM6loMQQAABBBBAoHwFCACWb9tRcwQQQAABBGok0NQFFXq7YMD3erb1q4A2bZT7bYF64p20Ry87Y+8+tk2CnkWa4+z6CVNs3AuT/RDeGlX8u4Pnu6HAqzZszkdRlIFAUQT078J1ao1JmufytSmLYraNGNjRtMJ3TdO2bZpZJzeUmIQAAggggAACdU8g9zv9umfFFSOAAAIIIFCRAloEoIvrCbiTCwRqXrBcFwxREPAHgzvbTcfsYAcM7lRthWDhfTpnhV386Gf24Aeza7ygh4YvT1+0xjQsmIRAuQloKP6cuEU+dA1vTFts6zb9d7Eb9c8dNaRLjS+vwzaN/aJANS6IAhBAAAEEEECgLAUIAJZls1FpBBBAAAEECiPQ1s0RqAVDNDy4c6smOa0Q2rJpIzt97772+zHDbEDnbapVtMoF7J76dL5d+Mh/7N3pS2q0oMeGzVtt5tK11c7BBgRKWUC9/KYtXF2t999WF9V+cdI3MVXfyS38oQB9TVLLpg2tX8fq/xZrUibHIoAAAggggEB5CRAALK/2orYIIIAAAgjUioBWDu7rAgYKBCYa0ptJJfq44cVXHba9/XS/ftbGzTsYnzT32c2vfmVXP/dlwp5Q8fmTvV60aqMtTzCPWrL8bEegmAKa92/KN6tt85bqPVe1yvV8t0BPOB08tGv4ZdbP1aNXc37Wdz19SQgggAACCCBQdwUIANbdtufKEUAAAQQQSCvQuGF9G9KtlV89OG3mBBk0LHif/h3tRjcsWCsBN9ASpHHpiwWr7NLHP7N/vDcr52HBM5ascQGVrXEl8xKB0hKIuB5+09yw9fAQ33ANX/w8tvdf97bNbKj795dLauLm9FTgTz16GzXglj8XQ45BAAEEEECgkgS4G6ik1uRaEEAAAQQQKICA5gjUUF6tGJxr0gIGP9m9l407crgN3bZ1tWI0jd/zExfYRW5Y8IezllXbn27DpqqIzVjMUOB0TuwvrsCspetsxbrEC9fMX7Hez5EZrqFW1lYQPZukjn4KHO7YvU3OgftszkdeBBBAAAEEECgPAQKA5dFO1BIBBBBAAIGiCigIoRWDNaw3y3hETL23dYGJX40eZL84oL9pUYL4tNQN5b1xwlT3M8WWrtkYvzvlaw0pXrQ6dvhkygPYiUAtCixatcGtgJ38/Rnf+09D7/fu3yGrGrZ3/6Z2cMP2e7RrzpDfrOTIjAACCCCAQOULNKz8S+QKEUAAAQQQQCBfAlqMoIkbFqxhjLmuvqtg4vf7tPfzCz7tFgN55rP51eZD+3DWcps0f6Uds0sPvwJqpvOXqYdVK7cISdNGDfJ1yZSDQI0FtOLvjCXJe6iu3Vhlr09dHHOe/Qd1cv/WMnsfa87OPu1bWGs33x8JAQQQQAABBBBIJEAPwEQqRdr24Ycf2u9+9zs76KCDrHv37takSRPbZpttbMCAAXbqqafaW2+9lZeaXXXVVX44if4AS/fzr3/9Ky/npBAEEEAAgcoR0ErBmhewccPshibGCyi4cbQL8I0bO9y2TzDPmVb4vffdWfabpya54b1r4g9P+LrKLawwPcO8CQtgIwJ5FghW/HXT/yVN/5qy2DZW/XcOSw3jPXBI56T5gx0NG9SzXu2b2w7dWxP8C1B4RAABBBBAAIGEAvQATMhS+xv33Xdfe/PNN6udeNOmTTZt2jT/c88999hJJ51kd955pzVuXH3YVLWD2YAAAggggECBBDQ8cfturf1qpskWNMj01F3bNLNfHzLY3vpqiV8IZPWGqphDv3Y9pxQEHOXmQztm5x6m3k6p0qr1Vab51Lq5ckkIFFNAK/5OTrLib1CvrW4CzPjhv7v2bueGyDcJsiR87NiyifV0Q321UA8JAQQQQAABBBBIJ0AAMJ1QLe2fP3++P1O3bt3s6KOPtn322cd69uxpW7ZssXfffdduvPFGmzdvnt177722efNmu//++/NSs4kTJ6Ysp0+fPin3sxMBBBBAoO4KaJiteu5NXbjGNMSxJkk90rVa8E492tr978+216YsiilOvademPSNvf/1Mjtlj962S++2KRdHmLNsnbVxwyG1+AgJgWIIBCv+rt+0JeXpP5q93BbHzXd58NAuSY9R8L13h+bW0g11JyGAAAIIIIAAApkKcFecqVSB8w0aNMiuueYaO/LII61Bg9ieDbvvvrudeOKJttdee9nUqVPtgQcesHPOOcfUa7CmaejQoTUtguMRQAABBOqwQMMG9W1w15Z+frNFq7JbtCMR2zZNG9pZ+/a1fd3iB//71tc2z/XkCyct9HHTy1Nt515t7ZQ9eyftJaVVhb9y8xQOdb0UM50/MHweniNQU4GZKVb8DZetwHY4aaGdgZ1bhjdFnzdyQ341/F4rc5MQQAABBBBAAIFsBBgzkI1WAfM+++yzdswxx1QL/gWn7NChg+8FGLx+9NFHg6c8IoAAAgggUFQB9d7r13Eb6+nmIstXGtS1lV03dpgd6+YIVNAjPn3kFgm56JH/2GdzV8Tvir5eu3GLzV0eG0CM7uQJAgUUWOhW/P0mxYq/walnLV1rXyxYFbz0jwe7oe76N5UoaREegn+JZNiGAAIIIIAAAukECACmEyqh/SNHjozWZvr06dHnPEEAAQQQQKAUBLZ1c+7177yN5atzknoXHrHTtnb9UTvY8G1bV7tELZpw00tTTXMEJkvzV663VRtqNjw5WdlsRyCRwMp1m1O+J8PHxM/917pZI9ujX/twluhzBcK7tmZeyygITxBAAAEEEEAgKwECgFlxFTfzxo3/HVoVP0y4uDXj7AgggAACCHwroIULhrkVSVu6obz5Sp1bNbVLRw+y80ZuZ61cgCScFAS8/sXJtjRuDrUgj+YOnO6GAm/RmGASAgUW0Iq/UxettlQr/gZVUGBaC9+E0wGDO7ker4lvz7VYDr3/wlo8RwABBBBAAIFsBBLfYWRTAnlrTeD111+Pnmvw4MHR5zV5ctBBB1mnTp38qsJ63G+//ey6666z5cuX16RYjkUAAQQQqMMCWnhjqOux169Ti4TDd3Oh0ZDIvbZz02EcvYPt5lZIDaflrsfVuBen2LpNsasHB3k2bN5qM91QSxIChRLY7Fb7XbOxyq/4W7Uls2Dzq18uss2hvAruHTC4c8IqqvdfFxcIJyGAAAIIIIAAArkK5O/r+VxrwHEZCWzdutUH5oLMmi8wH+mll16KFrN48WJTkFE/48aNs3vuuccOP/zw6P5snsydOzdl9gULFqTcz04EEEAAgfIX6NSyqbVt3ti0Iu/CPCwQIhGtgHre/tvZNc9/6YMtgZLO8eeXp9kvDx5oDetX/35TC5S0cb0H27seiiQEMhXY6nqObnLBPf/jeptucj8K9ulRvU+1fbN7zLaDaZW7r3vpy4Ux1dizb3u3cnXjmG3BC3r/BRI8IoAAAggggECuAgQAc5Wr5eP++Mc/2vvvv+/POnbsWNt5551rVINhw4bZEUccYbvttpt169bNNm/ebFOmTLH77rvPJkyYYCtWrPArEj/zzDM2evTorM/Vo0ePrI/hAAQQQACByhPQcMa+boGQji2b+HnRtDBHTZPKvPDAgXbl05Nsfmihhc/mrbS73pppZ+7TJ+EiCtPcUGD1uNJCCiQEwgIbq7bY+k3uxw3hXadH96PhvOEeeuH8NXmuoOLf35lpWtE6nEYN7RJ+GX1O778oBU8QQAABBBBAoAYC9SIu1eB4Dq0FAfXIO+CAA6yqqsoP1504caJ/zPXUCu61adMm6eG33367nXPOOX6/goNacKRp0+z+WEq2el2ik86ZM8e6d++eaBfbEEAAAQQqSEC3HOoJOGf5Ost0mGSqy9dKq1c8Nckt8hE79PfYXXvYETtum/TQzq2aWJ8OLRIGCZMexI6KEAgCfT7I5wJ8QdAvH+/HTIDUe/Bv//rK3puxLCb7ALd4zm9/NDRmW/Cil1tdu5ub/4+EAAIIIIBArgIaoRd00uHv71wVy/84egCWeBt+/vnnNmbMGB/8UxDukUceqVHwT5ebKvin/WeffbZ98MEHNn78eJs/f7499thj9uMf/1i7Mk76UEmVNARYvQ9JCCCAAAJ1R0BfDqn3XbsWjW2Wm5NvyZrYHlDZSmhxkF+OGmi/e/aLmJ5aD30wxzq6ob6aMzBRUhBSPb0GdG6ZdMGFRMexrXwFpi9e43vc1VagL5GUehTe6FatnuR6qoZTPffimF0Sj5xo3LCe6X1OQgABBBBAAAEEaipQfZKcmpbI8XkT+Prrr02LdGhBDq36++CDD9q+++6bt/JTFaQgYJDCi48E29I9qkdfqp+uXbumK4L9CCCAAAIVKtC4YX3r74JvQ7q2smaNG9ToKrfr1NKtDtzfFEQJp9ten+7mCFwV3hTzfNX6KpvoAjHJFg6JycyLshZYsW6TaQ7IYgb/tOLv1c99US34p4U/fr5/f9u+W+uExl1bs/JvQhg2IoAAAggggEDWAgQAsyarnQPU807DfvWoHhN33XVXzgty5FLjIUOGRA+bN29e9DlPEEAAAQQQyJdA6+aNbLhbLbhHu2bm4iA5p936tLMff79XzPFVbp61GydMtQUr1sdsD7/Y6FYHnjRvVbW52MJ5eF7eAhp2PnPpuqJexJI1G+23T39u0xfHrkTdxAXCLzl4kO3Rr33C+qn3Hyv/JqRhIwIIIIAAAgjkIEAAMAe0Qh+yZMkSO/DAA23GjBn+VLfccouddNJJhT5tTPnZzOEXcyAvEEAAAQQQyEKgvov8dW/b3Hbo0cYPDc7i0JishwzrYgcN6Ryzbc3GKhv34mRbtX5zzPbwiy0uUDh14WqblyJQGM7P8/ISWOAWidE8f8VK85avd4vVfB6zWI3q0rJpQ7v8h0NsmAuAJ0ua90//PkgIIIAAAggggEA+BAgA5kMxj2WsXLnSRo0aZV988YUv9brrrrOf/exneTxDZkUF51duLQRCQgABBBBAoJACTRs1sIFdWtog99O0Ufa3J/ri6qQ9etv3esYucqX5/m6YMMU2VW1NWn0thzbb9RL7atFq0wqtpMoQUJsXM7Cr99NVz3xerYdpezcH5pWHbW/93OrYyZKf+68lc/8l82E7AggggAACCGQvkP0ddvbn4IgMBdatW2eHHnqoffzxx/6IX//613bJJZdkeHR+s2kl4CCNGDEieMojAggggAACBRVo64IjO3Rv43oFZj8sOJhPTSv8htO0RWv8yqtbFelLkRav3mSfz19lWimWVP4C+VptOheJ/8xZ4eb8+9LUCzWctnW9+n77o+1Nj6kSvf9S6bAPAQQQQAABBHIRIACYi1oBjtm0aZNf7fftt9/2pZ9//vl29dVXZ32me+65x88ZqJ4QV111VbXjJ06caF999VW17eENd9xxh/3v//6v39SlSxdfr/B+niOAAAIIIFBIAQ177NHu22HBbVs0yupU6kmolYHVyyqc/v31Mnvg/dnhTQmfK2CjeQHjAzcJM7OxZAXUfotXbyxK/d6ZvsSud71ON8b1Ou3faRvX82+ItXcrVKdKWiSnM73/UhGxDwEEEEAAAQRyEGiYwzEcUgCB448/3iZMmOBL3n///e3000+3SZMmJT1T48aNbcCAAUn3J9vx0Ucf2RlnnGEjR4600aNH27Bhw6x9+/ZWVVVlkydPtvvuuy9aD608rGBgixaxPSmSlc12BBBAAAEE8imgYN6gLq1s+dpN9vXStaZFOzJJbZs39osraO619Zv/25vv2c8WWKeWTezAIV1SFqOho5+7FYL7uYBNhzTBmpQFsbNoAjOXrLU0HT4LUrcJn39j97wz0+L7mg7v3touOGCAG96eftVr9Q5k7r+CNA+FIoAAAgggUKcFCACWSPM//vjj0Zq8+uqrNnz48OjrRE969eplM2fOTLQr7bYtW7bYyy+/7H+SZVZQcPz48XbYYYcly8J2BBBAAAEEakVAw4JbN2vk53Ob7xbryGSaPvUgvODAATbun5NtSygSdNfbM22uW5jh+N16pgzG6BzTFq4xna+e/ufWYtBPffef6KO7+m+3K0fsPgVwtH6D8n/7E3ruxl9Et7vnDevXNw1fJuVHQD3/Vm+IHXqbn5KTl6K5Ix//ZK499vG8apn2dKv8njuinzVs4Bo7TVLvPwWpSQgggAACCCCAQL4FCADmW7TEyzvkkEN8YO/dd9+1Tz75xBYuXGhLly5135JHrF27drbDDjvYwQcfbKeccoq1atWqxK+G6iGAAAII1BWBYFhwRxcc+dr17lqxLvnKvoGJVlg9Y58+dvsbM4JN/nHCFwvtk9kr7Kx9+9rQFKuwKvPajf/tQRhTSB5fNGpQz4Z0a2XNG3NbVlNWreo8e9m6nIvRisEr1m2y5W7l6NUbNts61/7r3LZ1m6psrR7d0GL/GHqt/eGepuGTj9q+i1ucppcP+Ia3J3tO779kMmxHAAEEEEAAgZoK1HOBn/hRCjUtk+MRSCswd+5c69Gjh883Z84c6969e9pjyIAAAggggEAgsHTNRpvhAoFVW9Lfxjzy4RzXO6t6zyyV9YNBneyE7/csevBNq74O6dramjVOP0Q0MOCxusAcF/xTD8/4pDkBNZR8hQvs+QCfCyDrUYHk5aHH+Hn74svJ5vXRO3e3MTtt63qJZta7U73/durRhuG/2SCTFwEEEEAgIwH+/s6IqeIz8VVzxTcxF4gAAggggEDlCWghBQXLJn+zOu3cgEe5QEzLpg3dIiBzbNOW2HkEX5m8yD5xK7aeuU9f29EFX4qVNlVF7IsFq2x71xMwk3niilXPUj7vBjffo4Zsh9MyF/S7+ZVpNmXh6vDmgj5XuO/Uvfq4uSY7Z3Uev/I1Q8GzMiMzAggggAACCGQukH4ykszLIicCCCCAAAIIIFBrAhoyO7Rba9umServM9UD6+ChXW3ckcPdoiItq9VPQaJxL0y2216fXtTVf7X4iIKACmSqqKEOAABAAElEQVSRsheYtXRdzPyQ6vV33T+/rNXgn96L5x/QP+vgX5NG9a0jC85k3+gcgQACCCCAAAIZC6S+Y864GDIigAACCCCAAAK1L6Bhk5o/76tFa0yBvFSpS+umdvkPh9jLbg7A+9+fbfHDPV+futj+M3eFnbF3X9u5V9tURRVsn1Y6/tIFAXVNTRoyHDhT6JVuKG+4/TdWbbEbXpxicxIMB86kzGZutd5WzRpaCxdkbu6Cei1cb1MFnFs0+e5Rr9325u7R5/H7G1gbtwJ1Lgu6MPdfJq1CHgQQQAABBBCoiQABwJrocSwCCCCAAAIIFF1AAZcBnbcx9QBbsHJDyvpo9d2D3MIMGu5755szbNL8VTH5NSfcDROm2F5u5daT9+zthg43itlfGy82+CDgajcnYCtTgJOUWkDTWc9cujaaqWrr1qTDftVDr23zRj5Q18Y9tnUBOz22adY4ZnttDsNW7z9W/o02H08QQAABBBBAoEACBAALBEuxCCCAAAIIIFB7Ahrm27tDC1MwRYHAdEucdWrV1H51yGB7dcoiu++92dVWcX17+lKb6IKDJ7sVXPt13Mat4mp+JVetRqwgYvS1nrsYXQO/rZ5b8MEyXvQhlY5Wow16AjZqQBAwldU3qzb4lXqVR8HAO92qzx+7VZ7DSUG/3/5oe+vYsml4c0k8796mWV7eMyVxMVQCAQQQQAABBEpWgABgyTYNFUMAAQQQQACBbAW6tm7mh85qSPCWralXCFbQ8AeDOtuO3b/tDfifuStjTrfKrRh7y6tfxWzL5IWCg+qV2NBFBvUY/DQMPf92/7f7NNRXPRK1aEQ42LfOBQEnL1htg7u2tIYEARPSb3aLuoRX/dXQ7jemLYnJq+G7l40eXJLBPz/3X8smMfXlBQIIIIAAAgggUAgBAoCFUKVMBBBAAAEEECiaQLsWjf0celO+WWVaXTdd0orClxw8yAWOFts/3p1la13grSZJccetWyK2eUvm5Uyct9Jec70Rzx3Rz/q6HodB0kIWWul4sBsOrKAhKVZgzrJ1VuWslZ75z3x79rMFMRkau8DpL0cNsh7tmsdsL4UXmk9QvUsViCYhgAACCCCAAAKFFmBMSaGFKR8BBBBAAAEEal1Ac71t71YI1iINmSQFYUYM6GR/OGoH+17P4iwAop5slz81yR75aI4Lam2NVnv1hio/HDhdj8boAXXkyVoXHF20eqO/2tenLvILu4QvXfFSrcg7MMHKz+F8tf1cgdxe7ZvbsG1bu0VF+C6+tv05HwIIIIAAAnVVgABgXW15rhsBBBBAAIEKF9BCDtu71XRbN8t8IQ/1HrzooAF23sjtTCuz1nZS78HHP57nA4GzXe+2ICkIOMX1BNyaZlhzkL8uPH69ZK2f6/GjWcvtDjfvX3w6e99+RQvmxtcleK334vDura0b8/4FJDwigAACCCCAQC0J8LVjLUFzGgQQQAABBBCofQHNnac59Ga4YNGiVd/2FktXC/UG3Gu7Dv5HPfH8kF63uIR64G11j8FrBeMSPd/yXV7lr3I/etziVqb97/Nvt+m1hq++OnmhW8X2v8E+1U+vf/XERDtq5+522PBufvjvSjcn4ZSFq21g55Zu4ZG6PWx0yZqNpqDoZDfM+8+vTPXtEG7XH3+/p+07oGN4U1GfN2qgXn8t3DyEzPdX1Ibg5AgggAACCNRhAQKAdbjxuXQEEEAAAQTqgoACepprrUXjhjZvxbqM5gUMXGpj8Y2Rgzrak5/Msyfcj4sJRpMChw99MMfUw+0cNzegeiSuWLfZprkFTgZ0rrtzx8lFKz2rh+QNL05xcy2G0JzeYcO72g9d0LRUUseWjX3wL7zAS6nUjXoggAACCCCAQN0RIABYd9qaK0UAAQQQQKBOC3Rp3dT3wFq0eoPNX7E+q0BgIeG0WvBRO/fww1VvfX16zKq2Oq9WNL7s8c/suF172sFDu9iytZvsg5nL3eIRhaxV6ZbtOljaNyvX27X//LLagi0jXK+/43frWRKV1wq/fTu0sDbNG5dEfagEAggggAACCNRtAQKAdbv9uXoEEEAAAQTqlIAWYOjaupl1btnUFvpA4AYXCPzvghvFxNDqv9eMGWaPfjTXnvlsvp/fLqiPern9471ZLvC3zPcG7NyqabCrzj1qKPQ1z0/2vSHDF79zr7Z25j59i76qrgKzXV2wuXvb5qzcHG4gniOAAAIIIIBAUQUIABaVn5MjgAACCCCAQDEENIdeEAjUSrLzfI/A4gcCNUxUPdgUzLrN9QZcsHJDDM9ktxDIJY99Zie4PEPdKrJ1LWn4r3pJfrMq1kXzIv7P/v2LHnDT6tN9OrYwPZIQQAABBBBAAIFSEuDupJRag7oggAACCCCAQK0KKBCoocGd3OIMi93CEgoEbtxc/EDgABfQunbsMD8H4D8nfRNjstH1WLz7nZkx2+ryix7tmttFowZa44b1kzJoOG4jN9RavfPqu/+4p1bP/U9rqWiOSD367e658uhHr/3z7/K5B3/Mt9v+e5zK8f93Dy1d4E/lkRBAAAEEEEAAgVITIABYai1CfRBAAAEEEECg1gUUCNSwWh8IdD0C55ZAILBJwwZ20h69bZfe7ex21+tNPRVJsQIdt2lilx48KGmPu1bNGvrFU5iHL9aNVwgggAACCCBQ9wQIANa9NueKEUAAAQQQQCCJgHpvdXKBwI7f9QhcvnazRdz/MklanCJZSrQvKFf71mysipnzL1zOkK6tbNyRw+2+f8+yl79cFN5Vp5+3atrQLjtkkLVrUX2RDW3r1qaptWzaqE4bcfEIIIAAAggggEAgQAAwkOARAQQQQAABBBD4TsAHAt1CIZ3cT22kzVu22vJ1m0wBxxXu0U11F5OaNmpgp+/d13Z1vQEf/nCOTV+8NmZ/XXvRyw37PXe/fn4ex+DaNfK2g+sRuG2bZtascYNgM48IIIAAAggggAACToAAIG8DBBBAAAEEEECgyAJa/EPBRv1ooQsFAZet3WQr3Iq3VW4F4CAN797G9LM1PkIYZKgjjxqyHSSt7Kyh25rLUYFSEgIIIIAAAggggEB1AQKA1U3YggACCCCAAAIIFE1AAa32riebfhToW7Vhsy1VMNAFBTdVfRsMDAfAilbRIp+4YQO3gIsbrq3AnwKoJAQQQAABBBBAAIHkAgQAk9uwBwEEEEAAAQQQKKqAAn1awEI/ETdZ4KoNVW6Y8CZb7R7ratJQX83xp0VbFCwlIYAAAggggAACCKQXIACY3ogcCCCAAAIIIIBA0QU0L2HrZo38T9ErQwUQQAABBBBAAAEEykqA8RJl1VxUFgEEEEAAAQQQQAABBBBAAAEEEEAAgewECABm50VuBBBAAAEEEEAAAQQQQAABBBBAAAEEykqAAGBZNReVRQABBBBAAAEEEEAAAQQQQAABBBBAIDsBAoDZeZEbAQQQQAABBBBAAAEEEEAAAQQQQACBshIgAFhWzUVlEUAAAQQQQAABBBBAAAEEEEAAAQQQyE6AAGB2XuRGAAEEEEAAAQQQQAABBBBAAAEEEECgrAQIAJZVc1FZBBBAAAEEEEAAAQQQQAABBBBAAAEEshMgAJidF7kRQAABBBBAAAEEEEAAAQQQQAABBBAoKwECgGXVXFQWAQQQQAABBBBAAAEEEEAAAQQQQACB7AQIAGbnRW4EEEAAAQQQQAABBBBAAAEEEEAAAQTKSoAAYFk1F5VFAAEEEEAAAQQQQAABBBBAAAEEEEAgOwECgNl5kRsBBBBAAAEEEEAAAQQQQAABBBBAAIGyEiAAWFbNRWURQAABBBBAAAEEEEAAAQQQQAABBBDIToAAYHZe5EYAAQQQQAABBBBAAAEEEEAAAQQQQKCsBAgAllVzUVkEEEAAAQQQQAABBBBAAAEEEEAAAQSyEyAAmJ0XuRFAAAEEEEAAAQQQQAABBBBAAAEEECgrAQKAZdVcVBYBBBBAAAEEEEAAAQQQQAABBBBAAIHsBAgAZudFbgQQQAABBBBAAAEEEEAAAQQQQAABBMpKgABgWTUXlUUAAQQQQAABBBBAAAEEEEAAAQQQQCA7AQKA2XmRGwEEEEAAAQQQQAABBBBAAAEEEEAAgbISIABYVs1FZRFAAAEEEEAAAQQQQAABBBBAAAEEEMhOgABgdl7kRgABBBBAAAEEEEAAAQQQQAABBBBAoKwECACWVXNRWQQQQAABBBBAAAEEEEAAAQQQQAABBLITIACYnRe5EUAAAQQQQAABBBBAAAEEEEAAAQQQKCsBAoBl1VxUFgEEEEAAAQQQQAABBBBAAAEEEEAAgewECABm50VuBBBAAAEEEEAAAQQQQAABBBBAAAEEykqAAGBZNReVRQABBBBAAAEEEEAAAQQQQAABBBBAIDsBAoDZeZEbAQQQQAABBBBAAAEEEEAAAQQQQACBshIgAFhWzUVlEUAAAQQQQAABBBBAAAEEEEAAAQQQyE6AAGB2XuRGAAEEEEAAAQQQQAABBBBAAAEEEECgrAQallVtqWzFCFRVVUWvZcGCBdHnPEEAAQQQQAABBBBAAAEEEEAAgfwJhP/mDv8tnr8zUFI5CBAALIdWqsA6Ll68OHpVu+22W/Q5TxBAAAEEEEAAAQQQQAABBBBAoDAC+lu8d+/ehSmcUktagCHAJd08VA4BBBBAAAEEEEAAAQQQQAABBBBAAIGaCdSLuFSzIjgagewFNmzYYBMnTvQHduzY0Ro2pDNq9oqldYS6lQe9Od9//33r2rVraVWQ2hREgHYvCGtZFErbl0Uz5b2StHveScuiQNq9LJqpIJWk7QvCWvKF0u4l30RZV1DDfoNReMOGDbOmTZtmXQYHlL8AUZfyb8OyvAJ94Oy6665lWXcqnV5Awb/u3bunz0iOihKg3SuqObO6GNo+K66KyUy7V0xTZnUhtHtWXBWVmbavqObM+GJo94ypSj4jw35LvokKXkGGABecmBMggAACCCCAAAIIIIAAAggggAACCCBQPAECgMWz58wIIIAAAggggAACCCCAAAIIIIAAAggUXIAAYMGJOQECCCCAAAIIIIAAAggggAACCCCAAALFEyAAWDx7zowAAggggAACCCCAAAIIIIAAAggggEDBBQgAFpyYEyCAAAIIIIAAAggggAACCCCAAAIIIFA8AQKAxbPnzAgggAACCCCAAAIIIIAAAggggAACCBRcgABgwYk5AQIIIIAAAggggAACCCCAAAIIIIAAAsUTqBdxqXin58wIIIAAAggggAACCCCAAAIIIIAAAgggUEgBegAWUpeyEUAAAQQQQAABBBBAAAEEEEAAAQQQKLIAAcAiNwCnRwABBBBAAAEEEEAAAQQQQAABBBBAoJACBAALqUvZCCCAAAIIIIAAAggggAACCCCAAAIIFFmAAGCRG4DTI4AAAggggAACCCCAAAIIIIAAAgggUEgBAoCF1KVsBBBAAAEEEEAAAQQQQAABBBBAAAEEiixAALDIDcDpEUAAAQQQQAABBBBAAAEEEEAAAQQQKKQAAcBC6lI2AggggAACCCCAAAIIIIAAAggggAACRRYgAFjkBuD0CCCAAAIIIIAAAggggAACCCCAAAIIFFKAAGAhdSkbAQQQQAABBBBAAAEEEEAAAQQQQACBIgsQACxyA3B6BBBAAAEEEEAAAQQQQAABBBBAAAEECilAALCQupSNQBEFnnvuObvqqqvs0EMPtcGDB1uHDh2sUaNG1rZtW9t5553twgsvtClTpmRcw1mzZvljBg0aZC1atLB27drZrrvuatdff72tW7cu43Leeecd+8lPfmK9evWypk2bWpcuXWzUqFH2wAMPZFyGMir/QQcd5I9XOSpP5b777rsZl6N6/+EPf/DXoevRden6ZKPrLcc0c+ZMu+WWW+zII4+0/v37W/Pmzb1z9+7d7YgjjrAHH3zQqqqqMr60SZMm2dlnn239+vWzZs2aWceOHW2fffax2267Laty/vnPf9qYMWNM9WjSpIl/1GttzzSp3jqvzq96qD6ql+r3+eefZ1qMLVmyxK644gobPny4tWrVyv/oubYtXbo043JKLeOaNWvsjTfesBtuuMGOOeYY69Onj9WrV8//9O7dO+vq0vZZk1X8Afn6PVDxUDle4KJFi+zZZ5/1n0WjR4/2v7eDf8OnnHJK1qVW4uduvj6XssYs4AEffvih/e53v/P3NMHvyG222cYGDBhgp556qr311ltZnZ12z4qraJlXrVrl78l0zzlixAjbbrvtrHXr1ta4cWPr1KmT7bfffv4eNdP7kkq8v+Z3TtHenpy4kgUiJAQQqDiBzZs3R9znVtofFxCMXHvttWmv/+mnn464QEnS8txNamTatGlpy7nyyisj9evXT1qOC1ZG1q9fn7IcF7SLHHLIIUnLUPku8JmyDO1UfV2ALGk5ut5nnnkmbTmllOE3v/lNxP2xmPSagveEC9xG3E1V2qrfcccdEXcjmrS83XbbLbJ48eKU5WzZsiVy+umnJy1DdTrjjDMiypcq6Tyqd3AN8Y8uqBi58847UxXh97333nsRF3ROWk7Xrl0j//73v9OWU4oZ3B8LSa/LBcizqjJtnxVXncicr98DdQIrx4uM/1wLvz755JMzLrVSP3fz8bmUMWItZXRfaCX93A63/0knnRTZuHFjylrR7il5Sm7nSy+9lFHbuy/wIy+88ELK+lfi/TW/c1I2OTsRyFnAcj6SAxFAoGQFFAB03yJGDj/88Mg111wTcb2+Iq+//nrkgw8+iDz11FORCy64wO8Pbi5vvfXWpNfy8ccfR1xPK3+T4r6Rjvz+97+PuG8ZI6+88krkzDPPjN68KAjovs1MWo7ruRXN63ptRcaPHx95//33I08++WRk5MiR0X3HH3980jK047jjjovm1XE6XuWoPJUbXNPtt9+etBzVU/UN8uo6dD26Ll2frlP7XO+5yCeffJK0nFLbEQTaXE/GiOsNGbn77rsjrudAxPUuiPzjH/+ICaAp+Ll69eqkl+B6kEaDtZ07d47cfPPNPjDmehZExo4dG7Xbe++9I65nXtJyLr300mjenXbaKeJ6bvr20qNeB21w2WWXJS1D5es8QV6dX/VQoE71ct+U+30K/j7//PNJy5k9e3bE9Rz0eRs2bBi5+OKLI67HnP/Rc23TOVTenDlzkpZTqjtcD4KokevRGnE9ZKPv5WwCgLR9+bV9od+T+fo9UOh6lnv5wWecHnv27On/DQfbsgkAVuLnbr4+l0rtPRLct3Tr1i1y/vnnRx599FH/O9KNZojcdNNNkW233Tb6uZ7u/oh2T34vUmrtrvooANijR4+Igrt//vOfI48//nhE7f72229HHnroocjRRx8dadCggW9/fRn76aefJryMSry/5ndOwqZmIwJ5ESAAmBdGCkGg9ARSBWVU2xkzZkTccGB/Y6GgSLL8wbfTCo4oQBaf3BDa6M2pvoFMlNzwhWjAUX/UxPca07kPO+ywaDmvvfZaomJ8kC74Y0j54+usclW+8rRp0yaybNmyhOVcfvnl0XOp/vFJN19BMEhBlXJJCmKNGzcuaSBWXm5oaPTaf/vb3ya8tE2bNkX69u3r86kn5FdffVUt309/+tNoOXe7QGOi5IaYRx132WWXiHpvhtPatWsj2q72kneyXqQK7gbtrvPGJx0X9FB1Q2giCoAnSieeeGK0nIcffrhaFt1wB+fJ5o/tagUVaYOC3vfff3+MowJ/uqZMA4C0vUXKse0L/ZbLx++BQtexEsp30xD4nufffPONv5yvv/4668+kSvzczdfnUim+RzTyQb974u9ngrrqvib8haW+zE2UaHeLJLsXSeRVCtuStXm4bk888UT0M8BNmxLe5Z9X6v01v3OqNTUbEMibAAHAvFFSEALlJ+DmToveWLh5dapdgHpYBQER5U2UNOTEzTHo8ynophv1+KSgVFCOen4lSupxFXzTqSG+iZKbE8mXo2BRsh5aKj84V6Lgnuqn3pHKo3onG3oatlEPw0pJbv676LDeYcOGJbyscCAs2RBxBe+CAPKQIUMSlnPuuedG20LfaidK2h60V6Lgno4J3l/q1abzJkqqZ1BOouDeggULoj0a3ZyTiYrw27RP5ag3oY4p95RtAJC2r5y2z9d7N1+/B/JVn7pUTi4BwEr83M3X51K5vnc0HUnw++3nP/95wsug3S2S7F4kIVgZbRw4cKBvfw0Fjk+VeH/N75z4VuY1AvkVIACYX09KQ6CsBC666KLoTaWGicYnDcsMbjo1d1qyFA6+vPjii9Wy7bHHHr4c9dJKNYdNEHzRfG7xw4n1OpiP7uCDD652jmCDyg96g+m88Un1C67puuuui98dfR0OTKUanho9oIyeBL3uNMQ5UdIwo8AoVRAsHCRV74Nw2rp1a0RDmlSOW1glvKva8+DmVkOddFw4qdygLuecc054V8xz1TPIl2iYlHrHBfs1JD5ZCgeQUw0jT3Z8qW3PNgBI2387d2oltH2+3ov5+j2Qr/rUpXKyDQBW6uduPj6Xyvl94xZ4iv7+SvQFKe1+dtQn/l6knNs9qHtwz6bpaeJTJd5f8zsnvpV5jUB+BVgF2P1FSEKgLgq4xTbMzQfoL931dvKrzcU7BCvPaXVcrRycLLlhstFdbvhs9LmeuB535nrQ+W3uRsWvbhaTIfQiKMcF8Uyr4oWTm7/Ql6VtQb7w/uC5Vk/bfffd/Usd44aDBrv8Y3BNepGqHHfD5VfQVb74a9K2ck7yVXI9LhNeRmDkAnN+leWEmdzGsF+8kfvD1ebPn+8PDedLVFawf968eaZVjMMpqIu2BfnC+4PnWk3aDZPyL+Proo2ZlhM+R6JygvNV6mPgRNvHfo5Vantncl3Be6ImvwcyOQ95ai5QqZ+7wXuwJp9LNdctXgnB72zVINHvbdo9+T1o8VotP2d2AU1zc//5wtyXqTGFVur9dfDvnd85Mc3NCwTyJkAAMG+UFIRA6QsoGOYWQjDXA8r23HNPc3On+Uqfdtpp1rJly2oX8OWXX/ptbl41c8Nuq+0PNoRvSoJjgn1Tp041N8zWvwznC/aHH8P748v54osvolnD+aIbQ0+C/W5+leg1BrszLUfXq+tWiq9LUFY5Pi5atCh6PW5obbVLcD0NzA2v9tsDx2qZvtsQ3h9vlKmzisp3Oaq/GyocU+2gPm74d8qgplsF2FwPUn9s/DXFFFiBL2j7utv2qd7Owb+DmvweSFU++/InEHzOqcTw52qiM4T3B20c5MulnEJ97ubrcym4tnJ8dPP+Raud6Pd2Lu2lAmn3KGtJPXHzJft7V7cIjP/iU/eySr/4xS9i6lmp99fB+5LfOTHNzQsE8iZAADBvlBSEQGkKqEdVvXr1/I96x7khgeaG00S/UXTDbu3GG2+sVvkNGzaYmy/Ob+/evXu1/eENbi440zd1SkHwKNg/d+7c4KmlK8ethhbNW+hyVF83Z2H0fImeBPVxk3Bb+Bv4RHnLZdv1119vwc2kWxCkWrVLtb1U0UzfP66jvIWvQ8cGr9OVobxBu8e/B7WvklNgpGtM5xQYKW+8UzHLoe3VIvlL+fo9kL8aUVIqgUr8t5eva0rlVsr73PBec9OVRKtYar+3C/WZW9fa/Z577oneq+v+VCMaLrzwQlu4cKFve7fCs51wwgnR94Ge5Mso3+XU5P6a3zkxTcwLBAoiQACwIKwUikDpC7jJhM1NrG3PPfdctMdTuNarV6+OvnTzjkSfJ3sSBAD1bX04ZVNOUIaOL3Q52VxTovqEr7FcnruJle1Pf/qTr64CPG7S8GpVL9X2UkXTtVkm7590Zeg8QTnx70Htq+RE29fdtk/2vs7mPaEy6uq/nWR+tb09m/YK2kp1jP+sy3c5NfnczVddarst8nW+P/7xj9FpVMaOHZtwOpZ8GeW7HNq95u+CHXfc0be/m2vbBwjDJea7vVR2ujbL5HMjXRk6T7JysrmmcDnxn2HaR0IAgcQCycf0Jc7PVgQQKDMBt7CCTZw40ddaPb80z9oLL7xg48ePN7eogk2fPt3chLvVrkrfwgVJPQfTJbdwh8+iuQXDKZtygjJ0fKHLyeaaEtUnfI3l8FzfIh911FG+9596hP7973+PznEYrn+ptpfqmK7NMnn/pCtD5wnKiX8Pal8lJ9q+7rZ9svd1Nu8JlVFX/+0k86vt7dm0V9BWqmP8Z12+y6nJ526+6lLbbZGP82nor3p+KXXq1MluvfXWhMXmyyjf5dDuCZsr4cYjjjjCNPe0kv496t784YcftieeeMKP2tGXtz/84Q9jjs13e6nwdG2WyedGujJ0nmTlZHNN4XLiP8O0j4QAAokF6AGY2IWtCNSKQDA0tyaPGjaQKjVq1MiGDh3qf/RN4qGHHmq33HKLuVV9/beJv/rVr0xzAManpk2bRjdpouF0KRgi26xZs5is2ZQTlKECCl1ONteUqD4xF5nli5q0d3BsunYPV0nfqKrdg2EeGk60//77h7NEn5dqe6mC6dosk/dPujJ0nqCc+Peg9tU0Be1Xk8ds2j6b+tL2hW37bNqiVPJm855QnQv5b6dUTEq5Htm0V9BWup74z7p8l1OTz9181aWU2y1R3T7//HMbM2aM/9JOBo888ogPAibKmy+jfJdDuydqrcTbNCVNcK++66672nHHHWePP/643XvvvTZjxgw7/PDDLf53f77bSzVL12aZfG6kK0PnSVZONtcULif+M0z7SAggkFiAAGBiF7YiUPECw4cPt6uvvtpf5913320TJkyIuebwoiCZdK0PFl2I7/qfTTlBGapIocvJ5poS1ScGq4Rf6NtU3Th+9NFHvpYXXXSRXXzxxUlrXKrtpQqna7NM3j/pytB5gnLi34PaV8mJtq+7bZ/sfZ3Ne0Jl1NV/O8n8ant7Nu0VtJXqGP9Zl+9yavK5m6+61HZb1OR8WtX3oIMOsuXLl/tVf7Vw27777pu0yHwZ5bsc2j1pk2W848QTT7Sjjz7aNBfkeeedZ8uWLYsem+/2UsHp2iyTz410Zeg8ycrJ5prC5cR/hmkfCQEEEgswBDixC1sRqBWBYKWrmpxMq5bmmhQY+ulPf+oPf/TRR/0NZ1CWvoVr3769LV26NNpzLNgX/6ib1OCXeXhxAOULLyYQ9ECLPz54HV5MIF05wVCJ4NjwY7pyNBee6rtixYqUC4EE5XTs2DFmuEL4XLk8r61215BvTRj+2muv+WqeccYZpkVAUiUNGQ9SPtsrKDPRY+CsfenaXXNXJktBOepdF37fKb9eaxh0umtS3qCc+LpoX01TbbV9LvWk7Qvb9rm0SbGPydfvgWJfR105f/hzL91nXfA5J5v4z7r4cor5uZuvz6VyeQ/Mnz/fDjjgANOjfpfddddd/ku8VPWPb69UeWn3VDqluU/36hoOrPtWTeETLAZSqHYv5v01v3NK8z1IrSpLgABgZbUnV1NmAoMGDSpqjRXYCtKsWbOCp9HHIUOG2JtvvmlfffWVH4bSsGHij4zJkydHjxk8eHD0uZ5oJbMGDRrYli1bLJwvJtN3L8L748tRXYIUzhdsCz8G+1Xf/v37h3eZynnsscf8NuXbfffdY/YHLxQ80xwsSvF1CfLk+lgb7a5vi/XN8TPPPOOreeyxx9rtt9+etsr69lV/DOqPhMAx2UHh/fFGubSXzpOuHA1jT5aC+qj+4QmmlV/1US/IlStX2jfffGNdunRJWMyCBQts1apVfl98XRIekOXG2mj7LKsUzU7bF7bto9Bl9kT/dmr6e6DMLrlsq1uJn7v5+lwqh0ZdsmSJHXjggX7Ip+qr6VpOOumktFWn3ZPfg6bFK4MMye7VK/X+mt85ZfCmpIplLcAQ4LJuPiqPQM0EtCBIkBJ1n9977739bn3rGAwhDfKHHzVRdZD22muv4Kl/1GTAu+22m3/+7rvvppxfJChHkwPHfwOpOVGCiYWDfDEn+u6F5h7R/IZKOkZzIIZTcE3alqqcDz/8MNqrMf6awuWV6vOzzz7bNGxI6bDDDrP/+7//s/r1M/vID4ymTJnig2XJrjHsF2/Up08f69atmz80nC9RWW+88YbfrJ4evXv3jskS1EUbU5WjoN7UqVP9sfF10cZMywmfI1E5/gQV/J/AibaP/Ryr4CZPe2nBe6ImvwfSnoQMeRGo1M/d4D1Yk8+lvAAXsBB9OTVq1Cj74osv/Fk0V+/PfvazjM5Iuye/B80IsMQzJbtXr9T76+DfO79zSvyNSfXKVyBCQgCBOivwhz/8IeI+vfzPlVdeWc3BDZWN7ncBpWr7tcH17Iu4nlI+n5vEOOICcNXyjRs3LlrOAw88UG2/NrgeZxHXU9DnO+SQQxLmGT16tN/vevb5/IkyqfzgmnR98clNPBxp3bq1z6N6u55y8Vn8a11vUM7777+fME+pbrzggguidf/BD34QcfMAZlXVhx56KHr8tddem/BYd2MWadu2rc/nvq1NmOfcc8+NluOCvwnzaHvg7IajJ8wTvL/atWsX0XkTJdUzKMcNlamWxfXsi7gAqM/j/siqtj/YoH0qR3l1TLmnXr16+evRYyaJtq+cts+kvTPJk6/fA5mcizyxAm4uuOjn2sknnxy7M8mrSvzczdfnUhKyom/W7zX3hVO0rX/9619nXSfa3SLJ7kWyxiyxA3RPHNzfuCldYmpXiffX/M6JaWJeIJB3Act7iRSIAAJFF3jiiScibv6YlPVwPZ0irtefv6lQQM0Nn0yYf5999onmeeedd6rlSRdE1AFuHsFo0E2BCDfMJaYcN9w24nqpJb3BCTK/8sor0Tw/+tGPIjounBYvXhzp2bOnz6NgpJssObw7+vzyyy+PlpMoSKjrlIluuEaMGBE9rhyeKJAb3CjuueeeETcZc9bVVhC3b9++vpxWrVpF3BDwamUoWBecxy0iU22/NrjeGtGgruvRGVm3bl1MPr3WdpUjb9eDL2Z/8GL8+PHRc7keEcHm6KPqp3qqnO222y6yefPm6L7wEzckOlqOW1ExvMs/V+AwuKZM/9iuVkiJbcg2AEjbW6RS2j6fb8V8/B7IZ33qSlm5BAAr8XM3X59Lpfi+0ZeSbsGP6O+e888/P6dq0u4WSXYvkhNoLRyk+q5fvz7lmW666aboe8P19Kx231up99f8zkn5tmAnAjUSIABYIz4ORqA0BfQHrBsaEBkzZkzkL3/5S0TfGH7yyScRNzQ2ct9990WOO+64aG8oBTx+97vfJb2Qjz/+ONKsWTN/A6KA4TXXXBNRr61XX301ctZZZ0VvTNxcJBE3d1rScm677bZo3n79+kXcxNaRDz74IPLUU09FRo4cGd13/PHHJy1DO1T3IEij43S8ylF5KjfY5+a7S1qO6qn6Bnl1HboeXZeuLwiM6rrlVi7p5ptvjl6TG04beeuttyITJ05M+ZOox6au97nnnou+Rzp37hxxcxFF9K2sm4A6cuSRR0bP44ZqVLshDXtdeuml0bw77bRTxA1L9u2lR70O2uCyyy4LHxbzXIHecO8InV/1UH1Ur06dOvly1Gvv+eefjzk2/GL27NkRN5eOz6uA4yWXXBJxc5v5Hz0Pgr7Kox6p5ZamTZvm/wDSHxXBj1vIx1+vHoNtwWOyHo60ffm1faHfq/n6PVDoepZ7+fo8Cv596tEt2hT9jNRnYHifnidLlfi5m6/PpWRmxdo+duzYaBvvv//+kc8++yzl72wF+pIl2r0qGU1JbtcXdBrZcOaZZ0b+/ve/+3u2Tz/91N+T/O1vf4u579E9/UsvvZTwOirx/prfOQmbmo0I5EWAAGBeGCkEgdISUAAwCKykelSA68Ybb0xb+aeffjrawypReQqmKfiQLl1xxRURt6pd0rppmEO6b0PVayw8HCK+PgoCJRrOHF831dctEJK0LupR5hbQiD+spF+rt2K8R7rX6mGSLN1xxx0+kJysDDe3Y0S9LlMlDRE/7bTTUtbr9NNP90PJU5Wj87g5HZOW4+aNjNx5552pivD7FAR3C4AkLUf7lKcckwICydoq0fb4oUTha6btwxo8l0C+fg+gmVwg09/dwb/nZCVV6uduPj6XkpkVa3vQlpk+pprOgXYvVivmdt6gh366tner/UYmTJiQ8iSVeH/N75yUTc5OBHIWIACYMx0HIlC6AgsXLvQ94k455RQ/xNKtihpRgEQBP/UM03ATN8F02mHC4SucOXNmRHPLKdjXvHnziIbYavim5h9JNi9b+Pjg+dtvvx054YQTIqqTvtFU7y236l3k/vvvD7Jk9KiejDpOx6scladyEw1TTlaghseq/roOXY+ua+DAgf46db3llvIdANT1qwehvp3WkOCmTZtG1JNMvf5uvfXWpENtE7mp98bhhx8ecQuD+PbSo16n6rEXX46G9upbcZ1f9VB9VC/Vb9KkSfHZk75WMPE3v/lNZOjQob63p3p8Dhs2zG+LH56etJAS3JHPAKAuj7YvwUYucpXy9XugyJdRsqfPVwAwuMBK/NzN1+dSYFTsx3TBn/j9qQKAwbXQ7oFEaT9q6h19Ca9eoMOHD49otIVGIriVr/2IFo120O/1TO+xK/H+mt85pf0epnblKVBP1Xa/XEgIIIAAAggggAACCCCAAAIIIIAAAgggUIEC9SvwmrgkBBBAAAEEEEAAAQQQQAABBBBAAAEEEPhOgAAgbwUEEEAAAQQQQAABBBBAAAEEEEAAAQQqWIAAYAU3LpeGAAIIIIAAAggggAACCCCAAAIIIIAAAUDeAwgggAACCCCAAAIIIIAAAggggAACCFSwAAHACm5cLg0BBBBAAAEEEEAAAQQQQAABBBBAAAECgLwHEEAAAQQQQAABBBBAAAEEEEAAAQQQqGABAoAV3LhcGgIIIIAAAggggAACCCCAAAIIIIAAAgQAeQ8ggAACCCCAAAIIIIAAAggggAACCCBQwQIEACu4cbk0BBBAAAEEEEAAAQQQQAABBBBAAAEECADyHkAAAQQQQAABBBBAAAEEEEAAAQQQQKCCBQgAVnDjcmkIIIAAAggggAACCCCAAAIIIIAAAggQAOQ9gAACCCCAAAIIIIAAAggggAACCCCAQAULEACs4Mbl0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyHsAAQQQQAABBBBAAAEEEEAAAQQQQACBChYgAFjBjculIYAAAggggAACCCCAAAIIIIAAAgggQACQ9wACCCCAAAIIIIAAAggggAACCCCAAAIVLEAAsIIbl0tDAAEEEEAAgdwFevfubfXq1bNTTjkl90JK5MilS5dau3bt/PV88MEHeavVPffc48uU08yZM/NWbiUXFIlEbNiwYd7t7rvvruRL5doQQAABBBBAoIQECACWUGNQFQQQQAABBBBAoBACV1xxhS1fvtwOOeQQ23XXXQtxCsrMUEDB0l//+tc+tx7Xrl2b4ZFkQwABBBBAAAEEchcgAJi7HUcigAACCCCAAAIlLzBr1iy78847fT0VCCQVX+CYY46xgQMH2oIFC+yvf/1r8StEDRBAAAEEEECg4gUIAFZ8E3OBCCCAAAIIIJCLgIa0arimhrmWcxo3bpxt3rzZ9tprL/v+979fzpdSMXWvX7++XXDBBf56brjhBtuwYUPFXBsXggACCCCAAAKlKUAAsDTbhVohgAACCCCAAAI1FlixYoXde++9vpyf/OQnNS6PAvIncPTRR1ujRo1s8eLF9uCDD+avYEpCAAEEEEAAAQQSCBAATIDCJgQQQAABBBBAoBIEFFjSHHMKNCngRCodAS3KcvDBB/sKjR8/vnQqRk0QQAABBBBAoCIFCABWZLNyUQgggAACCCAQCMyfP98uvfRS+973vmetW7f2wbDOnTv7lViPP/54P8R31apVQfboY7JVgDU0WAs5ZPqz3377RcuMf/Laa6/ZySefbH379rXmzZtbq1atfL1++ctfmupd0/Twww/7IlSH9u3bpyzu1VdfNXn06dPHmjVr5uvTq1cv23333e2iiy4y7U+Xtm7danfccYftueee1rZtW2vRooUNHz7cfv/739u6deuSHq7jVL7Oo6HKHTp08O3Upk0b23HHHf322bNnJz1eO3SNahM9Kk2bNs3OO+8869+/v78W7Uu0UvFXX33lh+NqZV69P3Ttag+t/vzhhx/6spL9R0N3b775Zn/Ojh07+jorsKf5/UaPHm033XRTwnMG5R155JH+6dtvv21z5swJNvOIAAIIIIAAAgjkX8DNbUNCAAEEEEAAAQQqUuCNN96IuKBaxN1Bpfx55plnql2/C375Y1yALmbf119/nbKs+HONGDEi5ni9WL9+feS4445LWY4LnkWefvrpasdmusEFpyJNmjTx57j88stTHvaLX/wiZV10TS6AWK2Mu+++O3rc559/HvnBD34QfR3vsNtuu0XWrFlTrQxtuPLKK5MeF5TjAqSRxx9/POHx2ihn5dXjk08+GZFfcGzwqLYLp+uvvz7iekdWyxfkd0HDSDI7F6CNDBkyJOmxQRkXXnhh+JQxzydPnhw93gVOY/bxAgEEEEAAAQQQyKdAQ3dzQkIAAQQQQAABBCpOYOPGjeaCbKbefS1btrRzzz3XRo4caZ06dbJNmzaZCwbZO++8Y0888URW177tttvaxIkTUx6jnnf/7//9P59HvejCyd3I2VFHHWXPPfec33zYYYeZVoVVrzMtDvH+++/bjTfeaOrxpnzqHbbLLruEi8jo+QcffGAyUNp1112THvPss8/an/70J79fvfXkNHjwYN8bTnMIusCevfzyy75eSQtxO84880x77733fI9GXU+XLl38NfzhD3+wd9991x9/9dVX27XXXlutmKqqKuvatauNGTPG9thjD2/RtGlT3ytObfS3v/3NXPDQTjjhBPv44499/aoV8t0GuWm+Q/WodME722effaxBgwYmj2222SZ6mAv+2cUXX+xfB9et3oLqdThlyhT7y1/+4uutdlSPxP/5n/+JHqsnP//5z+2LL77w23S+sWPHWrdu3fy5tLqveg8+9dRTMcfEvxgwYIA/n5xff/11bxifh9cIIIAAAggggEBeBPIZTaQsBBBAAAEEEECgVAReeeWVaO+qRD38gnq6FXIjK1euDF5GH5P1AIxmSPLEBZoibhipP7cLpFUrWz293E2c73n2z3/+M2Epy5Yti2y//fY+nxsSmzBPuo1u9d/o9bvhpUmzn3jiiT6frnf16tVJ8y1durTavnAPQF3TP/7xj2p51BNx6NCh/hzqRSjv+KSeeS4oG785+lr1d4FXX4YLtkW3h58EPQBVDxeIi8yaNSu8O+a5eisGPf/U+9ANQY7ZrxdbtmyJ6FwqzwUOI2qTIKkHZ3B8qh5+yp/ILShHjy4o7c8xaNCg8GaeI4AAAggggAACeRWo725qSAgggAACCCCAQMUJfPPNN9Fr2nfffaPP4580bNjQz70Xvz2X15q37/DDDzcXIDLNBecCjzFlu7s4c4E5X7R6lAWLQMSfS/PnqYeaknoAaj67bNPcuXOjh6jXY7IUOGmOxHAPufj8up5UST3g1BMuPrlhyH4uPm13wbBor7lwPs23qIVKkqXu3bub5kVUcsOiTY6p0nXXXWc9e/ZMmkU9LF0g0vesdAFAP3dgfGb1xrzllltM9Vfvw0cffTSaxQUD/fHakOq9pf3p3IK2UY/UdNel8kgIIIAAAggggEAuAgQAc1HjGAQQQAABBBAoeQENKQ2S66kWPC3Yo4J+RxxxhF+8Q0FFBYz69esXcz4NGZ0+fbrfpuG9qVI4sKQhtNmmxYsX+0M0FLZx48ZJDw+c3HyJ0bolzZxix49//OOke3feeefovhkzZkSfJ3uiYdsKiGn48aRJk/yPrkMp2JfsWF1ruhWPFZhV0iIcWhwkWdJwYC0OohRuAy2oEpi6Xo+mIcy5piBAqOHaGgpMQgABBBBAAAEECiFAALAQqpSJAAIIIIAAAkUX2Hvvvf1ccqqIW+TC3CIUfv459ajTHID5TqeddpqfZ07lamVYzTcYn8KrymquOwWfkv2Ee+MFvfTiy0v1Wr3UlNSbMFU66aST/G71znNDdf28iQqYanXcbJIbwpo0exDkUgY3zDhhPjdk18+rp96AWo1XcyKqPgrA6eess86KHrdkyZLo8/gnmsdP8wcmSzpPEBy97LLLkvoH7RK0WbgN1Cvw2GOP9adQoHe77bbz8wk+//zzWQfxwu2zdu3aZNVmOwIIIIAAAgggUCMBAoA14uNgBBBAAAEEEChVAQ0pVU8vLWihpEUgfvWrX5kCg+rZpeG3999/v7m53mp8CVoo4sEHH/Tl/PSnP/ULaSQqdNGiRYk2p922bt26tHniMwRBMPVMTJXcyr1+wQs3b6G5+frsoYceMgUzFUjT0NtzzjnH/vOf/6Qqwu8LeuglyqjhtEFK5O3mQjS3oq6vhwJ06VKqawoH1BKVk6820CIhWsBFSXXWkO1DDz3U1DtQi67otZtbMlEVYraFryXVMOiYg3iBAAIIIIAAAghkKcAqwFmCkR0BBBBAAAEEykdAQSWt2KtAoH40zFU92xR0efHFF/3PTTfdZOq5FczFlu3VPfbYY6Z55JQUTPvzn/+ctIhw8Ev1UW+3TFIudevYsaMvWsNKNbdcqqGuP/vZz/ywWQVEX3rpJT/voIJX8+bNs9tvv93cwiU+eKpVfPOd1JtPq/sqyKlejxdddJGNGjXKD59WT8BgqO2rr77qfXX+VHPlacXfVCncBldccUXa4cJBWS1atAie+sdWrVr5+Qi1arNWff7Xv/5ln376qQ8oq9egfm644QZ78skn/crGMQeHXgQ9NbVJ10tCAAEEEEAAAQQKIUAAsBCqlIkAAggggAACJSOggJDm5tOP0oIFC+yFF16wv/71r/bRRx/5n7PPPtueeOKJrOv8ySefmIbQKiClYaAKBGn+v2RJvcOCpF6IGuJaqBQEAN0Kt74nms6XKinIqKHS+tExCmbJRD3dFET8/e9/73u2aZGTfCYNoQ3mvtP5DjjggITFhwNlCTNkuDHcBupxV9M20NBy/ShpeLMCgffcc489/vjjpt6GmmdQ8z6qh2WitHz5cr9Z/kGvzUT52IYAAggggAACCNRE4L/jMWpSCscigAACCCCAAAJlIqBFL0499VS/qINWvlV69tlnfa/AbC5Bc8IpGKaea+q5pR594bnuEpW10047RTdrLsJCpmDxCp1j6tSpWZ1KQ3Zlo6HNr7zySvRYBTjznbTQh5LskgX/tD+Yi0/Pa5I0t2DQ0y7fbdCyZUs/LFi9QrXKs5ICzm+99VbSKgdts/322yfNww4EEEAAAQQQQKCmAgQAayrI8QgggAACCCBQlgLq/TVixAhfd63iGvRCy+RiNFeeehTOmTPH1MNQ8/+lWgQjKFNBNc2rp6RhtSqnUGmfffaJFq35D3NNqnMwr16qxTdyLT9YQVcW6nmYKCnIqtV285HUXocccogvasKECfbll1/mo9hqZWg4eJCSuWlF4ylTpvhs3//+94PsPCKAAAIIIIAAAnkXIACYd1IKRAABBBBAAIFSEHjzzTdTrmSrlYBff/11X1XNPRcMmc2k7meccYb9+9//9lm12IMWFMkkqWedFiJRmjFjhh8+vHHjxqSHKkCkIbi5pB49elivXr38oZqnLlnSoh/hhSji86nnXTBMtU+fPvG7a/xai40oKciXqIeh5uyT9/z582t8rqAArf6rQKACjkcddZTNnTs32FXtUee/7777YvKo7YL3TrUDvtug4GKQkrnJNpjP8KCDDgqy84gAAggggAACCORdIPkkNXk/FQUigAACCCCAAAK1J6ChqxrCqp5wWp11+PDhPsinYJeGXd5222328ccf+wqdfvrpKefuC9f6rrvu8gEhbdt///3twAMPtEmTJoWzxDzX4hHhAJBW1dVCG5rv7pFHHvF10ByEmkdOQ1MV9Js8ebKfS+7pp5/288Kdd955MWVm+kJDlG+++WZ77bXXki4Ecskll/iVfpV33333tQEDBpjqvHTpUj909ZZbbvGnU8BMgbh8p2OOOcYHRRUI1dBszT0oU1loeLDOr7ka99prL784ST7Or+HRWqDjggsusC+++MLPA3jWWWf59uzcubPvmTlz5kw/TFxzFGoYrxaTCXpvzp4920aOHOlXLh4zZoztsssutu222/qqqVeogqpBMHPHHXe0ZL37guHVHTp08KtT5+PaKAMBBBBAAAEEEEgkQAAwkQrbEEAAAQQQQKAiBNTDSz21UvXWUuDr2muvzfh6FfwJklamDc+1F2wPP2qYsRaGCJJW41WA6Pzzz/dBSC0QcfHFFwe7qz3msgJwUMiZZ57pA4AKSqlHpAJ8iZKGP//973/3P4n2N2nSxNdVga58JwXVbr31Vh9c1DDgcePG+Z/weY499ljTtaSaIzCcP5PnWuxEgU49asVj9eTUT6KklYgTLdCh4KF+kiUNC9diIMlWYH7ggQf8obo+DUknIYAAAggggAAChRIgAFgoWcpFAAEEEEAAgaIKXHTRRb7X38svv2xarVdDSLUqq1KXLl18jzut4KvegbWdFOz529/+Zueee67deeedPkCowOKaNWtMw5HVY3DnnXe20aNH2w9/+MOcq6cVbvfYYw/fk+3+++9PGABU70AtYPLGG2/4npFa3ERDfps3b279+vUzzWWnemrxjEIl9fwbOHCgD8BpYQ4FJNUrbocddvC9AtVLMBxEzVc9FFT80Y9+ZLfffrtpyK7m49O5FfBUjz4Fd9UbUSv5qj5BUq9S1efFF1+09957z88FuXDhQt9zUIuZqN5jx461U045xZcVHBd+fPfdd+3rr7/2m+RLQgABBBBAAAEECilQz807EinkCSgbAQQQQAABBBBAoHgCGoqqHmZayENBRgUYScUX0HDq8ePH26hRo+yFF14ofoWoAQIIIIAAAghUtACLgFR083JxCCCAAAIIIFDXBY4++mjfm1C9+nJdUKSuG+b7+hWIvffee32xv/3tb/NdPOUhgAACCCCAAALVBAgAViNhAwIIIIAAAgggUDkCmn9O8+op3XTTTbZ27drKubgyvRLNObl582ZTcDbZAiFlemlUGwEEEEAAAQRKVIAhwCXaMFQLAQQQQAABBBDIp4BW09XKvppPb8iQIfksmrKyENDsOwrIasGT0047zXr27JnF0WRFAAEEEEAAAQRyEyAAmJsbRyGAAAIIIIAAAggggAACCCCAAAIIIFAWAgwBLotmopIIIIAAAggggAACCCCAAAIIIIAAAgjkJkAAMDc3jkIAAQQQQAABBBBAAAEEEEAAAQQQQKAsBAgAlkUzUUkEEEAAAQQQQAABBBBAAAEEEEAAAQRyEyAAmJsbRyGAAAIIIIAAAggggAACCCCAAAIIIFAWAgQAy6KZqCQCCCCAAAIIIIAAAggggAACCCCAAAK5CRAAzM2NoxBAAAEEEEAAAQQQQAABBBBAAAEEECgLAQKAZdFMVBIBBBBAAAEEEEAAAQQQQAABBBBAAIHcBAgA5ubGUQgggAACCCCAAAIIIIAAAggggAACCJSFAAHAsmgmKokAAggggAACCCCAAAIIIIAAAggggEBuAgQAc3PjKAQQQAABBBBAAAEEEEAAAQQQQAABBMpCgABgWTQTlUQAAQQQQAABBBBAAAEEEEAAAQQQQCA3AQKAublxFAIIIIAAAggggAACCCCAAAIIIIAAAmUhQACwLJqJSiKAAAIIIIAAAggggAACCCCAAAIIIJCbAAHA3Nw4CgEEEEAAAQQQQAABBBBAAAEEEEAAgbIQIABYFs1EJRFAAAEEEEAAAQQQQAABBBBAAAEEEMhNgABgbm4chQACCCCAAAIIIIAAAggggAACCCCAQFkIEAAsi2aikggggAACCCCAAAIIdHe4kAAAAGxJREFUIIAAAggggAACCOQmQAAwNzeOQgABBBBAAAEEEEAAAQQQQAABBBBAoCwECACWRTNRSQQQQAABBBBAAAEEEEAAAQQQQAABBHITIACYmxtHIYAAAggggAACCCCAAAIIIIAAAgggUBYC/x8xc5LFCwoIaQAAAABJRU5ErkJggg==\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for s in symbols:\\n\",\n    \"    for d in dates:\\n\",\n    \"        _ = mkt_impact(s,d,plot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/.ipynb_checkpoints/Untitled-checkpoint.ipynb",
    "content": "{\n \"cells\": [],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/Algo.py",
    "content": "import sys\nimport numpy as np\nimport pandas as pd\nimport itertools\nimport logging\nimport inspect\nimport copy\nimport matplotlib.pyplot as plt\nimport tqdm\nimport collections\nimport environment\nimport utility\n\n\nclass Algo(object):\n    \"\"\" Base class for algorithm calculating cash inventory for a market making strategy.\n    You have to subclass step method to submit new orders.\n    \"\"\"\n\n    COMMISSIONS = 0.003 # fix commission\n    LATENCY = 100*1e-3*1e9 # 100 ms: LON to NY is ~60ms\n    ONLY_EXEC = True # the algo only takes a decision at times of executions, and not at each timestamp\n\n    def __init__(self):\n        \"\"\" Subclass to define algo specific parameters here.\n        \"\"\"\n        pass\n\n    def init_run(self):\n        \"\"\" Called before run method. Use to initialize persistent variables.\n        \"\"\"\n        self.orders = orders()\n        self.cash = 0\n        self.inventory = 0\n        self.value = 0\n        self.history = np.empty((0,4), float)\n\n\n    def run(self, env):\n        \"\"\" Run algorithm and get inventory,cash and total value.\n        :params env: environment object used to test the\n        \"\"\"\n        self.init_run(env.initial_param)\n\n        if self.ONLY_EXEC:\n            time = env.time[env.messages.type=='E']\n        else:\n            time = env.time\n\n        time = time[time < 57600000000000][time > 34200000000000]\n\n        for i in tqdm.tqdm(time.index):\n\n            t = time.at[i]\n            tmp_row = env.get_data_at_time(i)\n            self.collect_from_orders(tmp_row)\n            if tmp_row[1].type == 'E':\n                self.update_history(t,tmp_row[1].price)\n            self.step(tmp_row)\n            self.orders.timestamp = t\n\n    def step(self, data_up_to_now):\n        \"\"\" Get startegy for next tick.\n        Should update orders.\n        \"\"\"\n        raise NotImplementedError('Subclass must implement this!')\n\n    def collect_from_orders(self,last_message):\n        \"\"\" Method to update inventory and cash after observing next tick given limit orders submitted.\n        :params pt: last execution price.\n        \"\"\"\n        t = last_message[0].time\n\n        if self.orders.timestamp < t - 2*self.LATENCY and last_message[1].type=='E':\n            pt = last_message[1].price\n            # buy\n            for price,size in self.orders.get_buy_orders().items():\n\n                if price > pt:\n                    self.inventory += size\n                    self.orders.delete_buy_order(price)\n                    self.cash -= size*(price + self.COMMISSIONS)\n\n            # sell\n            for price,size in self.orders.get_sell_orders().items():\n\n                if price < pt:\n                    self.inventory -= size\n                    self.orders.delete_sell_order(price)\n                    self.cash += size*(price - self.COMMISSIONS)\n\n    def update_history(self,t,pt):\n        \"\"\" Method to update history of the algorithm observing next tick.\n        :params pt: last execution price.\n        \"\"\"\n        self.value = self.inventory*pt + self.cash\n        self.history = np.vstack((self.history,[t,self.inventory,self.cash,self.value]))\n\n    def plot(self):\n        \"\"\" Plot inventory, cash and total value of the algorithm.\n        \"\"\"\n        fig = plt.figure()\n        gs = fig.add_gridspec(2,2)\n\n        ax1 = fig.add_subplot(gs[0, 0])\n        ax2 = fig.add_subplot(gs[0, 1])\n        ax3 = fig.add_subplot(gs[1, :])\n\n        fig.suptitle('performance')\n        ax1.plot(self.history[:,0],self.history[:,1])\n        ax2.plot(self.history[:,0],self.history[:,2])\n        ax3.plot(self.history[:,0],self.history[:,3])\n        ax1.set_title('inventory')\n        ax1.grid(1)\n        ax2.set_title('cash')\n        ax2.grid(1)\n        ax3.set_title('value')\n        ax3.grid(1)\n        fig.autofmt_xdate\n        plt.show()\n\n    def get_total_value(self):\n        \"\"\" total final value of the strategy\n        \"\"\"\n        return self.value\n\n\nclass orders(object):\n    def __init__(self, orders = [collections.Counter(), collections.Counter()]):\n        self.orders = orders\n        self.timestamp = 0\n\n    def get_sell_orders(self):\n        return self.orders[1]\n\n    def get_buy_orders(self):\n        return self.orders[0]\n\n    def add_sell_order(self, price, size):\n        self.orders[0][price] += size\n\n    def add_buy_order(self, price, size):\n        self.orders[1][price] += size\n\n    def set_buy_order(self, price, size):\n        self.orders[0][price] = size\n\n    def set_sell_order(self, price, size):\n        self.orders[1][price] = size\n\n    def delete_buy_order(self, price):\n        self.orders[0][price] = 0\n\n    def delete_sell_order(self, price):\n        self.orders[1][price] = 0\n\n    def delete_all_buy_order(self):\n        self.orders[0] = collections.Counter()\n\n    def delete_all_sell_order(self):\n        self.orders[1] = collections.Counter()\n\n    def add_buy_orders_from_dict(self, dct):\n        self.orders[0] += dct\n\n    def add_sell_orders_from_dict(self, dct):\n        self.orders[1] += dct\n"
  },
  {
    "path": "examples/Algo_fixedSpread.py",
    "content": "from Algo import Algo\nimport environment\nimport utility\n\nclass fixed_spread(Algo):\n\n    def __init__(self,spread_ticks):\n        \"\"\" Algo specific parameters here.\n        \"\"\"\n        super(fixed_spread, self).__init__()\n        self.spread_ticks = spread_ticks\n\n    def init_run(self,env_p0):\n        \"\"\" Called before run method. Use to initialize persistent variables.\n        \"\"\"\n        super(fixed_spread, self).init_run()\n        self.tick = env_p0['ticksize']\n        self.spread = self.spread_ticks*self.tick\n        self.last_price = env_p0['p0']\n        self.width = 1\n        self.a_buy = self.last_price\n        self.a_sell = self.a_buy + self.spread\n        self.alpha = 10   ##liquidity parameter\n\n    def step(self,last_message):\n        \"\"\" Get strategy for next tick.\n        \"\"\"\n        t = last_message[0].time\n\n        if last_message[1].type=='E':\n\n            pt = last_message[1].price\n\n            self.orders.delete_all_buy_order()\n            self.orders.delete_all_sell_order()\n\n            if pt < self.a_buy:\n                self.a_buy = pt\n                self.a_sell = self.a_buy + self.spread\n            elif pt > self.a_sell:\n                self.a_sell = pt\n                self.a_buy = self.a_sell - self.spread\n            else :\n                pass\n\n\n            self.width = max(self.width, int(abs(pt - self.last_price)/self.tick))\n\n            self.last_price = pt\n\n            for i in range(1,self.width):\n                self.orders.set_buy_order(self.a_buy-i*self.tick,self.alpha)\n                self.orders.set_sell_order(self.a_sell+i*self.tick,self.alpha)\n\nif __name__=='__main__':\n    s = fixed_spread(10);\n    a = environment.ts('08302018','PSX','AAPL')\n    s.run(a)\n    s.plot()\n"
  },
  {
    "path": "examples/Algo_lowRegret.py",
    "content": "from Algo import Algo\nfrom Algo_fixedSpread import fixed_spread\nimport environment\nimport collections\nimport utility\nimport numpy as np\n\nclass low_regret(Algo):\n\n    def __init__(self,B):\n        \"\"\" Algo specific parameters here.\n        \"\"\"\n        super(low_regret, self).__init__()\n        self.B = B\n        self.experts = [fixed_spread(b) for b in B]\n        self.Expert_distribution = utility.normalize(np.ones(len(B)))\n\n    def init_run(self,env_p0):\n        \"\"\" Called before run method. Use to initialize persistent variables.\n        \"\"\"\n        super(low_regret, self).init_run()\n        for e in self.experts:\n            e.init_run(env_p0)\n\n    def step(self,last_message):\n        \"\"\" Get startegy for next tick.\n        \"\"\"\n        self.orders.delete_all_buy_order()\n        self.orders.delete_all_sell_order()\n        t = last_message[1].time\n        p = last_message[1].price\n\n        for i,e in enumerate(self.experts):\n\n            e.collect_from_orders(last_message)\n            e.step(last_message)\n            e.update_history(t,p)\n            e.orders.timestamp = t\n\n            c1 = collections.Counter({x: y*self.Expert_distribution[i] for x, y in e.orders.get_buy_orders().items()})\n\n            c2 = collections.Counter({x: y*self.Expert_distribution[i] for x, y in e.orders.get_sell_orders().items()})\n\n            self.orders.add_buy_orders_from_dict(c1)\n            self.orders.add_sell_orders_from_dict(c2)\n\n        self.update_expert_distribution()\n\n    def update_expert_distribution(self):\n        k = 0.0001\n        inventory = np.array([e.inventory for e in self.experts])\n        Loss = np.exp(-k*np.abs(inventory))\n        self.Expert_distribution = utility.normalize(Loss)\n\n\nif __name__=='__main__':\n    s = low_regret([1,2,3,4,5]);\n    a = environment.ts('01302019','NASDAQ','ACAD')\n    s.run(a)\n    s.plot()\n"
  },
  {
    "path": "examples/AutoregressionOfBuySell.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import environment\\n\",\n    \"import utility\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import stats\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Autocorrelation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def autocorr(x,lags):\\n\",\n    \"    '''function to compute correlation of x for lags'''\\n\",\n    \"\\n\",\n    \"    corr=[1. if l==0 else np.corrcoef(x[l:],x[:-l])[0][1] for l in lags]\\n\",\n    \"    return np.array(corr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tmin=9.5*1e9*60*60\\n\",\n    \"tmax=16.5*1e9*60*60\\n\",\n    \"lags=range(20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Data Selection: some big cap/liquid stocks and some medium cap.\\n\",\n    \"Algorithmic execution slipts large clients order in small ones, hence we expect large autocorr\\n\",\n    \"The splitting is less common in med-cap stocks, hence we expect weakest autocorr\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#symbols = ([\\\"AAPL\\\",\\\"ARQL\\\",\\\"CSCO\\\",\\\"QQQ\\\",\\\"FB\\\",\\\"INTC\\\",\\\"QCOM\\\",\\\"NVDA\\\",\\\"CMCSA\\\",\\\"AMD\\\",\\\"EEM\\\",\\\"SPY\\\",\\\"GDX\\\",\\\"MSFT\\\",\\\"AMZN\\\"])\\n\",\n    \"#symbols.sort()\\n\",\n    \"#dates = ['01302019','12282018','05302019','05302018','03272019']\\n\",\n    \"symbols = [\\\"AAPL\\\",\\\"RARE\\\",\\\"MSFT\\\",\\\"INTC\\\",\\\"CSCO\\\"]\\n\",\n    \"dates = ['01302019','12282018','05302019','05302018','03272019']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_acf(date,symbol,lags=range(50)):\\n\",\n    \"    a = environment.ts(date,'NASDAQ',symbol,PATH=\\\"/Volumes/LaCie/\\\")\\n\",\n    \"    executions = a.messages[(a.messages.type==\\\"E\\\") & (a.messages.time < tmax) & (a.messages.time > tmin)]\\n\",\n    \"    sign = executions.loc[:,['time','side']]\\n\",\n    \"    sign.loc[:,'side'] = sign.side.apply(lambda x: x if x else -1)\\n\",\n    \"    acf = autocorr(sign.side.values, lags=lags)\\n\",\n    \"    return acf\\n\",\n    \"\\n\",\n    \"def plot_acf(lags,acf):\\n\",\n    \"    plt.loglog(lags,acf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"AAPL\\t01302019\\n\",\n      \"AAPL\\t12282018\\n\",\n      \"AAPL\\t05302019\\n\",\n      \"AAPL\\t05302018\\n\",\n      \"AAPL\\t03272019\\n\",\n      \"RARE\\t01302019\\n\",\n      \"RARE\\t12282018\\n\",\n      \"RARE\\t05302019\\n\",\n      \"RARE\\t05302018\\n\",\n      \"RARE\\t03272019\\n\",\n      \"MSFT\\t01302019\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.7/site-packages/numpy/core/_methods.py:32: RuntimeWarning: invalid value encountered in reduce\\n\",\n      \"  return umr_minimum(a, axis, None, out, keepdims, initial)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MSFT\\t12282018\\n\",\n      \"MSFT\\t05302019\\n\",\n      \"MSFT\\t05302018\\n\",\n      \"MSFT\\t03272019\\n\",\n      \"INTC\\t01302019\\n\",\n      \"INTC\\t12282018\\n\",\n      \"INTC\\t05302019\\n\",\n      \"INTC\\t05302018\\n\",\n      \"INTC\\t03272019\\n\",\n      \"CSCO\\t01302019\\n\",\n      \"CSCO\\t12282018\\n\",\n      \"CSCO\\t05302019\\n\",\n      \"CSCO\\t05302018\\n\",\n      \"CSCO\\t03272019\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"acfs={}\\n\",\n    \"tmp = {}\\n\",\n    \"for s in symbols:\\n\",\n    \"    for d in dates:\\n\",\n    \"        print(s,d,sep=\\\"\\\\t\\\")\\n\",\n    \"        tmp[d] = get_acf(d,s,lags)\\n\",\n    \"    acfs[s] = np.mean(np.array(list(tmp.values())),axis=0)\\n\",\n    \"    tmp = {}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Log log Plot of the autocorrelation for different stocks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAQABJREFUeAHs3QeY3GW1+PGzO7O9JdmSTgqpJIEAAkmoAanSFFQEUWz3EZXL9cFyRf+Wq1dEBS8o97kiNlAQBAUBxdBDCyQkIIQE0nvZ3Wzv7X/OO/ubnZlsmd3sZmd++32f52VmfvOrn9+wmT37vuekdGgTGgIIIIAAAggggAACCCCAAAIIIIAAAgj4UiDVl1fFRSGAAAIIIIAAAggggAACCCCAAAIIIICAEyAAyAcBAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCxAA9PHN5dIQQAABBBBAAAEEEEAAAQQQQAABBBAgAMhnAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8LEAD08c3l0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGcAAQQQQAABBBBAAAEEEEAAAQQQQAABHwsQAPTxzeXSEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIZwABBBBAAAEEEEAAAQQQQAABBBBAAAEfCwR9fG1cWgILNDY2yltvveXOsLi4WIJBPooJfLs4NQQQQAABBBBAAAEEEEAAgSQVaG1tldLSUnf2CxYskMzMzCS9Ek77UASIuhyKHtsOWMCCfyeeeOKAt2dDBBBAAAEEEEAAAQQQQAABBBDon8Brr70mJ5xwQv82Ym1fCDAF2Be3kYtAAAEEEEAAAQQQQAABBBBAAAEEEECgewFGAHbvwtIhFrBpv16zv0CMHz/ee5mwjw0NDbJ8+XJ3fqeddppkZWUl7LlyYggggAACCCCAAAIIDIYA34EHQ5F9IDC8Anv27AnPwIv8XXx4z4qjH24BAoCHW5zjOYHInH8W/Js0aVLCy9iXn6KiIneedr4EABP+lnGCCCCAAAIIIIAAAocowHfgQwRkcwQSTCDyd/EEOzVOZ4gFmAI8xMDsHgEEEEAAAQQQQAABBBBAAAEEEEAAgeEUIAA4nPocGwEEEEAAAQQQQAABBBBAAAEEEEAAgSEWIAA4xMDsHgEEEEAAAQQQQAABBBBAAAEEEEAAgeEUIAA4nPocGwEEEEAAAQQQQAABBBBAAAEEEEAAgSEWIAA4xMDsHgEEEEAAAQQQQAABBBBAAAEEEEAAgeEUIAA4nPocGwEEEEAAAQQQQAABBBBAAAEEEEAAgSEWIAA4xMDsHgEEEEAAAQQQQAABBBBAAAEEEEAAgeEUIAA4nPocGwEEEEAAAQQQQAABBBBAAAEEEEAAgSEWIAA4xMDsHgEEEEAAAQQQQAABBBBAAAEEEEAAgeEUIAA4nPocGwEEEEAAAQQQQAABBBBAAAEEEEAAgSEWCA7x/tk9AggggAACCCCAAAIIIIAAAgkl0N7eLrW1tVJdXS3Nzc3S1taWUOfHySAQKRAIBCQ7O1tGjRolmZmZkW/xHIG4BQgAxk3FiggggAACCCCAAAIIIIAAAskuUFNTI7t27ZKOjo5kvxTOf4QItLa2SlNTk1RUVEhBQYGMHz9eUlJSRsjVc5mDJUAAcLAk2Q8CCCCAAAIIIIAAAggggEBCC3QX/LNAio2woiGQqAIWAPRaVVWVpKenS1FRkbeIRwTiEiAAGBcTKyGAAAIIIIAAAggggAACCCSzgE37jRz5l5ubK2PGjHFTKxlNlcx31v/nblPUKysrZf/+/e5iS0tLJT8/3wUC/X/1XOFgCVAEZLAk2Q8CCCCAAAIIIIAAAggggEDCCljOP2/arwX/Jk2aJDk5OUylTNg7xol5AjZCtbCw0HVvmX2eaQj0R4AAYH+0WBcBBBBAAAEEEEAAAQQQQCApBazgh9ds5B+j/jwNHpNFwEb9ea2urs57yiMCcQkQAIyLiZUQQAABBBBAAAEEEEAAAQSSWcCq/VqzwJ9VVKUhkGwCGRkZ4cC193lOtmvgfIdPgADg8Nkn3JFXrlwpF1xwgSstbkPhFy1aJA888EDCnScnhAACCCCAAAIIIIAAAgj0V8DyqFmz6ZSM/uuvHusngoB9br2CNZbTkoZAfwQoAtIfLR+v++yzz8q5554rmZmZcsUVV0heXp489NBD8tGPflR27NghN9xwg4+vnktDAAEEEEAAAQQQQAABBBBAAAEE/CvACED/3tu4r8xKin/uc5+T1NRUWb58udx5551yyy23yJtvvimzZs2SG2+8UbZt2xb3/lgRAQQQQAABBBBAAAEEEEAAAQQQQCBxBAgAJs69GLYzeeaZZ2TTpk1y5ZVXysKFC8PnUVBQ4IJ/llvg97//fXg5TxBAAAEEEEAAAQQQQAABBBBAAAEEkkeAAOAw36v9+/fLY489Jt/+9rfl/PPPl6KiIpePwub2X3PNNf06OxulZ1N158yZ48rZW2WrE044QX7yk59IfX19j/t67rnn3HvnnHPOQevYtGBrzz///EHvsQABBBBAAAEEEEAAAQQQQAABBBBAIPEFyAE4zPdo7Nixg3IGjz76qHz84x+XyNL2FvRbtWqV63fddZc8/vjjMmPGjIOOt2HDBrds5syZB703btw4yc3NFW+dg1ZgAQIIIIAAAggggAACCCCAAAIIIIBAQgswAjCBbs8RRxwh3Y3C6+sU16xZ44p1WPDPgnX//d//LS+//LI8/fTTLrefbf/ee+/JBz7wAampqTlod1VVVW6ZTfntruXn54u3TnfvswwBBBBAAAEEEEAAAQQQQACBvgRsZpnNdvO6/d46kDbQ/djsN+/YsY/Z2dkyZcoUufTSS+Xee+8Vy5XfXfvud78b3oc3m6679ViGQKIJEAAc5jtiU39t9N7evXtdoY1f/vKX/T6j66+/XhoaGiQYDMqyZctc3r7FixfLmWee6Qp6/PjHP3b7tCCgFfegIYAAAggggAACCCCAAAIIIHC4BWJzy999990DOoXB2k/kwe136u3bt8sjjzwiV111lSxZssT9nh65Ds8RSGYBAoDDfPe+973vyYUXXigDnQr82muvyQsvvOCu4jOf+YxY4C+2WV7AuXPnusW33XabtLS0RK3ijfzraZSfjSz01onakBcIIIAAAggggAACCCCAAAIIxCFgAbYHH3zQrWkz16w98MAD0tTU5J7H+5/B2s+1114rb731Vri/8sor8vOf/1ymTp3qTmXlypVyySWXSEdHR7ynxnoIJLQAAcCEvj19n9zDDz8cXulTn/pU+Hnkk9TUVPnEJz7hFlVWVsqzzz4b+bZ4uf+6y/NnIxNra2vD60RtyAsEEEAAAQQQQAABBBBAAAEE4hD461//Gk5Jdfvtt7stKioq3Iy4ODYPrzJY+ykpKZH58+eH+6JFi+RLX/qSrF69Opw73wbcWNFOGgJ+ECAAmOR38cUXX3RXkJOTI8cff3yPV3P66aeH33vppZfCz+2J955NH45t//znP90ib53Y93mNAAIIIIAAAggggAACCCCAQF8C3nTfo48+WmzwyuzZs90m3vK+tvfe99Y/1P14+4t9HD16tHzjG98IL37iiSfCz3mCQDILEABM5run575u3Tp3BVbd13IA9tTmzJkTfsvbxltw1llnyfTp012i0zfeeMNb7Ap//PCHP5T09PTwCMLwmzxBAAEEEEAAAQQQQAABBBBAIA6BPXv2yFNPPeXW/PjHPx71aAG20tLSOPYiMlj76etgJ554YniVbdu2hZ/zBIFkFug5YpTMVzVCzr2xsVHKysrc1U6aNKnXq7a/Ytgowbq6OtmxY0fUuhY4vOuuu+Tcc8+V0047Ta644grJy8uThx56yBUm+elPfxrOgxC1YS8vdu7c2cu7oR/c3gqWw8F6ojfz9lrkc28ZjwgggAACCCCAAAII+E0g8ntv5PNkvM729vZwPjd7Tjt8An/4wx+kra1NLD2V/b5p/h/72MfEimJajnqrunvdddf1eUKHup/I+265/SJfRx48EAiEX1o14Mj1InMC2vLI98IbDfET79zj/T063vWG+LTZ/TALEAAc5htwKIevqakJb+4lUQ0v6OaJFwC0nH6xbenSpWLTib/zne/I/fff734IL1iwQG6++Wb56Ec/Grt6n68nT57c5zreCsuXL5eioiLvZVI82jnTEEAAAQQQQAABBBAYSQLJ/h24sLBQsrKyJCUlRazQIe3wCXhVe0855RQ32MT87X7YSLtXX31Vfve738knP/nJPk/oUPdTX18fPoYVH+npc2C5/7xWXFwctV5k0RLbX0/78LYf7EcLSHqDaNavXx/X7r2BQ3GtzEq+FSAAmMS3NvIvcDZNt6+WkZHhVukp+m8/fP/xj3/0tRveRwABBBBAAAEEEEAAAQR8KdCuo8IqG1p9eW09XdSorKCkalB0qJpV2l27dq3b/Uc+8pGow9hrCwBaKioLZkWmropaUV8M1n5i9xv72gJsd9xxR3jxySefHH7OEwSSWYAAYBLfvczMzPDZNzc3h5/39MT7S4X91WuoW+w049jjWe4GL6+CTTvuawpz7PbD8doCrt5fPe2cI/2H43w4JgIIIIAAAggggAACQy3gp+/A27dvd9NQLQVSfn5+t3TltU1y5u3PdPueXxeuvPFMGZUbGiwyFNf4l7/8xe3Wfg+96qqrouw/8YlPuIIb9vusVfe96aabejyFwdhPdnZ2eP82QCbyc2DpslatWiX/9V//JStXrnTrTZkyxY1M9AbT2MLI57a/yH2Edz6ETyxfolnaLMDeCoFGnkJfKboi1+W5fwUIACbxvbU8fV7rblqv9573aD/QrMUzXdjbZqCP/Qno2Q+vwxGUHOi1dLedBf+S7Zy7uw6WIYAAAggggAACCCAQr0Cyfwe2/HNevjZ73l3raXl36/plmV3zUF23jaa77777HNVFF10ko0aNimKzVFAXXHCBPPzwwy4PoAUAuzuXwdpP5L4t0Ge9p1ZSUuLOK/b3PptC7rWhtPOO0d2jnYMdO/bculvXlsW7Xk/bs9wfAt3/1PPHtfn+KuwfYMubYK2viH5FRYUrAGLr9ic/n61PQwABBBBAAAEEEEAAAQQQQKC/Av/85z9l3759bjOv+m/sPrzl9jvts88+G/u2ez1Y++l25zELp02bJl/96lfdlOOFCxfGvMtLBJJXgABg8t47d+ZHHXWUe9y4caPYX0V6apHJQefOndvTaixHAAEEEEAAAQQQQAABBBBAYFAE7r77brcfG7hy3nnndbvPCy+8MDwy0Fs/dkVv+aHuJ3K/1157rQvyWW7Bt99+W+x36srKStm8ebP8+Mc/FhsBSEPATwJMAU7yu2lVlF544QU3uu/111+Xk046qdsrev7558PLSWIapuAJAggggAACCCCAAAIIIBAWGJ2dLq9/6/3h1yPhiV3zULSqqir529/+5nZdXl4u8RSutDx///u//ys5OTnhUxqs/YR32PnEAnzz58+PXcxrBHwrQAAwyW/tpZdeGk6U+tvf/rbbAKDlufD+YmI5F5YuXZrkV83pI4AAAggggAACCCCAAAKDL5CamiKFQ1gQY/DPOHH3+MADD4gVkelPs9z2FgS8+uqrw5sN1n7CO+QJAiNUgABgkt94q6R76qmnulGAv/71r12FosWLF0dd1S233CLr1q1zy66//npJS0uLep8XCCCAAAIIIIAAAggggAACCAymgDcIZfz48XLrrbf2uWvLu2d5AG27yADgYO2nzxNgBQR8LkAAcJhv8IsvvuhyDXinUVZW5j11y3/3u9+FX9uTa665Juq1vbjtttvEpvU2NDTIOeecIzfeeKMb5Wev//SnP8mdd97ptpk1a5bccMMNB23PAgQQQAABBBBAAAEEEEAAAQQGS2DLli3y0ksvud1ddtllcsUVV/S56xUrVrjfbZ955hnZtWuXTJw4UQZrP30enBUQGAECBACH+Sbfdddd8vvf/77bs7AfmN4PTW+F7gKAxx57rNx///1i1ZOqq6tdANBb33u04N/jjz8ueXl53iIeEUAAAQQQQAABBBBAAAEEEBh0ARu119HR4fZ7+eWXx7V/W88Gt1gKqz/84Q/y9a9/3Y0GHIz9xHUCrISAzwUIAPrkBl900UXyr3/9y/3AtECfDZ22JKszZsyQD3/4w/KlL31JsrOzfXK1XAYCCCCAAAIIIIAAAggggECiCtxzzz3u1KzQhqWsiqctWbJEbLrwnj17xLa3AOBg7See4w9knSeeeEK2bt3a56ZXXnllXEVQ+twRKyBwCAIEAA8BbzA2tSm+sdN8B7rfKVOmuNwK8eRXGOgx2A4BBBBAAAEEEEAAAQQQQACBngRsFtumTZvc2x/84AclNTW1p1Wjltt6tr5VAV67dq2bDTcY+3n99dfl+OOPjzrWYL24+eab49qVFe+MpwpyXDtjJQQGKBDf/4kD3DmbIYAAAggggAACCCCAAAIIIIDAyBHwinbYFVv+v/60yPWHYj/9ORfWRcBvAik6nz40Md9vV8b1JLSATVGePHmyO8cdO3bIpEmTEvp87eSsqMqyZcvceVqxlaysrIQ/Z04QAQQQQAABBBBAAIFDEfDTd+ANGzZIa2urBINBmTlz5qGwsC0CwyYwkM9xMv7+PWzAPj4wU4B9fHMT6dLmzZsXdTotLS1Rr3mBAAIIIIAAAggggAACCCCAAAIIIDA0AkwBHhpX9ooAAggggAACCCCAAAIIIIAAAggggEBCCDACMCFug/9PwpK4RrbIIciRy3mOAAIIIIAAAggggAACCCCAAAIIIDC4AowAHFxP9oYAAggggAACCCCAAAIIIIAAAggggEBCCRAATKjbwckggAACCCCAAAIIIIAAAggggAACCCAwuAIEAAfXk70hgAACCCCAAAIIIIAAAggggAACCCCQUAIEABPqdnAyCCCAAAIIIIAAAggggAACCCCAAAIIDK4AAcDB9WRvCCCAAAIIIIAAAggggAACCCCAAAIIJJQAAcCEuh2cDAIIIIAAAggggAACCCCAAAIIIIAAAoMrQABwcD3ZGwIIIIAAAggggAACCCCAAAIIIIAAAgklQAAwoW4HJ4MAAggggAACCCCAAAIIIIAAAggggMDgChAAHFxP9oYAAggggAACCCCAAAIIIIAAAggggEBCCRAATKjbwckggAACCCCAAAIIIIAAAggggAACCCAwuAIEAAfXk70hgAACCCCAAAIIIIAAAggggAACCCCQUAIEABPqdnAyCCCAAAIIIIAAAggggAACCCCAAAIIDK4AAcDB9WRvCCCAAAIIIIAAAggggAACCCCAAAIIJJRAMKHOhpPxrcC8efOirq2lpSXqNS8QQAABBBBAAAEEEEAAAQQQQAABBIZGgADg0Liy134I1Fc1SV1uUz+2GJ5VGxubpa0xRVICHcNzAhwVAQQQQAABBBBAAAEEEEAAAQQQGIAAAcABoLFJ/wXWrl0btdHOnTtl8uTJbtkDN62S0bnbot5P3Be57tTufWWljBqbLaNKsqWg83HU2CwpKM6WtIxA4p4+Z4YAAggggAACCCCAAAIIIIAAAiNOgADgiLvlXPBgCDTWtcrezdWux+4vZ1SGBgY1GGiBQQ0IWmDQgoX5RVkSCJJ2M9aL1wgggAACCCCAAAIIIIAAAgggMLQCBACH1pe9j0CBukqd0qx913uVUVefkiKSV5gZMWpQA4M2glC7LU9N1RVoCCCAAAIIIIAAAggggIBPBJ577jlZunRpt1eTlZUlhYWFcswxx8iHPvQhueqqqyQjI6PbdbtbWFNTI+PGjZP6+nr39s033yxf+9rXuls1allra6ukpaVFLfNepKeny6hRo2Tu3Lly7rnnymc/+1kpLi723u728ZRTTpGXXnqp2/d6Wmjnnpsbml3W0zosR2CwBQgADrYo+0OgB4EOTR1YXdbourxzIGqt1GCKFOgIQQsG2ujB8PRifZ0zKl1SLHpIQwABBBBAAAEEEEAAAQR8ItDQ0CCWGsr6448/Lrfeeqs89thjMnXq1Liu8M9//nM4+Gcb3HPPPXEFAHvbeXNzs+zfv9/1559/Xm655Ra5//775ayzzuptM95DICkECAAmxW3y90le8e0TZdLESQl/kY2NjfLsM89IW0uKHD3rfVJf2SqV+xukan+9VO6rl9qKgRcyaW/tkIq99a7HQgQ1p2BBcWi0YDg4qFOKCzRQmJmTRnAwFozXCCCAAAIIIIAAAgggkHAC1157rXzhC18In5cF2t5++235yU9+4oKAljf+4osvljVr1kgg0Hde9bvvvtvty0bS1dbWun2tXr1ajjvuuPAx+nqyaNEi+dWvfhVezUbmbdiwQX7xi1/IypUrpby8XC699FKxczviiCPC63X3xM75jTfe6O6tg5ZlZ2cftIwFCAy1AAHAoRZm/30KZGanuUBWnysO8wodqa2Smi7aO2TyvNFiQ9YjW0tzm1SXNmhQMBQQrNLgoPe8oaYlctV+PW9tapPynbWux26YkR0MjRq0PINu9GAoMGjP07P43zvWi9cIIIAAAggggAACCCAwPAIlJSUyf/78qIOfeeaZ8qlPfUqOPvpo2bp1q7z11lvy17/+VS6//PKo9WJfbNu2TZYvX+4Wf//735fvfve7UlVVJRYU7E8AMCcn56BzWrx4sVx99dVuWvLDDz/sgos2OvF//ud/Yk/joNex13fQCixAYBgFiBAMIz6H9pdAWnpACifmuh57ZU0NraGRgi442DVq0EYQNut7A21N9a2yf2u167H7yMrX/BU2ndgCg52Vim3UoI0mDOq50hBAAAEEEEAAAQQQQACB4RbIy8uTb33rWy7fnp3LU0891WcA0Kb7dmiOJcvlZ8E6Cxz+5je/kfvuu09++tOfSjB4aKEOS8H0ox/9SCwA6J2Te8J/EEhigUP7vyKJL5xTTxyB3d/8lnQkQQLUtrY2maDD1NszM6Rq715p179SZWpy2IAmie2rZehovJIp+a5Hrmv/aDXWtrgpxBYMtBGDoSnFoSBha0t75Or9et5Q3SzW92ysit5O0wnmjrZKxZZvMBQctMCgPc8rytTh9lQqjgbjFQIIIIAAAggggAACCAylwIIFC8K737FjR/h5T08sAGjt/PPPd4VELAhoAUCbVvzEE0/IhRde2NOmcS+fOXOmZGZmiqWCiuec4t4xKyIwTAIEAIcJnsN2CdS9+KLU9lCFqWutxHjm1WmqWL1GKjpPKW3CBMk4aq4LBmYedZRYD+rw9ngKd9g6WXnpro+fER1I7GjvkLqqJpdn0HIMusCgBQn1eXVZg7S3aVWRgTTdrPZAk+s713tXEdqRVSK2IKAbMVhswcHOwiQ6gjB3VIakUKl4IOJsgwACCCCAAAIIIIAAAr0IWPVdr/VUodd7f8WKFfLee++5lx//+Mfd4+mnny6TJ092gTqbBjwYAcDU1NRwLsK+zsk7Nx4RSGQBAoCJfHc4t6QQaNm9W6zXPvV0+HwDY8a4QKCNEMzsDA6madLYFP1HJN5mwbbc0ZmuT5o9Omqz9rZ2qTnQGBEc7Bo9WF3eKDLA2GC7Bh0td6H1bVIedcxAWmqoGImbTuxVLA6NIMzKoxhJFBYvEEAAAQQQQAABBBBAIG6BdevWhdftqwqwV/yjoKBALrroIredDay48sor5eabb5ZHH31UKisrZVQcM7XCB+3miY36q6urc+/0dU7dbM4iBBJOgABgwt0STsgPAm0HDoiNbLTutVRNMJsxd46OFNRRghYYnHeUZEyfLikDGP2YqtN0C3SEnvUp8wq9Q7jH1hYrRmLBQS1G4qYUh0YN2gjCuqrmqHX786JNpyMf2F3neux2aZmBzinFGhjszDdoU4q9SsWx6/MaAQQQQAABBBBAAIGEFGjXFDwNBxLy1IbspLLGaKXD+AcqDPZ5WKolqwTstd4KgDQ3N8v999/vVr3sssvcFF1vOxsNaAFAm7L7wAMPyL/92795bw3o8aabbgpv19s5hVfSJ1bVuK82RgeLTNBZZDQEDrcAAcDDLc7xDhIo/OxnpHh09Ai3g1ZKgAUtLS2yUUvCByurpETLzLds3Cgd+g9QvK1d/3rUsOp1171tUnSoe4bllrBRgjZ9WAODGbNnS2pMhWFv/Xgeg2kBGTMhx/XY9ZsbtRiJVSp2U4pDowa95411A69U3NLYJqXba1yPPWZmbtrBwUGbWqzBy7QMipHEevEaAQQQQAABBBBAYBgFLPj3kyOH8QSG4dBf3SSSU3TYD1xaWuqKd3z729+WNWvWuONboO2UU07p8Vwee+wxOaCDLax503+9la0C78KFC+WNN95w1YAHEgCsqalx04tvv/12tw/b92z9/ezzn/+8d5geHy2QGZnLsKcVP/OZz8hdd93V09ssR2DIBAgADhktO45XYIwmbC2aNCne1YdtvYaGBjmwbJk7/sJzzpFMrSzVtHmzNL6zThrXvSNN7nGdWKAv3mYBxMa1a10Pb6N/fUufNi0cEPSmEAd0iPuhtvTMoBRPznM9dl8WAHQjBjU42FWQJBQsbGlqi1097tdW5GRvbZXs3RxTjET3kKN5BcN5Bl1REi1GoiMI84uyJBAcvr9Cxn1xrIgAAggggAACCCCAAAJxCXzve98T69217OxsF2Szyru9NW/67yT9/fGMM844aFULCloA8KWXXpLN+rvadJ1x1Vt7+umne8zdbjkAL7nkErnjjjsOeTpxb+fAewgcLgECgIdLmuP4TsCm7mbqX4OsywcvddfXoVMGWjRXRKPmsGhc+07oUZ+3lUfn0+sVQ/fRvGmT69Wav8JraRMnupGCGTZ92OUWnKfFRop7/AfL2y7ex8ycNBk3rcD1yG2sUnG9VhP2qhO7qcU2glBHElquwLbWgVcqrqtsEuu73q2MPKRek0heoRYjsWnEnTkHvYrFuWMydYaCrkBDAAEEEEAAAQQQQAABXwjYyL1///d/l96KbZSVlcnf//53d72W76+7oou2/Gtf+5q06+9UVin4O9/5zoB9JurvX1/+8pdl/Pjxce0jEAhIa2trXOuyEgLDIUAAcDjUOaZvBazIR/qUKa7nn3eeu04LoLXuL9WRgjrST4OBTRYc1NGCLbt29cvB1rde8+RT4e0ChYWhYGBnTkELDKZp9av+FBsJ76yHJ/YPa05BhusTZkZP1baiIbUVjVK1r3M6seUd1OcWLLRiJFbJeCBNybTScaPr8k50DpbUYIoU6AjBAhsxGBEctNc5o9K7/SIwkHNgGwQQQAABBBBAAAEEEBg8gWuvvVa+8IUvuB1aoGznzp3y4IMPukDdyy+/7Eb0vfbaa1JcXNztQe+77z6xtEzWYqf/ehtYsO6ss86SJ598Mq4A4KJFi+RXv/qV29x+b9u7d688//zz8vOf/9xVFD733HPlqaeekiVLlniH4BGBpBUgAJi0t44TTxYBC6CljS1xPW/p0vBpt2llqsb16zunEFtQ8B1p3rJF9M9V4XX6emIjC3ssNuJyCmpeQc0vONBiI30d30bi5RdmuT75KE0eHNFsZGCNBgEtx2CoIEkoMGivayuaItbs39P21g6p2FvveuyWQc0pOKoklF/QphZ7owa9YiTd/ZUwdh+8RgABBBBAAAEEEBjBAlYQw3LijaRm13wYWklJiViePq/ZqL8LL7xQlurvSNdcc41s3bpVPvvZz8ojjzzirRL16E3/tYVHH3101Hvdvdiks6psKvDJJ5/c3dtuWY4Waow8J8vhd/bZZ8tHPvIRF/SzKsA2qtCKe+Tm5va4H95AIBkECAAmw13iHH0pENCy9Dn6FyfrXmvXPINN774bmjqsAUEbKdj03nvS0fmXLm+93h57LDYya1bn1OHQFOJDLTbS2znYe5bDz43Q01F6sa2l2SoVh/ILxgYHG2oGXoykVXMVlu2odT32mBnZwc5Rg52Bwc4RhBYctNyINAQQQAABBBBAAAEEXDXcYSiIMZLlP/nJT8qjmvrooYcekr/97W/yzDPPyJlnnhlFsk5nUa1atSpqWTwvLGjYWwCwp31YgPEHP/iBmwK8bds2ueWWWw5pOnFPx2E5AodTgN96D6f2CD7WvHnzoq7eG7odtZAXrvpvlv4lzLrXLPjnio2EcwqGCo6019d7q/T56IqN6F+tGiPL0tt05elabGSujRIMVSDOnDtHBqPYSF8nlJYekMKJua7HrtvUoJWK3VTizmIklm/QXmu+wWZ9b6Ctqb5V9m+tdj12H9n56RocDBUgcaMGLfegG0mYJUE9VxoCCCCAAAIIIIAAAggMncAPf/hDefjhh8Uq6d54442yYsWKqINFjv678847JS8vL+r92BdWZdcKfDzwwANiFX0zMjJiV+nztU1X/tnPfibbt293AcDrrrtOxow5PKMl+zw5VkBgAAIEAAeAxiYIHE6BqGIj8kF3aFdsRP8hcsVGrPqwjRa0YiMHovPl9XqeVmxkoxYb0X5wsZHOqcOu4MhRg1pspNdz0jczsoJSMiXf9ch1LSeHVRQOTSkO5RysctOLQ1OLW1vinzoduV97bkVOrO/ZGFOp2IqRjM6MCg66QKEGCPOKMiUQoFJxrCWvEUAAAQQQQAABBBDor8Asna1k024tz9+rr77qcvjZVFxrVtDjj3/8o3tu04Y/97nPuee9/ScYDLoAYKWmXbJRhR/+8Id7W73b99LT0+XrX/+6fPGLX5Samhq57bbbeqxi3O0OWIhAggkQAEywG+LX01m7dm3UpVnC18larII2MAFXbGTqVEnXnn/++W4noWIj+0PBwM6AYJMVG9m9u18H6So28mR4u0BRUVexEc0paCMGXbERK9d7mJrl78vKS3d9/IxRUUe1YiN1VU3RwUEdMWjBwuqyBmlvG1gxEtHNag40ur5zfUXUMS3/oQUB3TTnYitIorkHXVGSbMkdlaGFWA6fTdSJ8QIBBBBAAAEEEEAAgSQUsJF/f/rTn8R+r7Hpt14A8Nlnn3UFOeySLr/88riu7IILLpDs7Gyp11lTNnpwIAFAO9CnP/1p+f73v++Kg1hhkK985St9jj6M6wRZCYFhECAAOAzoHBKBoRAIFRsZq8VGxspBxUY6Kw+7EYP6vHnzZtF/WeM+jbayMql74QXXvY1SNQlu5pw5ktEZELSpxBlHTpcU/Wvb4W4WbMvVkXrWJ82JPnp7mxYj0SCeTSG2gKAbNdiZf9CWW5BvIM0qIFfpPq1vk/KoXQTSUqWg2JtS3FmxuDPnYFZeGpWKo7R4gQACCCCAAAIIIICAuGIcF198sSsCsnz5cnnxxRfllFNOcQE8z+eyyy7znvb6aMG/8847T/7yl7/IE088IaWlpT1WF+5tR5mZmXLDDTfIV7/6VamoqJBf/OIX8o1vfKPHTaxYSDxtypQpBBLjgWKdQRU4/L+pD+rpszMEEOhLwBUbWbxYcrR7zfIHNnYWG2my4KDmF2zasKF/xUZqa6VeE/Fa91qKDpPPsGIjllPQAoM6hdgVG9F/OIerpeo03QIdoWd9yrzCqNNobbFiJBYcDFUqtmBeaIpxvdRXNUet258XbTod+cDuOtdjt0vPDHQWIwnlGfRyDtoIwozstNjVeY0AAggggAACCCCAwIgR+OY3vxmuAmwj7yyAZ92a5ZWfowMQ4m02WtC2bW1tlXvvvVeuv/76eDeNWu/zn/+83HTTTXJA0y1ZTkDbjwUYY5vlL7QqwvE0K3piFZBpCBxOAQKAh1ObYyGQIAKp+g9W9rHHuu6dkhUKccVGInIKWnDwkIuNBAKSocVGMjrzCVpQ0IKDgfx879DD9hhMC8iYCTmux55Ec6MVI+nMNdhZhMQLDjbVDbwYSXNjm5Rur3E99piZuWkSCgh2TSd2U4s1eJmWQTGSWC9eI4AAAggggAACCPhL4IQTTnBTf5988klZtmyZ3HrrrVKrAw+sxTv6zxOxAJsV/2hqanKjCAcaAMzVmU+27Xe+8x03kvCXv/ylqw7sHYdHBJJFIEXn1w9wAlyyXCLnmYgCkTkAd+zYIZMmTUrE04w6p4aGBvePkC0855xzJCsrK+p9P74IFxvpzCnY2BkcbNPh74fa0vSeu2DgvFAFYgsQppWUHOpuD8v2rhiJBgW96sRuBKGrVtwgLU1tQ3IOOZpX0AUDbSqx66EpxvlFWRIIUoxkSNDZKQIIIIAAAgiIn74Db9AZLzYazApEzJw5k7uLQFIKDORznIy/fyflzUnwk2YEYILfIE4PgeEUiCo2ool0rbliI/v2abERnTq8TqsPdz627t7Tr1Nt0UIw1mv0r3tec8VG3NThUFDQRgoe7mIj3rn09mgj9cblFsi46QVRq5mNVRN2gcF9odGDoVGDDTrVuEHaWgdeqbiuskms73q3MuqYVoclrzBUjKQgJjiYOyZTrFgJDQEEEEAAAQQQQAABBBBAYGQLEAAc2fc/Ma7+f44WKUj86Y2Wxe5CLUHfGsiSYMVxIpOOF5lwbKgX6AjGw1gRdzhvnCs2Mm6cpGnPO3Np+FRadVRg0/r1nVWILTioxUa2bOl/sZHlWmxEu9dS8/JcsRELBrppxJpfMGP68BQb8c6pp0ezySnIcH3CzNFRq1nRkFotOuJNKw6NGrQiIlqpuLxRrJLxQJqN4a4ua3Rd1h6I2kVqMEUKdISgVSoOBQf1eWcxkuyCdIqRRGnxAgEEEEAAAQQQQAABBBDwrwABQP/e2+S5svYWEU2YmujNxlFZmDLQWiOy9flQ9046u6grGDhhYeh53vgRExQ0huDo0RKMLTZSV6fFRt4LjRTUgGCTjRbUqRfSovc8ztZeUyP1K1e67m2Sork8XLERl08wVHDEXqcOY7ER79x6erSReDZd1/rko8ZErWYjA6vLQhWFQwVJOisWa3CwtqIpat3+vGhv7ZCKvfWux24X1JyCo0o016DmF7SpxV5g0B5thCMNAQQQQAABBBBAAAEEEEDAPwIEAP1zL7mS4RSoLxPZqFNZrXstd2xEULBzpGBucuS48y7hUB9Tc3Ik+zgtNqLda67YyKZNnVOHNSCo+QVt5GC/io1oIt/Gt95y3duvdBYbsQrEXQVH5iREsZHwOfbwxHL4jR6X43rsKi3NVqm4qzpxpRYmqdJ8gxYobKiJP5Aau99WzVVYtqPW9dj3MrKDnaMGOwODnaMGCzRgmJ7JPxuxXrxGAAEEEEAAAQQQQAABBBJdgN/kEv0OcX7JK1C7T+S9J0Ldu4r8iSLjO0cIuunD+jxHRw+OoJaSnh4q/qGj97xmxUaat20TqzpsAcFQXsF10q9iIzqKtGnDRtflkb95u3Y5BL3Kw6HHoyRYXBx+P9GfpKUHpHBiruux59rUYJWKNRjoAoJdowYtSNis7w20NdW3yr4t1a7H7iM7P/2g4GCBjiAsKNap8VpVmYYAAggggAACCCCAAAIIIJB4AgQAE++ejLwzuvJBHSmno+USvDXqqLMVK16R7OYyOXZsiqTtf0tk9xqRxuiiDL1eRvUuTdim/d3Hu1YrOEKvPyYomBWdP65rZX8+s2IjGdOmuZ4fWWxk716XS9ALCFrRkX4XG9Eq0y3aa5YtC+MFios6g5A2fTg0hdiqElsOv2RqGVlBKZmS73rkeVsxEhsdGKpSbKMFvVGDoZyDrS0DL0ZiRU6s794Q87lXurzRmZprMJRz0KYSu+f6mFeUqQM0qVQceY94jgACCCCAAAIIIIAAAggcTgECgIdTm2N1K/C9LQ9KXm1et+8l0sI2HWG2r2OfZKVnyUVTL5LTTr9eijILRSq2hgKBFgy0vudNkabq+E+9aruI9XVdo9Zk9NTo6cPjjxHJLIh/nz5Y0xUbGT9e0rTnnXlm+IpcsREbKWh9rY4W1MfmrVv7V2yktEzqSrspNmI5BV1ewVDBkUQtNhLG6OGJ2dlIPevjZ4yKWsuKjdRVNXWNGtQRhKEpxaFKxVasZEBNN6vRIifWd66viNqFy3+oIwS9gKDLPaiFSSxImDsqQ1KoVBzlxQsEEEAAAQQQQAABBBBAYLAFCAAOtij767fACztfkLT65Co6sOrVVSKviswYNUMWT1gsi8cvluNnf0Oy07JFdDqrHNjcGQx8oyso2Fwbv40FFa2v/WvXNoUzuoKCNo14vFZPzkj8wGnXBQzOM1dsZMkSydHutXav2IhNH9ZRghYUtOnA/S428tprUq/da67YyOzZ4SnLmfM0v6AVG9EiJMnaLNiWqyP1rE+aE30V7W3tLoBXuU+nE3uBwc78gxbYkwHGBi2o6KYp61TlbVIeddBgWqoUT8lzgcoJGqwcNz1fMrKT6+dB1AXxAgEEEEAAAQQQQAABBBBIQAECgAl4Uzil5BHYWLlRrN/zzj0STA3KwuKF4YDgUfM/JIGjPxy6mHatclyuASlvlKAbKfgvkdaG+C/Wtrf+1p87t9E5l0WzuoKCllNw3AKRdA1CjrDWY7GRjRtDIwWt+rCNGNRiIx319XHrdFixkX/9y/XwRq7YyHSdOqyjBcMFR+ZKIC/5g7GpOk3XqgJbnyI6ujWitbZYMZJGFxgMBwd1arE9r69qjlizf09tOvKejVWur9bwoOjHunBCrkyYUeCCguP10YKVNAQQQAABBBBAAAEEEEAAgYELpGiuqAGO6Rj4QdkSgZ07d8rkyZMdxOxbZ0vaGP+N+MlLz5OTxp0UDghOzg9db/jut7WKlL0XHRTcq3kF25rCq/T7SYrmWSvW4hqROQXHzhdJI4Bilh06jbt523Y3SjCy4EhbZWW/qWM3SNPPs8sn2DmF2KYSJ1Oxkdjr6c/r5kYrRtI5atAVJQk9t+BgU51+zg+x5RVmajBQA4JHjtLA4CitlpzNtOFDNGVzBBBAAAEE4hVoaGiQZZ25lM855xzJysqKd9OEW2/Dhg3S2toqwWBQZs6cmXDnxwkhEI/AQD7Hkb9/79D86JM0/zlt5AkQABx59zwhrjjyB9DUb35a0kYly+ipDknN3CvB7E2SEuhfoG5i7kRZNH6RCwhaYHBUZnRuNndj2lpE9q/rCgru0SnEe9/WacW6fKBNRyZKiQUFdYSg10uOEgkm7zTWgVJ0t539DaTVKzbSmVPQRgu27tnT3er9WmYBwAwbKehyC2qxEZ1CnDZxYtIVG+nXRces3FjbEppObIHBziChV7W4tUlHxg6gZeQEXTDQgoIWECw+Ik8CQQ1+0xBAAAEEEEBg0AUIAA46KTtE4JAECAAeEt+I3pgA4Ii+/cN38ZEBwInX/k6C+UXDdzIDOnKbpGbtlGDOBgnkbJRA1nYN6sRfWTVF5zkeVXhUOCB4bMmxkh5I7/5MWjXQuP+drqCgTR+2IGH7IYysStURl2PndQUELTBoQcKA/0Zido/a91IrNtKoOQVDIwV1+rA+b96mU1QPcdB0an6+ZM6ZEwoKakDQgoPpWgE5Rf8SPZKaBV5t6nDpjhrZs8mmAFfKvq3V+rHu/6D0gOYRHDs1PzRKUAOC46cXSLpWSKYhgAACCCCAwKELEAA8dEP2gMBgChAAHEzNkbUvAoAj634nzNVGBgC//OunJL9oXMKcW08n0qbTBbZu3y5aH0F21AelQXOXhVtqkwSyN3cFBDP2h9+K50lmIFOOH3t8OCA4c/RMSbXpvD21Fi3IsG+tBgVXa9dRghYULNWgYEfEOfW0bU/LAzoi0HIIeqMEbRpx0WwNChJI8chCxUbe1WBgKCDoio1onsH+FBvx9hX5GC424kYLalBQH5O92Ejk9cX73PIMlm6rkd0aDLSg4F7tTfX9D3RrEWQpnJQbNUowR6sN0xBAAAEEEECg/wIEAPtvxhYIDKUAAcCh1PX3vgkA+vv+JuzVRQYAkyUHQeSXnzPOer9sLG+WV7eUy6ubD8iqrQekrrlrOmNKsEpHBm7QgKCODtSeGuxHBWC9a2Myx8hJ4zV/oFYXtirD43LiCJA2a3ELyyEYWWjEcgwOtHSrfXqCmuPFqg2Hg4I6UtCqEacG7F2aCrQ3N0uzFRuxCsResZF33+1XsZFuIa3YyJFHhkYKRhQcCeTmdru6Hxd2aPXgA3vq3OjA3RtDowRrK/o39d5zyS+yPII6OvDIUHERl0fQIoU0BBBAAAEEEOhVIPI7MDkAe6XiTQQOi39I1ccAAEAASURBVAABwMPC7MuDEAD05W1N/ItK9gBg7Jef1rZ2Wbu7Wl7bcsAFBe2xWgsjhJrmDczY6wKBbspw9hYtYNC/nH7TCqa5YKDlEDxh3AmSmx5nEKip5uCgoFUSPpRmxx4XExQcM12Dgr2MWDyU4yXhtqFiI9s6A4Kd04g1v2BbVdUhX03aEUd0BgVDIwVdsZGiZJtCP3CGmgONUQHBA7vrBrSzzJy0cGGR8TMLpHgyeQQHBMlGCCCAAAK+FyAA6PtbzAUmmQABwCS7YQl0ugQAE+hm+PlU5s3TfHMRraWlRewHl7VkHAEYGwCMuDT3tE1HLq3fW+1GB9ooQQsIVtR3Bv1SWjVn4LZwQDA1c5fmD4w/71kgJSBHFx8dni48v2i+pFlOv3hbowah9rwZMVJQpxBXbIl36+7Xy8jXkYLHRI8UHD1V9MK6X38ELnXFRrSwiE0bjpxCbAVIDrVZsRGrQBwuOKLPR0qxkca6FjdVeM8mnTasowT3bRtYHsGg5RGcZnkEdZSgFhcZN408gof6uWR7BBBAAAF/CBAA9Md95Cr8I0AA0D/38nBfCQHAwy0+Qo830gKAsbe5XQOCG0trNSBYLitslKBOGy6r7ZzKmFqvU4U3dQUE0w/Ebt7r65y0HDcq0KswPC1fC0r0N/BWr8eMDQpWbe/1uH2+aVWOLY9g5PThgskEBWPgWg8ciB4pqNOIB63YiKs+rFWIO6cQu2IjOrXYz83yCO7faoVFKmX3BssjWCnNjV3T8+O9di+PoFUZ9oKCOQXkEYzXj/UQQAABBPwjQADQP/eSK/GHAAFAf9zH4bgKAoDDoc4xxW9TgPt7S2002OayOhcIfM3yCGpQcE+VFvbQlpJWHs4daDkEUwJadaQfbWz2WJc30AKC1guzCvuxdcSqdWWhAiN7tMCIV2ikelfECgN4mq3nEhkQHK8BwvwJBAVjKNtq66Tp3fVdOQWtGrEVG9FCNIfSUjIzJWP2rK4pxHN11OAsLTiT4d/AlgXfbZqwVRm2brkE6yoHmEewOEsmWA7BmaFcgqPGZvc/2H4oN5BtEUAAAQQQGAYBAoDDgM4hEehFgABgLzi81asAAcBeeXhzqARGegAw1tUCgjsONOjowFBREZs2vLPCAn/tYlOEQ8VENripwymp/RvNNHv07HBA8Lixx0mWFfYYaKvZpyMF34iYPqzBwVpddigtpyQ6KGgBwryxh7JHX25rxUaadNp8k5tC3FlwxIqNNPQvQHwQTjAoGdOnuynEbqSgjhrM0O7XYiP2/1ooj2CoqIgFBCu00MhAWlae5hE8MjRl2B6LjsiVQIBcmAOxZBsEEEAAgcQVIACYuPeGMxuZAgQAR+Z9H4yrJgA4GIrso98CBAD7JttV2aC5A72A4AHZoiMGJaVZAlpExBUTsQrDmf3LH2e5Ao8rOU4WTVjkiorMGTNHAodS0VeDKVKzp2uEoKtAvFqkvrzvC+xtjTwdFehGCkZMIc4ZOYUueqOJfC9cbEQLjLjcgut0pKBOIR6UYiNTrNiIFhqxacSdU4iDhQMcTRp50gn4vLG2RfZsDgUEbZTg/m010t4Wf15O75KC6ZZH0KoMF+hIwVEydnq+pGcGvbd5RAABBBBAICkFCAAm5W3jpH0sQADQxzd3iC+NAOAQA7P77gUIAHbv0tvSfdWNbqqwFxTcsL9WpwfXhHMHBjQgmJpW3dsuDnovP71ApwmfFB4hOClv0kHr9HuBBQWrdkaPErRRgw0V/d5V1AaWPzAyp6BNH84eE7UKL0RcsZHdu7uKjXSOGGzdd4gjNRU3WFLiAoKu2IgWGrEAYdrECb6bBtvarHkEtZiI5RC0XIJ7N1UNLI9gaooUTcoNBQQ7cwlm56fzMUUAAQQQQCCpBAgAJtXtSriTfe6552Tp0qXh88rNzZV9+r00Ozs7vKy7J/a5GzdunFRXd/1+8+yzz8oZZ5xx0Op1dXVyzz33yN/+9jd58803pby83H0nzs/Pl6lTp8qCBQtk8eLFct5558nkyfo7RUy75ppr5Pe//33M0p5f2vft7373u/K9732v55XieOeTn/yk/O53v4tjzehVCABGe/AqfgGGJsRvxZoIDKvA2PxMufiYCa7biZRrERGrLvzqlgWyQouLvLupWvMHlmpAcENohGD2Zg0QNvd6ztXNVbJs2zLXbcWJOZPk5IlLXEDwhHEnSEFGQa/bd/umVU8Ypf+wWj/q4tAqFhSs3BYdFNytlYibtCJxvK1qhwYWta97tGuL0VO1+nDEKEGrRJw1quv9EfjMCsBYBWDree9/f1ggsthIo+UU9IqNhNfo+0nr/v1Sa/3558MrpxYUhEYJeiMF9THZi40E0wMyYeZo1+1CLY9g+a5aV2XYVRveUCl1Vb3/v2Xbdeh2pdtrXP/XMxoU11ZQkhUqKqK5BK3AiL3ud9Eetyf+gwACCCCAAAIIJJ9AbW2tPPzww3LllVf2evKPPPJIVPCvp5VfeeUVueKKK2T79oMLGJaVlYn1VatWyW9/+1sZO3as7N3bvxlUPR2X5QgkowABwGS8a5wzAipQmJsh5y8Y77qBVNY3y8qtFa7SsBUVWbvhgKRk7ggHBFOzdmqgob1Xu111O+WB9x5wPUVSZc7ouXLKpFBAcGHxQkkLpPW6fY9vWlDQgnXW530wtFq7nkvFloig4Buh/ILNtT3u5qA3KrbqPrS/83DXW2OOjMgpuFADhBoUzMjren+EPguOGSO5p5zsukcQLjYSnkK8rt/FRtqrqqR+xQrXvf2mZGVJ5qxZ4kYKusDgvFCxkfTkHP2WqiP5iifnuX700knuL8o15Y3hoiI2bbhib713+b0+Vu1vEOvrX9ap89pcHkEbHWgBQS0uYiMGU8kj2KshbyKAAAIIIIBAcgpkakG6xsZGN1qvrwCgjeiz5m3T3RW/9957cu6550pNTY17++KLL5bLL79cZun30HT93mnBPxsR+OSTT4qNHoyn/fOf/5QJEzQdUR/tC1/4gjtWd6tZ8PJb3/qWe+sHP/iBXHLJJd2tJqNHj+52OQsRGCoBAoBDJct+ETjMAqOy0+Xso8a6boeubmyR1y0g6EYJlstbG/WvXZmbwlOGUzO0ym8vrUMLkKyrWOv6r976laSlZsixxcfL6ZNPdiMEZ4yacWgjl1K1WEKhBuusL7g8dCYWFCzXarcul+Ca0OPef4m0xBdccTs5sEnE+tsPdl6dBh+LZkYEBY8VGbdAJD2nl6sfGW8FcnMk+/jjXfeu2BUbeW+DTiHWUYI2fdiCg1ZsRL+sxdusMEmDftmyHm5WbOTIIztzCur0Yc0rmDFHc1DqNJBkazZiL78oy/XZi8a702+obe4cIRjKJVhqeQR1BGBfraGmRTavKXXd1g1mBGTctPzQKEHNJTh2KnkE+zLkfQQQQAABBBBIDgEL0D3wwAMuIGcj8WyKb3dtv844WbZsmXvLgmf3339/d6vJN7/5zXDwz0b42VTe2Hb22WfLV77yFSktLXXHjn0/9rUFD6dOnRq7+KDXJZoax3p3zUYcem2izsqZP3++95JHBIZVgADgsPJzcASGTiA/M02Wzilx3Y5S19Qqq7fbCEGbNlwub27eKu2Z73VWGNb8gcHeK6G2tOuU430vu277yw2OlpPGLZIzp56ieQQXSUl29/8A2rpxNwsKFs8K9WM+GtqsrVWk7L3o6sN73xJpjTcgpUEY2976vzq/PKTYceaEgoLeFOJx+g9zWlbcp+rXFVP1r6VZ8+e57l2jKzaydas06rRhmz4cKjiyTmz0X9yttVWaNJBovUqnfXgtfcoUyV68SKcrny05J54gKUk6SjArN12mLyx23a6txfIIbql2OQT3aKVhKzLS0tjmXXaPj61NbbJzfYXrtlKKG31oeQS7qg2TR7BHPt5AAAEEEEAAgQQWOOecc2T58uVuGu59990nX/7yl7s9W3uvVb87WoDQAnjdBQDb2trk8ccfd9u/733v6zb4F7nz4uJi+eIXvxi5iOcIjDgBAoAj7pZzwSNVICcjKKfOLHbdDBpbTpI12ytdMHDF5jJ5Y5dWj818NxQQ1ErDKakaeOul1bZWyNM7/+G6rVaUcYScPGGxnD3tVLH8gdlpvSf27WXX0W8F9MfU2KNCfeGVoffaWkRK10eMFNTpw/veFmlrjt62p1cdOtJw/zuh/sYfQ2ulBERK9DiRhUbGzrMhWT3tZcQsTwkE3Og9G8FXcNGF7rq9YiMNlk/QFRrR4KA+9rfYSPO2bWK98k/3S2penuSecYbknXWW5J56iqTmJO8ozTTNIzhx9mjXDay9rV3zCNa5gKArLqLThuur+/68Wh5Bq0ps/c2ndzj7UWOz3ZRhLyhYUEweQQfDfxBAAAEEEEAgoQUC+p3yYx/7mPzsZz9z04B7CgDefffd7jpsmrBt012zEX1WKMTajBkzuluFZQggECNAADAGhJcIjBSBzLSALD6y0HWRWdLUepL8a2eVKyzy8ua9smbvGmlNDwUEUzN363Tf3qczljVtl0e2WL9fUiQgk7LmyKmaP/D8GafJ/KL5EkwdxB83lovQpvFaP+4ToVvWqsEUC+pFTh+21+29BzLD97tDR2ft05GF1tfcE1qcqsexIGBkUNCChAPNhRg+WPI/iSw2on+aDV9Qq1ZdcyMFLSio04htxGDLtu3h93t70q75W6offdT1lIwMyVmyxBUyyT1zqQSTPEeK5fUrPkLzCGo/eulkl0ewuiyUR9ByCO7RSsPx5hGs3Fcv1td15hG0EYHjdbrw+CNDowTJI9jbp4z3EEAAAQQQQGA4Ba6++moXAFyzZo2sXbtW5s3T79oR7R397rh69Wq3xNZ94w39Q383zXL8eW2dfu+kIYBA3wKD+Bt53wdjDQQQSFyBjGBATpg6xvUvLp0hLW1L5O1dVS6H4Is6XfjN/auk2QUEN0hqemWvF9IhbbKjYa3cu8H6rzQcmC3Tc46WM6acLBfPOkOm5E85tPyB3R09qF8CXKBuob77qdAaLTpNeN9aDQrql4g9+uVht/b9+gXBgn3xtHYdaWjbWX/9d6EtAjoi0KYLT9BcgtZtCrFNJ7aRijQJFha60Xs2gs9rbVrtrWn9+qgpxE2bNE+jTu3oqXU0NUmtJmu2Ljo13HIV5p39fjc60CocJ3uzAKqN3LM+Z3FnHsEazSOogUALCO7WacNlWkE4njyCNpJw0+pS180lzfIITvfyCI6SsZpT0EYk0hBAAAEEEEAAgeEWOPbYY13Qz4J/VujjRz/6UdQpecU/LG/ewoULewwAjtECd1M0lcw2nUlihT5uvvlm+epXv6pfGzXVDw0BBLoV4DfWbllYiAACaTpi6dgjRrv++dOPlLb2M2Xdnmp5ZVOZLN+yXt4sXynNaet1yvAmSQn0no+vTeplQ90K2fDOCvnVO7dIhhTKzLzjXP7AD805QwqzxwwNeFqmyKTjQ907QnN9aLpw5EjB0nf13d5HOHqbS1uTyK7XQ91bGNTcgTYa0QsK2qMVHkkl6GJEVugjW3OzWPdauwb4mrTYSN0rr0jNU09J47+02EtPTYvD1K9c6fq+H96kBUSOktz3n+VGB2bMnDn4weSezmOIl2flxeQR1HyA+7ZqHkEbIah97+ZqadFlfTVbZ8e6CtdtXatiXKQjDyfYKMHOisN2LBoCCCCAAAIIHCzQrqliKpsqD37Dx0tGZYySVMuRfZjaJz7xCfn6178u9957r9x0003h73KWYuaPf/yjOwtbp6923XXXuQIftt5//ud/yv/93/+JFRpZorNITjzxRJk2bVpfuzjofassXKt/vO6pzZ49W9LSdJYQDYEkFCAAmIQ3jVNGYDgEAhpEmD+xwPXPnXakjky6QN7dV6MBwf3yzNbV8nb5KmnSgGAge7v+I957kKJJyuXtmifl7beelNt1xm22HCFzC94n50w/VS6Zc7LkpA9hMY70bJHJJ4a6B9mk/8hbYZHIoGD5Bu/dvh9bNf/IztdC3Vs7TfPXjT+mMyioowQtKDjmSDeazVtlJD+m6hTfrAXzXS/6t89Ji1aCq3n6aanVXvfayl5HB7pCJDo9pOz2n0valCN0VKCODHz/+yVr4TGau/LwfXkd6vtnI/kmaR5B69Ysj2DZztrOasOhUYINceQRtFGE+zWQaP2Np3a4fY0eF5lHcJRWNM4Mf/l2K/AfBBBAAAEERqiABf9Ov//0EXX1z3/0eRmTOUR/kO9G8qqrrpJvfOMbsmPHDnnuuedk6dKlbi17bstsFJ/l/+urWQ5BmzL8m9/8xq26VYvW3X777a7bgrFjx8oZZ5whdrwLL7wwru865557bq+H3bJlS1xVgnvdCW8iMEwCBACHCZ7DIpDsAjaqaO74fNc/fcoMzWn2YdlUWivLN+6Sp7askHcqbITguxLI3NfnpdbLdnm9Svuav8gPXw9KfsosmTf6fXL+kafJB+YcLxnBIf5RlZErMmVxqHtn21ilU391VJoXFLRpwAc2e+/2/diiVZW3vxzq3trpeRoItGBgZ0DQgoKj9S+TOh10pLc0rfI2Rr+cWW/T6sK1+gXQRgbWvvCidDT2PMLU8gse0C991gPFRZJ3ZmhkYM5JJyZtReGePguWR7BkSr7rx5wVyiNYVdoQDghatWHLDRhPs3yD1t95aY9bPbtA8whqDsEJM0O5BAsn5bqRg/Hsi3UQQAABBBBAAIH+CEzUdC4W9Hta//BrU369AKA3/ffMM88UW6evZoHCX//613LFFVfIrbfeKk/pd0erHuy1ffv2uQrCVkXYKgX/6U9/kiO1qB0NgZEqMMS/VY9UVq4bgZEnYDnNZpTkaZ8jn14yRwOCn5Rt5fXylA6jf2rrS/Je1WppSl8vqcGaXnGs+nCNvCMrKrW/frd8+7UcGZ1ylCwoPEEunHG6nDlztlgBkyFvmQUiWtHYde9gDRUaFHyzKyhowcHK7d67fT8267VvfSHUvbXtOJZH0IKBXh91xIgOCgYKCqTgkktcb9fqbnUvvyw1T2owUPMBWnCwp9ZWWiaV+gXPeqpOO849/XSXNzD31FOTuqJwT9dr/8+NKsl2fe6SUB5Bywe4V/MI7u6cNly6o1asknBfrb7K8gjud93WTcsMyPjpNmU4FBAsIY9gX4S8jwACCCCAAAL9ELApvhYAfOihh+SOO+5wWz744IPuMZ7pv5GHOlsL0lmvrq6Wl156SVZq6phVq1bJ8uXLparzu6O9PlW/E77++usyfnzoe1PkPrznjPDzJHj0owABQD/eVa4JgQQQsODE1KIc+WzRsfLZJRrc0ra9vE4eW79Gnt3+kmyqWa0jBDfqlE2t3ttLSwnUSaWslBd0ROELK/9X2l8qksLU+XJs8YlaUORUWTJ9smSlH4aAoJ1j1miR6WeEur22VleuQUENBLqRgjpK0AqNVO8MvRfPf22k4ZbnQ91bP0unYHjBQPeoAcJ8/Suomo60lpqV5Qp/5J11lnToX3Tr9ctbzVNPu9GBrTptuKfWrrlbqh9/3PUUrRKXs3hxKBiof1EOatJovzarCDz92GLX7RqbG1s78wiGiovs3VItrfHkEWxsk+3vHHDd9pMaSHEVjC2HoMslqKMFM3PJf2M2NAQQQAABBBDov8CHPvQhufbaa13Q7pFHHtHBAx1SU1MjOTk5Yu8NpOXn58v555/vum3fpDmnLc/gDTfcIBUVFbJnzx75f//v/8ldd901kN2zDQJJL0AAMOlvIReAQPIIHFGYI184+RTX7ay3V1TLX9a+JMt3vCxb6t6QluA2jXH1PlopNb1MKuQ5eabiOXl6xU+l47lJUhRcIO8rOUk+MGuRnDStWHIyDuOPtpxCkRnvD3XvVtTuDwUCvenD9ljbc7DK2yz82HBAZNPToe4tzCmOCQpqUDVvnPfuiHhM0angOYsWuT72mzdK49trXSCw5umnpHnjph4NOpqbpfb5512X1O9I9nHHdRYROVvSJ/U9vaTHHSfBG+mZQZk8Z4zrdrptmkewvDOPoDdKsKGmpc8raW/rkH0aPLT+xpOh1V0ewZkaEDwyVFwkr5A8gn1CsgICCCCAQMILWEEMy4k3kppd8+FuuTpb44Mf/KAr+mFTfy0AaM2WWRBwMFqG5pz+1Kc+JRMmTJDzzjvP7fIvf/mL3HnnnVQLHgxg9pF0Aofxt+Sks+GEEUBgiAWOGJ0v/3HK+fIfcr470ubyUvnz2ufkpV0vy/aGN6UtUNrrGaSktEtKluaA0xyCyyoel3++nC7tT0+XkrQFsmjcYnn/jAVywrRCKcg6zCOVcktEZp0T6t4VVGuuNcsj6AUFd60WqS/z3u37sU4tNiwLdW/tPJ2+4I0U9KYR52qgcAQ0G2HqFREp+fJ/SJMmZLacga6i8Juau7GnZhWFdRSh9f0/ulky5s7VAiKdFYVnzdIAtL9HWQa6yyO4vyE0ZVinDlu14Sp9HU8L5xF8YbdbPWdURnjKsE0dLpxIHsF4HFkHAQQQQCCxBKwa7uEsiJFYV394z8am+lrV32XL9DtuZ+vv9F9vu94erbDH5MmTXYERGwlYXl4uxcUj4ztzby68N/IECACOvHvOFSOQsALTC4vl66d9WM/Pusja/VvlwbXPyIq9r8juxrekPbXOLe/pPymBZgnkrtcaw+vl8Yo/y6Mv50vbUzNknAYEl0xYIqfPmC4nTh0jo3PSe9rF0C3P12Cd9dmhYKf+mVOnCu/qCgh6gUHLMxhvq9Gg4rvW/961Rf4kDQrqlGEvMGiP2f6d8updeMa0aZLxuc9JkfaWfful9hmdJqx5A+te0+rMEcmgvfW9x6Z168R62c9/IWn6xdCqCeedrRWFj9GKwoHDNLXcO5lheHR5BMdqHkHtR508wZ1BXVWTyyNoRUVslGDZjhr3ce3r9Ooqm2Tjqv2u27rpmkdwnI0O7CwuYgVMgodrun5fJ8v7CCCAAAIIIDDsAmdpihfLx2dTc63ZSD1bNhTN9m0Vhq35/Q++Q+HHPv0hQADQH/eRq0DAlwLzSqbKvJJP67V9Wto72mXl7rfk4fXPysp9r8n+5nXSkdJV5as7gNS0akktWK0BwdXyaOXv5eEXx0nbPzUgmH60nDzpBDl5+kQ5cdoYKc7L6G7zoV1mI80KNFhnfe5FoWNZUNCKinjBQPeoowabNE9gvM3yD1pf/1jXFqOmdAYEOwODNlowa1TX+z57lja2REZ/7GOuu4rCmgDaFRF54QXp0KIiPbUW/VJ44Le/dT1QpBWFtTqdBQOzddpxquYRHCktpyBDjjyuxHW7ZpdHUKf+2ujA3RoU3LelSlqb2/vkaLY8gmt1fK52a5ZHsGRKnkzSKcmWp7BIKw3zBbxPRlZAAAEEEEDAtwIB/WPr1VdfLbfddpu7RntulX0Hu9XX18s777zjdmt5AgsLNYUPDYERKEAAcATedC4ZgWQUsOkYJ008xnU7/4bWBnl55yp59L3nZHXpa1LRurXPywpk7hXrB+RF+VtFQP764pTOgOACOXnysbJoepHmECyUcQWZfe5rSFawoOBoDdZZn3dp6BAWFDywuSso6KoQv6FRGa0oHG+r3KaBRe3vPNy1xZjp0aMExx0tkpnf9b5PnrmKwhddJAXa2xsbQxWFtYhI7TPPSFtlZY9X2VamFYX//GfXUzUPTe7pp7nRgTmnna6jTAcnL02PB0+wN1wewbmaR1C7NcsjWKbVhS0gaKME92yqlHjzCO7dXC3WV/19q1jOwOkLQwVLxmnF4dRU/fzTEEAAAQQQQGBECdx8881ivb+tVgu+2WhBK+pxwQUX9Bg4bNf0L9ddd50rMGLHuPjii/kDZH+xWd83AgQAfXMruRAERpZAVjBLzpp6qut25eUN5fLCjlfkH5uelzfKVkp9u1bn7aWlpLZJMGez6xWyTEcIZslfXzhS2p6YKWPT58uSI2a7YOBJ08fIpNHZvexpiN+yoGDhkaG+4PLQwfSLjBzQoheRIwUtMNhSH//JWFDR+tsPdW5jx5kRHRQcr0HBdP8Eu1IzMyVPqwBbdxWFX18dLiLSujs09aQ7wPa6Oqn++z9cT0lLk+wlWlHYpgrrCMGgjhQcac3yCI6dmu/6wveLS9pdua9eA4GhHII2SrC6tOeRlp5XTXmjvPn0Dtez8tJk2jGhYOCk2aMlEBz8v/57x+URAQQQQAABBPwh8JqmerlI/8g7ceJEufTSS2Xx4sUyZcoUycvLk0r9Q++aNWvkN7/5jbz11lvuggsKCuT73/++Py6eq0BgAAIEAAeAxiYIIJB4AoVZhXLprAtdtypiW6u3akDwZXlyywuytmK1tHT0HpBICTRIWv7brlfJXzWH4Bh5ZOcMDQjOkJLgfFk0dbJYMNBGCE4pzB7evxza1IiimaF+9EdCN6O9TaTsPQ0K6uhALzC4V4thtDbGebN0pGH5hlB/64HQNjrqUopmRwcFx80XScuKc5+Ju5qrKHzSiZKjfeyN35BGnRZiBURqtTdt2NjjiXe0tEjd88td36vB2SytKOzlDUyfpNO5R2Czabyjx+W4HplH0I0OtFGCGhjsK4+gjSB858XdrlvuwCkLitzowCPmjdFcgnxVGYEfKy4ZAQQQQACBXgWCwaCMGzdO9u7dK7t27ZI77rjD9Z42mjlzptx3330yderUnlZhOQK+F+Bbte9vcWJc4Lx586JOpEV/iaYhMFQCFpCYVjDN9U/Mv0pa21vl7bK35cWdL8uz21+SjVVrpV00YNZLS00/IOnpWkBi9GtS3ZEiT1RMlMeWa0DwHzOkMDhbFk0b6/IHLtKg4JHFCZDLLDUgUjI31Bd+LHRlba0ipeu7AoIWGNz3ts7hbO7lyiPe0ryLUrou1N+8N/RGSudxJizsCgyO1aBgcBjyKEac6qE8tc9Llv6Msl5y/fXSvHWr1DwdKiLS8IYGVHtqGmhueP111/fr1JWM2bPDwUB7bvsdqc3yCM44vsR1M7A8gns3V8n2dw7IljdKpbqs58C05Q7csHKf6zYScPJRmjNQpwpPO7pIMnMPc0XvkXoDuW4EEEAAAQQSXCBTZ3ZY4G/FihXylP4B1x7fffdd2bdvnzRq2pccTeFihT+O0cJul1xyiVx22WX63X7k5HRO8NvH6Q2TQIqOlNFhHzQEhlaguwDghg062kibVWOalAQjZxq0eIBXov6cc86RrKzkHwU1tHc9cfde21wrq/atkpd2vSzLdZTg7vpt/TrZjvY0aaufJq11GhDUPjptSmi6sI4OtFGCs0ryEjefWasG/yyo540SdEHBtSIaJB1wS9WgzNijQgFBKzAy4VgNROrrYPJ/yWrZbxWFn9HRgU9L3auv6jTr+P54kaY/0/I0L42rKHzssSOionC8nx/72lG+q1Y2rymVzRoMLN/Ve3Vvb78pmiNwwsyCUDBQpwvnjRmmXJ3eCfGIAAIIjBABP30Htt8/WltbxUaP2YgwGgLJKDCQz/HOnTtl8uTJ7nKT5ffvZLw3iX7OBAAT/Q759PyS8QeQn778+PRjNeDL2lu3V1bsWSEv73pFXt79ilQ1V/RrX+2tuS4QGAoIzpSC9CI5YapNFx6jhUUKZe74fAkkcoGDFh2NtV+DgC4oaFOIte/XSmkdvY+S7BUpoME/Gxn4/9k7D/g4ivPv/9R779Wyiovk3iu44Q6uxBSblgQSEiB0SPKmwT+JgVDSSUIzHYy7sY3BNjbuvcqyZBWr997vTu8zc5aQrLvT6XySb1fP8Fnubnd2duY769Ps754ixMDWLWgQKLibydNs+aC2qgo15P4rXYVFRmHKKGdOcaBMc14zpsOTBEEPik1j76Jca0lzxtvdOhVFdWQVWEJiYJFMEGLu+SKjsMgmLKwDhQsyFybABJgAE+gZAmpaA1sinPQMVW6VCVhOwJL7WInP35YT4jONEWAB0BgZ3t+jBJT4BaSmxU+PTq7CG9eR22tqeapeEMw7gGMFx9Gka+zWqLSNQSQIJugtBOti4eXsKQXBcSQIClFwSIQPnCiRgk2XZoqZWEDuwu0tBUtSSBQkt2BLiyNZbIUO/V4QFMJg4ABAuC8rrOgaG69mFKa4gbt2Q1tunmhs7+4Oj6sZhT1vFhmFPRU28p7tbm1lIzJOkxh4sgi5KRXQ6cxzUvALdZdCoBAEg6K9+rT7dc/OELfOBJhAXySgpjWwJcJJX5xzHrNtE7DkPlbi87dtz4Iye8cCoDLnTfG9VuIXkJoWP4q/gXpxAE0UL+9U0SkczD+Ig2QdeKH0AlroP3NLS4s9tPVRVwXBBOjqI+FO8UdG9/OT1oFCEBwW6QtnJWQ9bawhUZCyqOULK0GKJyi2EuHKbz6PTtycyHIrbjogMhwPmKvIBCMio3A9ZZkTloHVO79Gc15ep2Ea3EEZhT0mTNDHDZw5o09mFDbI5erOhtpmZJ0rla7CV86XQtNsnvjs6e+C2KsZhcPifW3XHd/U4PkYE2ACTMCGCKhpDWyJcGJDU8FdYQKSgCX3sRKfv3m6rU+ABUDrM+UWzSCgxC8gNS1+zJgirmKEQGVjJQ7nH24TBHNrco3UNLy7ResCDVkF6i0EE9DSFAgXRweMivZryzI8MtoXrk4KsYprqCJRkLINtwqC4rUs3fDgu9pLlpIYtAAYQmKgEAUdlJfwQcS3a0xOljEDhSDYeIkyM5tTRCKSESPakog4R0ebc1afqdPcpEU2JRARcQMzz5agsc68mJUiaYhIHiIsAyMH+cFRKf+u+szM8kCZABNQAgE1rYEtEU6UMEfcx75FwJL7WInP331rVntntCwA9g5nvso1BJT4BaSmxc8108Efr4NAdnW2tAwUMQSFMFjVRIJYN4qu2edq/MAE+dqi9YQzuQePiPKVgqBwGxbWgu7OCoqdV19BVoKnO4qCFd1LtAI3fyBxkd4yMHoSuQnbuMu0kTlvysrSi4GUVVhYCcLMvFsuAwboxcBZM+EyeDC7tLbjq9XqkHepQiYQEUlE6irNy2rt5OKAfkMCpKuweHV2U9C/qXbj57dMgAkwgd4moKY1sCXCSW/z5usxga4IWHIfK/H5uysOfLz7BFgA7D4zPsMKBJT4BaSmxY8VppCbMEBAq9MiuSxZCoLCZfhk0UloupldV9sQdtU6kDIMU6ZhtDjBkRKIDI300WcapizDY0gQ9HJVmHVcXdn3gqB0ISY34spsAxQN7PKOAJKW6MVAkWWYrOWUWDTFxaimeIHCMrD20CHzMwpHRJAYSBmFZ82C26hRnFG43eS3UIzAwswqvRhI1oGVxRS70oxi72iHqEH+UgyMIQtBd2/lZ6w2Y9hchQkwASZgEQE1rYEtEU4sgsYnMYEeJGDJfazE5+8eRNhnm2YBsM9O/Y0duBK/gNS0+Lmxs993rl7XXIcTRSfaBEGRXKQ7pUXnSPED+7UJgrqGcDrdHiKhcFK4EAQpqQhlGR5HGYd93BUmCAoQNUXApR3AubVAxl7zEoz4x+mFQOEmHEQJRBRatDU1lFH4W70YSJmFdeZmFPbzgydlFBZioMekSZxRuN38C/frsrzaNsvAkmyKWWlGEXqyiBUosgn3HxEI7wA3M87iKkyACTCBvkNATWtgS4STvjPTPFKlELDkPlbi87dS5kNJ/WQBUEmzpaK+KvELSE2LHxXdSooaSkl9icwuLJKJHMo7hKJ6EsC6UXQad7IKjGsTBFuayU2WihAwBoV66wVBEgWF23CAp0s3WraBqtWFwPn1ejEw56h5HQoddlUMXAb4RJp3jg3WEhmF68giUCYR+WYXtGVkLWlGsaOMwp5Tp0ox0HMaZRT28jLjrL5Tpaqkvk0MzL9caXauGpFFOJaEwNgRwfALc2f3675zy/BImQATMEJATWtgS4QTI1h4NxO4YQQsuY+V+Px9wwCr+MIsAKp4cm15aEr8AlLT4seW742+0jdhrZRemd4mCB4tOIo6TV23hq9rCoCmllyFaxPolSzjdN9bLiUEe7YlFRGWgsHert1q+4ZWLs8kIfAL4CxtRefN60r0REoeQkKgcBX2CDTvHBus1aLVXs0o/I0UBJtzcszrpcgoPH68dBX2nDEDTsHB5p3XR2rVVTUh43SxFARzLpZDpzUvc7VviHubGBjczwt2wvyWCxNgAkygjxFQ0xrYEuGkj003D1cBBCy5j5X4/K2AqVBcF1kAVNyUqaPDSvwCUtPiRx13kbpG0axrxpniM22C4LmSc9C2aM0eZEuLHXQNkW2CoLZOZJH9PslB/0CPqy7D5DbcPwDhvt+LhWZf5EZULLygtwo8S27C5iQSsaPsybHT9JaBgxYCrt43otdWuabMKJySguqdX0sxsJHem1VERuHhw+F1yyx4zZwJ55gYs07rK5Ua6zXIOlciMwpnnS+DptG8f2cevi6IHa7PKBye4At7StbDhQkwASbQFwioaQ1siXDSF+aYx6gsApbcx0p8/lbWrCijtywAKmOeVNdLJX4BqWnxo7obSoUDqm6qxpGCI9JVWGQYzqzK7NYoW3TOMolIq4WgrjGEzv/eeinK300KgcJdeAIJguKznS0n1xDZc3OPk1UgCYHn11H8QHIZ7qo4kBv0gDl6MTBhNuCkENHTyLiasrNJCNRbBtafOGF+RuGEeHhSzEARN9A1MdG259nI2Htqt6ZJi2yyCEw/WYSMMyVorNWYdSkXD0f0H6oXA6MG+8PRmYRnLkyACTABlRJQ0xrYEuFEpdPKw1IwAUvuYyU+fyt4imy26ywA2uzUqLtjSvwCUtPiR913lzpHl1+TD5FZWMQOFIJgeWN5twaq03iRq3B8m4Vgi6ajZVyYj+tVC8EA+SosBm1WEKRsy8j8jsTAz4HkTUADxXfrqjhTfLzBZBEokofETgMcvreO7OpUWzyuKS2ljMK7pGVg3YGDaGluNqubjuFhZBWoFwPdR1NGYUdlczBr0GZW0ml1yEurlJaBwl24przRrDMdXRzQL5EyCo8MQj8SBV3cmKlZ4LgSE2ACiiGgpjWwJcKJYiaKO9pnCFhyHyvx+bvPTGgvDpQFwF6EzZf6noASv4DUtPj5fib4nRIJ6Fp0SClLaRMERabhRq15YkXreLUNIWQhKATBBBIG+5M1WcekIUFeLjKZyASyEBSZhkVMQZsUBDU07rRv9G7CKdsAyrzcZXGnGIFJi/ViYNR4SqysbFdOkVG4du9eaR0oMgvramu7RCAqOPj6UkbhGTJuoMwo7KqgOJFmjdDySsL9uiirWoqB6aeKUVFoxn1Fl7N3sEPkQD8pBvYfHgR3b2fLO8FnMgEmwARshICa1sCWCCc2Mg3cDSbQRsCS+1iJz99tA+Y3ViPAAqDVUHJD3SGgxC8gNS1+ujNXXNf2CTRoGnCy6GSbIJhcltytTre0OJAYGK0XBGsSKJZgBJ3f0aXR38MZ42KEGKjPMjyYsg7b21pChCYSvoQIKNyE076mpChmWMV5U/bgIUv1bsIiq7Atu0GbMau6piZ9RmERN5AsBLVkKWhOkRmFp0yRcQM9b6aMwt4dLUTNaUPNdcrya/UZhU8Wo/hKtXlDJY/7sFgf9B8RRIlEguATpGwXdPMGzbWYABNQIwE1rYEtEU7UOKc8JmUTsOQ+VuLzt7JnyTZ7zwKgbc6L6nulxC8gNS1+VH+D9fEBljeU43D+4baEInm1ed0i0qJ1lVmFpYUgCYItzQF0/vfxA0Vj3q6O0kJQJBQRomBimDccbSkpQl2Z3j1YiIHCXRhmZH0NSNALgcJNODBeDFPRRWYUPn26LYlIM8UQNKuQW7DHuHF6MXDGTDiFcEbh9tyqyxraxMD8tAqI8JTmlIBITykExpGrsH+4DbvYmzMYrsMEmECfIqCmNbAlwkmfmmwerCIIWHIfK/H5WxGTobBOsgCosAlTS3eV+AWkpsWPWu4jHkfXBIQr45XqKziYR/EDKXbgkfwjqG4204LpavO6Jt+rrsLx0kqwRevR6cKeLo4Y3c9PioFCFBwW6QMnWxEEq/Ipcch6vZuwSCRiTgkboRcDk8g60EdYRCq7yIzCl1LJTXindBVuTDbfSlRkFPacNVMmEXHpT+7iXNoI1Fc3yeQhwk04O7kMOo15aqA3WQMKq0AhBobEeMPO1qxp20bIb5gAE2ACgJrWwJYIJ3wPMAFbI2DJfazE529b466G/rAAqIZZVOAYlPgFpKbFjwJvGe6ylQhodBqcLz0vBUEhCp4pPgNNi3mZT0UXWlrsyEU4/GoyERIE62Nop1On3rk5OegFQYohKDINj4j2hYtjR7fiTif1xo7SyyQErtOLgcUXzbgiWT72m0RuwsuARIob6CGsIZVfmnJyUPMNZRQmV+E6kVFYpzNrUM7xcW1JRFyHJNlmXEizRmL9Sk0NGmSdK5XWgeK1uYGS1ZhR3H2cIeIFxpEgGD7QFw62Ipyb0XeuwgSYQN8goKY1sCXCSd+YZR6lkghYch8r8flbSXOilL6yAKiUmVJZP5X4BaSmxY/KbiceznUQqKOkGccKj7VZCKZVpHWrtRadI1kF9m8TBHWNYXR+56Qazo72GBnlKxOKiMQiI6P94OZ8AwVB4bdZeF4vBJ79Aqi80vW47Sm7a9wMffKQQfMBF8osrIKiKStDjcgoTGJg7UHKKExxBM0pjmEio7DeMtB9zGjOKNwOmrZZh+yLZcggy8CMMyWorzYjHiWd7+LuSJmEA6R1YHRiAJwowzAXJsAEmMCNJqCmNbAlwsmN5s/XZwLXErDkPlbi8/e14+bP10+ABcDrZ8gtWEBAiV9Aalr8WDBlfEofIVBUV9QWO1C4DJfUl3Rr5DqNB2UVFtmFyTqQMgy3aHwNnu9E2VKHRZIgSGLgjEHBGEWC4A1LKiLEwJyj+uQhwlW4tshgnzvsdKSEDgPm6N2E428BnNSRQVdbU4va7/ZJMVBmFKYMw+YUBx8feE6fLuMGekyeDHvOKNyGTadrQcHlClymBCLCVbimzLyM3Y5O9ohK9JcZhWOGBsLVo7OlbdtF+A0TYAJMoAcJqGkNbIlw0oNouWkmYBEBS+5jJT5/WwSHTzJJgAVAk3j4YE8RUOIXkJoWPz01r9yuugiIuHHCIlC4Ch/MP4jjhcdRr6nv1iB1jYFt8QM1dXHkampYKAvxdsG8IWG0hWIMZRt2uFEx0bTkDp25l8RAsgpM3gw0VnY9XhfKmDv4Vr2bcP+bKYEyWQqqoAhLwNrDFDPya5FR+Btoi80Tg+3c3OA5ZbKMGeg5bRqEOMhFT0D8myrJriExsIjEwBKUU3Zhc4oQx8MH+ErLQBE70MPXxZzTuA4TYAJMwCoE1LQGtkQ4sQpEboQJWJGAJfexEp+/rYiMm7pKgAVAvhVuCAElfgGpafFzQyadL6p4As3aZpwqPtXmLixiCepazIsdJwbf0mIPXX1kmyCorY+mvZ1dHIO8XDA3KRTzh4bJ+IE3TAzUkKVW6k69m3DKdsAc8dMjCEhaoncTjhpHyZM7Zk9W6k3QQjEC609RRuFvSAwkQbA5ywyXaTFYmVF4LDyvugo7hYQoFUGP9Lu8oFafUZjEwKLMKrOvEdLfu00M9A1xN/s8rsgEmAATsISAmtbAlggnljDjc74nsGfPHkwnLwFRfvvb3+J3v/udfN/6P7t2a6WoqCikpaXB2dm59XCn13fffRf333+/3L97925Mox8bMzMz0d8KicrED3WGSg15RHzyySfYsWMHTp06heLiYtTV1cHPzw9xcXGYOHEifvCDH2D8+PGGTrf6PkvuYyU+f1sdHDcIdZgp8EQyASbABJhAjxNwcnDC2NCxcnsUj6KSrOOOFhxtsxDMrs422Qc7Ox0c3K/IDUHfoEXrTPEDY9FcPQSaqqGkEOqtmoqrG/H+oSy5BXo6Y85VMVC4Czv2ZoIER+rP4IX6rZEyJ1/8Ui8GXt5FloxGEqfUFgNH/qPffEjgHEJZhIcuB0KGKFoMtLO3h/uokXILfuopNKaKjMJfo+brb9Bw4YLxeddoUHvgoNwKX3gRrsOGwWfBfPgsXQoHL3XEUDQ++K6P+IV6YPRcscWgprxBWgUKN+G81Aq0kOuwsVKYUQWxHVx/Gf7hHnoxkDIKB0Z6cmIWY9B4PxNgAkyACdg8gezsbPz3v//Fz372M5vp6z//+U8pXJaUdPaEKCoqgtgOUgzlV199VQqAr7/+OiZMmGAz/eeOMIH2BFgAbE+D3zMBJsAEmIDZBHxcfDCr3yy5iZNyqnPa4gceLjgsBUJTjdk5NMHR66LcWkI2kRA4DM0VY8lKUFgG6i3nSmqa8OHhK3Lz9xBiYIi0DJwQSwkSelMMFAk/hq/Qb7Wl5B68Ue8mnLWf+mpEqBGJRfa/rt+CBumtAoUgGECu0Aou4pd61wED5Bb08MNozs0ly8CrGYWPHzeZUbjhzBmIreiNv8J3yRL4rbwbLlb4xV7BONu67unnimHTI+XWUNOMzLMlMm5gdnIZRFIRY6UsrxZiO/ZlJrwCXBE/KhiJU8LBloHGiPF+JsAEmAATsGUCf/zjH/HDH/4Qrt2IJxwREYGzZ88aHdbQoUPlsTFjxuCdd94xWq/9Aa1WiwcffBBvv/223O3g4IDFixdj3rx5iI+Phxf9kCksAc/QumbDhg04cOAADh8+jFdeeQVr165t3xS/ZwI2Q4AFQJuZCu4IE2ACTEDZBCK9IrHcazmWD1gOrU6Li+UX9e7CeYdwougEmnXGM6EKMdDZ95jctI1BJASOgaZyFFkJkvB2tZTVNuHjI9ly83N3wuzEUMwbGorJ8YG9KwZ6BABjHtBvlbnA+XX6BCL5p1q72vm1+CKw+0X9Fj5KbxWYRGKgt8iarOziRItu/3vukZumvJwyCu+W1oG1+/cbzSjcQm4z5R9+KDePm2+C/6p74DF5EluvXb0VXD2dMGhimNyaGjTIvlAmxcAsEgWbGrRGb5jq0gac3HlFbpGD/JA0NQL9RwTCoTfFcqO94wNMgAkwASbABIwTCAwMhLCyy8vLw7/+9S88/vjjxitfc8TJyQlDhpC3RRfFw8PDrHqimd/85jdt4t/AgQOlqGfoGnPmzMHTTz+Nffv24dFHH+2iB3yYCdxYAiwA3lj+fHUmwASYgCoJONg7ICkgSW4/GvojmTzkZOFJmUxEJBVJKU8xOm4Hl2I4hGxDS/AOaGoGQiPEwBqyoGsXL7C8rhmfHsuWm4+bE25JDMECihkoxEBnR3ujbVv9gE8EMOkR/VaSRi7CX+jdhEsuGb9U3glAbDt+BcRM0ScPSVwEuPsbP0chRxwpFo7vsqVy09XWoua7/XpXYYr/o6smN2oDpfbbvRCbc3wc/Feugs+i22BPiUS46Ak4uzoijqz6xKbV6JCbUo7L5CaccboE9VVNRjHlXCyH2Ny8nZE4KUxaBXoHMlejwPgAE2ACTIAJ3FACwrLu5MmTOHfuHFavXo2HHnoI7u43Js6tsOT705/+JHlERkZKcS8oiOI8myhTp06VrsCbNm0yUYsPMYEbS4AFwBvLn6/OBJgAE+gTBNwc3TApYpLcxIBL6kuku/C2jG34Lvc7g8lERMxAJ69kuek0ntIiUFgG6pqCOzCrrG/G2uM5cvMisUSIgfMpo/DUAYFwceycZKTDydb8EBgPTHsWuPkZoIDcUM6R+8c5sg6sNBYbkVyHM/fpty+fBuJn6t2EB84DXDyt2bMb0pY9/cruPWe23GRG4SNHUUEuMdVffWXQTbgp7TIKfvc7FL32Gvx+cDv87roLTmHKt5C0JnwHErejkwLkdvOdLShMr9SLgSQIVpU0GLyUEAmPb8/C8R1ZiE4MwJCbwtFvSADs2SrQIC/eyQSYABNgAjeGgD3FG/7973+PZcuWobCwEH//+9/xzDO0proBRYh/rQlB/vGPf6Ar8a+1i8JtWSQD4cIEbJUAC4C2OjPcLybABJiAigkEugViYexCuRXVFWHT5U1Yn7oeV6opbp6BYu9YA+cAshKjTVsXjebKMWiuGkZCkmuH2tXkLrnuRK7cvFwcMXNwsIwZeNOAILg69ZIYKLLZhVHfxDbzdxQc8Qi5CH9OrsIbgLqSDv1t+yDcoy9RpmGxOdGv3QPm6t2E42dRJl19cpS2ugp8Y0fZ/DynTJZbM7n2lH/0Eco/XwtdZWWn0Yh9pf/9H0rffgdet9xCrsWr4DZyJLsHX0PK3t4OYfG+cpu8LB6luTVIOVSA5IP5aKw1kKSG9OYr50vl5unngsGTw5FIm3jPhQkwASbABJiALRBYQvGBR4wYITPtvvTSS/jpT38qY+31Zt9KS0uxefNmeUmR4ffWW2/tzcvztZhAjxJgAbBH8XLjTIAJMAEm0BWBYPdgCDfhHw75oYwVKITAr7K+km7Dhs5tzSTsFroFTZVDZOIQbX0MVdUnDmk9p7pRgw2n8uTm4exAYqBIIBKKaQODe08MpF+zET1Bv81dDWTsITGQ3ISTaWHZZNglFs11+riCIragqw9lIb5NLwbGTAWZbbUOT7GvTuHhEJmEAymBSCW5yZS9/wGaLl/uPB4Kvl29fbvcXCmujxACvefOhRATuXQkIBKzBEZ6IXC5F8YvisXlE8U4vy8X+WmdBVZxZk15I45uyZCJQ2KGBiDppghED/aHHYmKXJgAE2ACTIAJ3CgC4u/ZH/7wB9x2220QQtwbb7yBX//6173aHRHLT6fTJ98SbsmiT1yYgFoIsAColpnkcTABJsAEFE5ALLBGh4yW2/Pjn8eOzB1Yl7oOp4tPGxxZi10TnHxP6DddMGpKRpJlICUO0ZBodk2pbdJi0+k8ubmTGDh9ULCMGThtYBDcnXvpT6EDXUdY9Ilt4WtAKrnCCsvASzsAbeM1Pb76sYEEnJPv6zfPECBpid5NOHIM6Z3KXpDaU1wfvzvugO+KFajdfwBla95D7d59Bjk0UDygvGeeReHLL8tzxHmOAQEG6/b1nY5k6TpwfKjcSvNqSAjMk5aBTfWdrQJbdC0ylqCIJ+gd6CrjBA6eFA53ihvIhQkwASbQVwm0kPijrajoU8N38PWlH4F6MYayCbrC4m7s2LE4evQoXn31VTzyyCPw8em8tjPRxHUdOn36+3Xn6NGjr6stPpkJ2BqBXnrqsbVhc3+YABNgAkzAlgl4OHlgacJSuaVXpGND2gbpJlzaUGqw2832RXChpCGuwTvhrk1EScFwaKoGU93Of+bqSAzceiZfbm4klkwfFIR5FDNwBomCHuQ23CvFiVyXE8myT2wNVcDFrfqYgZd3g1IfG+5CTSFw+N/6zbefPnnI0NuBkETD9RWyVwi/re7BjekZKP/gA1Rs2ACRKfjaoi0uQcnf/o7Sf78J74ULpVWg62Axz1wMEQgI98RNKwZg4uI4pB4rlGJgUSbdbwaKiCF4aEM6jmzOQOyIIGkVGDGAHggVLjQbGCrvYgJMgAmYJCDEv9RJk03WUdvBhAP74ehvO8nIhBWgsL4rLy+XIqCIDdhbRVgetpbg4ODWt/zKBFRBoJeedFTBigdxHQSSkpI6nN3cTPGuuDABJsAEzCAQ6xuLJ8Y8gUdGPYLvcr7DurR12JezD1oDQlkLdKh1OAe3CNqiveHWOA45V4aQgV2owSvVN2vx5dkCublQggVhETifsgkLd2HP3hIDXb2BEXfqt1qKEXiBYgUKN+ErBwz2We6syAK+e1W/BZMAOGSZfvPvb/wcBRxxie2P0N/8PwT94jFKGPIFyj/8EM25uZ163kJ/QyrXr5eb+5gx8Lv3HnjNmAE7B+W7SHcarBV2OLk4yHh/IuZf8ZVqnCP34EtHCqFp7Cw267QtSDteJDffEHckTQ3HoAlhcPV0skJPuAkmwASYABNgAl0TmEshPyZNmoQDBw7g9ddfx2OPPQb/XhIoq6u/D9HiQQnNuDABNRFhkqy+AABAAElEQVRgAVBNs8ljYQJMgAmomICTvROmR0+Xm8givPnyZqxPW4+MygyDo67XVqHe8Wu4x36NUNcE2NeMR2p6PHTajolDWk9u1Oiw43yh3JxJDLwpIQgLhoVKMdDbtZfED49AYOyP9FtFtj4W4FnKJlxwprWbnV+LLgC7xPYCEEGuwUOX612FvQyLnp0bsL09Dt7eCHjgfviTsFe9axfK17yPOnIFMlTqjh2D2JwiIuB3993wXb4M4nwuhgkERXth+t2DMHlpPC4dLcS5vbkozakxWLmisA7716ZJy8C40UEYMjUCoXE+bBVokBbvZAJMgAkwAWsSeOGFFzBz5kxUVVXhZQoBIjLz9kbx8vJqu0xtbW3be37DBNRAgAVANcyiAsZw/vz5Dr3MyclBVFRUh338gQkwASZgLgGRRfj+IffjvqT7ZIxA4SK8LWMb6jSd3UZFmwUNqeQNnAq/QS5I8JyEhvLROJsWAK3OcBy9JhIDv04ulJuzgz2mJgRKy8BZiSHwceslMdCXviMnP6bfSqj/Qgg8R1tpmnFMuccAse34JSCShggxcPCtgJuf8XNs+Iiw6POmTMBia0hOpjiB76NqyxYIC8Bri7AULKKMgcV//zt8Fy+G36qVcOmvbIvIa8dozc/Obo4YQsk/hIVfIbkFnychMPVYEbTN+sDn7a+lpX8Plw4Xys0/3IPOicDACaFwoTa4MAEmwASYABPoCQIzyLJ/2rRp2LNnD/5Of9ufeOIJBAUF9cSlOrQZ0C7GcGEhhV/hwgRURIBXbiqaTB4KE2ACTKCvERDxyUYEj5DbM2OfkdmDRRbhE0UnDKJo0jXifBXF2XPYjbhREUhwm47SwuE4lqaDhhIiGCpNWh2+uVgkNycHO0yO14uBs0kM9HXvpWQJgQnA9OeBac8B+RScWiQPObcOqM4z1GWKI0giTsa3+m3LE0DCLXoX4YHzAGdlurOIWH/hf/ojZRB+EuWfforyjz+GiAl4bRGxA8s/+khuHjdNpTiB98Jj8iS2WrsW1NXP4t9QaH8fuU1enoCUwwVSDCwvMCyml+XVYt+nl3BwfRoSxoTIWIHB/byYrxG+vJsJMAHlERAJMURMvL5UxJhtsYhYgDfddBNqamqwevVqvPLKKz3ezeHDh7dd48SJE7j//vvbPvMbJqB0AiwAKn0Guf9MgAkwASYgCbg7uWNx/GK5ZVZmYuPljdiYthHF9cUGCeXX5iK/9gOKG/chbr5pAiKdbkbGlRgcTKtEM8VBM1TE/j0pxXL7pb0dJgkxcEgoZieFwt+jF8RAkZAhfIR+u4Vcfq8c1FsFnqe4gfVlhroM6MhaLuVL/UbJVSBEQJE8JG4GWUX2Qp8N98rivSL7b9DDDyPwRz9C1fbt0ipQZAk2VERWYbE5x8XBnywCfW67DSL7MBfDBFw9nDB8RhSGTY9EPv07EO7Bl08WQafp/O9B06RD8oF8uQm3YmFJmDA2BM6uvLQ0TJf3MgEmoBQCIhuuLSXEUAq3nujn1KlTcQt5AezcuRP//Oc/8dRTT/XEZTq0Ka5pT/eAjrJBf/nll/jrX//KP3J1IMQflEzANnJ9K5kg950JMAEmwARsjkCMTwweG/UYvlr+Ff4x8x+YFT0LjnaGhYkWtOBI4UGsy/kz0t2excr5J/H0Qi/MpKzAwv3XWBEWg3svFeO5dWcx9v++xqq3DuOjw1dQWtNo7BTr7qfFKWImAwtfA566BNy9Fhi2giz8PI1fp5li2Qg34o+p3itkVbjpUbIS3EsiYedkEMYbsY0jds7OUtCL+fwz9PvoQ3hRwHAYSQLSdPkyCn73e6ROn4Eish5ozjNiOWkbQ7vhvRBWgeEJvpj9wyTc9+fJmETxAn2C3Iz2SyQW2fNhCt59bj++/SgFJTnfB1A3ehIfYAJMgAkwASZgBgFhBShKfX19r8QBFC7At95K4VOopKenY/PmzfI9/48JqIGA4achNYyMx8AEmAATYAJ9noCjvSNuirxJbqX1pdiavlUmDkmrMBxHr7KxEmvTPiFun2BwwGA8P/o2uDSMxu7kWnxLYp+IDWioaEkM3JdaIrdfbziLCbEBmEfZhOeSZWCQl4uhU6y7z8FJ7+YrXH2byHUzdYc+ZmDqTlAKZMPXaqgATryn3zwpYciQpbRRzMCIUaCfug2fY4N7hVjlPmqU3ISwJ1yDyz/7HLrKyk69FftK//cWSt95F15kUeB/zyq4jRzJv+x3IvX9DjdPZ4ycHY0Rs6KQk1KO85RBOONUCVlGdLYKbG7QSqtBYTkY0t9bxhiMHx1MhqYO3zfI75gAE2ACTIAJdIPAhAkTsGDBAmzduhVvvvkmfv3rX3fjbMuqPv/889i0aRNaWlrws5/9TGYkDgwM7LKxhoYGKRjefjt5WnBhAjZIwLhpgw12lrvEBJgAE2ACTMBSAgFuAbgn6R6su20dPpr/EW4fcDs8nYxbyyWXJePVE6vxl4sr4dfvM/zrR554bcUwzEkKgQtlCTZWhC5y4HIp/t+Gcxj3x6+x4s2DWHMwE0VVDcZOse5+Z3JxTVoC3PEh8HQqsOifendfO+N9Rk0BcIjq/W8G8NeRlFH4RaDoonX71QutOYWHI/jJJ5GwexdCf/c76fpr8LJaLarJfTjrrruRufx2VG7ciJamJoNVeaeegB25vEcN9sfcB4finj9NwvhFsfDydzWKpzCjCt+8lyytAvd9dgll+WR9yoUJMAEmwASYgAUEWq0AGxsb8cYbb1jQQvdOGT9+PIQIKIpIXjllyhRcm9Ty2hb3798vhcJPKU4xFyZgqwTYAtBWZ4b7xQSYABNgAj1CQFiMDQ0aKrenxz6Nr7O+hsgifKTgiMHrNemasC1zm9zCPMKweOhiPDV/IS7mOGLbuXzsogQhDQYyp4rG6IdjHM4ok9tvN53H2H7+ZBkYinlDwhDqY1w8MdgRS3a6+gAj79ZvNUWAiBUoXICzDxtvrTwD2PuyfgsZok8eMmQZSAU1fo6NHRFx/vzuWAHfFT9A7YEDFCdwDWq/3Wuwlw2UpT7v2edQSK7BfnfcITcRZ5CLcQIePi4YMy8Go+b0w5XzpWQVmIessyXyfr/2rMY6Dc7sypGbcCtOuikccSOC4eBkQpC+thH+zASYABNgAn2awCiy9F+8eDE2bNiAkpKSXmEhRMeCggK8/fbbSElJgUgOsmTJEsyfPx9xFFvYy8sLxcXFOHv2rLQW3LtXv86IjY3tlf7xRZiAJQRYALSEGp/DBJgAE2ACqiDg5uiGW+NulVt2VTY2XN4gE4cU1hUaHF9+bT7+dfpfchsfNh5LJi7Bi0tuxqHL1dh6lsTA5CLUNxuOpyfEwCOZZXL7/eYLGN3PD/PJTXgeJREJ9zUeX81gRyzZ6RkMjH9Qv1VcISHwC3ITpq3wrPHWCim5hti++T0QOY6Sh5CLsLAuFG0poAix13PyZLk1ZmSg/IMPUbF+PUSm4GuLyChc8re/o/Tfb8KbXI2Ee7BrYuK11fhzOwL2ZBUYMzRQbtVlDUjen4cL3+WhttKwNWVeagXEts8zFYMnhiGREof4BpPFKhcmwASYABNgAl0Q+P3vf4+NwmJfLKh6oThQXOG33noLo0ePxm9/+1spPK5duxZiM1aEpWCr5aCxOryfCdxIAiwA3kj6fG0mwASYABOwGQJR3lF4ZOQjeHj4wziUf0jGCtx1ZReaRRZdA+Vw/mGIzcvZC/P7z8fDc5bg5WWzsDe1GF+eLcA3yYWobTIsBormjmeVy+2FLRcwMtqXsgmTGEjWgZF+vSCI+EYDUx7Xb8LVV1gFnqVNWP8ZKzlHyA+Gtu3PAf1v1ouBgxYCbr7GzrCp/S79+yP0//0aQY89ioov1pEY+AGac3M79bGluRmVZGEgNvcxY+BHQqDXzJmULZrj2HWC1W6HcAced2ssxsyPQeZZsgqkOIBXkikztYHntIaaZpzceUVukYP8ZKzAmOGBlMOFrQLbIeW3TIAJMAEm0I7AsGHDIGLrffbZZ+329vzbhx9+GKtWrcInn3yCHTt24NSpU9LyTyQl8fX1RXx8vHT9vYO8CMbQuoELE7BlAnakoBtYmtlyl7lvaiAgYilERUXJoWRnZyMyMtLmhyW+5L/66ivZz9mzZ8PNrRcsdmyeCneQCaibQAUlytiasRXrUtfhUvmlLgc7wG8AlsQvwYLYBXBz8JZZgr8ky8CvyTKwplHT5fmiwvBIH2kZKKwDo/x7QQxs7ZVYDuSd0FsFnl8HVOe3HjH+6uBMyUdm692EB8ylDMS92F/jvTLrSAvFAazZvRtl761B3dGjJs9xioiA3913w3f5Mjh4e5usywe/J1BZXC8tApMP5KG+2rCQ3lrb3dsZiVPCMXhyGLwD+O9rKxd+ZQK2QEBNa+DU1FRoNBo4OjoiISHBFvByH5hAtwlYch8r8fm722D4hC4JsADYJSKu0BMElPgFpKbFT0/MKbfJBNRMQPxWJpKCrE9dLwXB6qZqk8N1snfC9KjpWJKwBBPDJkIkD953qQRfUszAnRcKUd1gnhg4NMJHWgUK68CYQA+T17TqQR1ZLmYd0FsGXtgI1Jd33byzJzBogT6TcNx0UJC3rs+xkRoNyckoe/8DVG3ZYjIZiB3FFvRdvAh+K1fBJba/jfTe9ruhpX8A6aeKZQbh3BTKPm2qUALqfkMCkDQ1Qr4KN2MuTIAJ3FgCaloDWyKc3Fj6fHUm0JmAJfexEp+/O4+c91wvARYAr5cgn28RASV+Aalp8WPRpPFJTIAJSAKN2kZ8k/WNdBEWrsJdlWD3YCyKWyQtA4WbcaNGiwNppeQmnI+vSAysrDdtGdXafmKYNxYM08cMjA0isa23iobiuaXvJsvAz4GLXwLNZmRzdfMHEhfp3YSjJwH2ynDt1JSWopyy95V//DFETEBTxeOmqfBfdQ88pkyGiDXIxTwC5QW1OE9xAi8ezEdjrWkh3NPPRVoFJk4Oh4evi3kX4FpMgAlYnYCa1sCWCCdWB8oNMoHrJGDJfazE5+/rxMSnGyDAAqABKLyr5wko8QtITYufnp9hvgIT6BsEcmtysSltk8winFeb1+Wgx4aOlULgrH6zIBKQNJFl1MF0EgPP5GPHhQJU1JknBg4KpbiD5CIstvjgXhQDm0j8u7Rd7yacthPQkjjYVfEKJ6vApXoxMGwESC3r6owbfrylqQlVFOdHuAc3nKMkKCaKM2X7EwlDfG67DSL7MBfzCGgoWc7lE2QVSLEC8y9XmjzJjqwA+w8LlBmEowb5Q3zmwgSYQO8RUNMa2BLhpPdI85WYgHkELLmPlfj8bR4NrtUdAiwAdocW17UaASV+Aalp8WO1ieSGmAATkAR0LTqZEGR92nppHdikMy2MeTh5YF7/eVIMHBo4VFqQNWt1OCTEQEogsuN8AcpqTbfRin5AiGebGDggxKt1d8+/Crfg5C16y8DMfZTsgfycuyr+ccDIu4GxPwZcbT+WnnD9rj95ityD16D6KyF4Gk/qYk+xAX1vXw5/ihXoFE6iJxezCZTm1pB7cB5SDuWjqcE4Y9Ggd6CrdA8eRFmERdxALkyACfQ8ATWtgS0RTnqeMF+BCXSPgCX3sRKfv7tHhWubQ4AFQHMocR2rE1DiF5CaFj9Wn1BukAkwgTYClY2V2JaxTboIXyi90Lbf2Jt433gsjl+MhbELEeAWIKtpSAw8klGGreQmLMTAkhrzxEBhDTh/SCjFDQyDsBLsNdfU6kLg/Hp9zMAc0wk15ABdKXPwpJ8D4x5ShBAo+tycn4/yjz5GOWUf1FWasFgjd2evW26RVoFuo0b13hxIsMr+X3OjFqnHCqVVYFGW6Tib9g52iB0ZhCEUKzB8gC9zVvbUc+9tnICa1sCWCCc2Pj3cvT5IwJL7WInP331want8yCwA9jhivoAhAkr8AlLT4sfQnPA+JsAErE8gpSxFCoFb0rdACIOmiqOdI26OullaBU6OmAxHe0dZXatrkWLgNkogsu1cAYqrG00103YslpKGzBsaKq0DRfzAXhMDyzNJCPyCLAPXAkVdCKBufsBEEgLHkxDo0ovWi22Uuv9GRxnhKzdtllaBTWmXTTbgmpgI/3vvgde8ebB3Zms1k7CuOVh8pRrn9uXi0pFCaEgYNFV8Q9zJKjAcwirQ1UM5yWdMjYmPMQFbIqCmNbAlwoktzQX3hQkIApbcx0p8/ubZtj4BFgCtz5RbNIOAEr+A1LT4MWOKuAoTYAJWJNBEsfJ2Z++WYuCB3ANoof9MlSC3INwWd5u0DIzxiWmrKsTA41nlMoGIEAQLq8wTA2MC3KVVoMgmPCSiF8XAQhIAz5EQKMTAiqy2cXR6I5KGTHqELALJNVghQqBwD649cADla95HzbffdhpS+x0OgYHwu+MO2lbAkd5zMZ9AU72GRMACnNubB+EqbKo4ONojfkywdBEOje3F+9xUp/gYE1ABATWtgS0RTlQwhTwElRGw5D5W4vO3yqbNJobDAqBNTEPf64QSv4DUtPjpe3ccj5gJ2A6BgtoCbEzbKBOH5NTkdNmxUcGjpBA4J2YO3J2+TzKhIzHwxBUhBhaQZWA+8isbumxLVIjydyM3YX0CkWGRPr1jGUhiGXKOAQfeoLiBm433UwiBkx/Vxwh08TRez8aONGVmouyDD1G5bh10dXVGe2fn5ATvBQuke7CwDuRiPgEhuBZmVEn34NTjRdA2m445GRDhIYXAAeND4eKmt6Y1/2pckwkwgfYE1LQGtkQ4ac+C3zMBWyBgyX2sxOdvW2Cttj6wAKi2GVXIeJT4BaSmxY9CbhPuJhNQNQGROOR44XGsT12PnVk70aA1LeCJrMFzY+ZiacJSDA8a3kG4E2LgqZwKmU1YuAnnVtSbxS7Cl8RAchMWMQNHRvVSHLX8M8C3q4GLlEDEWHGnWIiTSAgUFoHOHsZq2dx+bXU1Kr74AuUkBjbnmBZ33caMhv8qcg+eOQN2jixQdWcyG2qbKWFIASUOyUV5gXHBVbTp6GyPpCkRGHFLNDz9XLpzGa7LBJjAVQJqWgNbIpzwjcAEbI2AJfexEp+/bY27GvrDAqAaZlGBY1DiF5CaFj8KvGW4y0xA1QSqm6qxPXO7FAPPlpztcqwx3jFYkrBEugkHunV0KRWWUqdzKBEJJRARSURyys0TA8N9XDGXLAMXDAslMdAP9vZ2Xfbjuirknwb2kBCYstV4M+40tsmPkUXgDxUlBLZQtuCaPXtQ9t4a1B05Ynx8dERkDPajzMG+y5fBwcfHZF0+2JGAuNfz0yqke/Dlk0XQaYy71ts72mHwpHCMmh1NmYTdOjbEn5gAEzBJQE1rYEuEE5Nw+CATuAEELLmPlfj8fQPQqv6SLACqfoptc4BK/AJS0+LHNu8K7hUTYAKCQGp5qnQPFolDyhrKTEJxsHPA1IipUgycGjkVTvYdEyAIgeRcbpUUAr8kMfBKmWlrqdaLhXoLMVCfQGRMvx4WA/NOkRD4Z+DSttbLd371CNILgWOEEPi9G3Tnira3p+HiRUoY8j6qNm9BS5PxbM52bm7wWbyIrAJXwSU21vYGYuM9qq9uQvLBfLIKzENVsXHR246E7YHjQjBqbj/4hSrHutTG8XP3VE5ATWtgS4QTlU8vD0+BBCy5j5X4/K3AqbH5LrMAaPNTpM4OKvELSE2LH3XeVTwqJqAuAs3aZuzN2Yt1aevwXe53EC7Dpoq/q7+0CFwSvwSxvp0FJCEGns+rkvECRdzAjJJaU821HQv2cmkTA8fG+MOhpywDc0/oXYMvbW+7dqc3HsHAlF8AYx4g0zllWXFpyspQ8emnKP/oY2iKizsNrf0Oj6lTZZxAj8mTYWdv3/4Qv++CQAu5w+eklMtYgemnSyA+Gyxk4Bo/Khij5/VDYKQyMlAbHAfvZAK9QEBNa2BLhJNeQMyXYALdImDJfazE5+9uQeHKZhFgAdAsTFzJ2gSU+AWkpsWPteeT22MCTKBnCRTVFWHT5U3SMjCrykQ23avdGBY0DEIIFDEDPZ07J9MQYuDFguo2N+HLxeaJgYGeQgwMkUlExvX3h6NDD4hTucf1FoGpXxmH6hlCFoFCCLxfcUKgsAKs2vEVytasQcNZ0+7ezmQJ6L9qJXwWLYK9u7IsH41PXu8dqSqpx8mvruDCgTyT7sExwwKlEBjan12we292+EpKIqCmNbAlwomS5or72jcIWHIfK/H5u2/MZu+OkgXA3uXNV7tKQIlfQGpa/PCNyASYgDIJCOHuZNFJrEtdh6+yvkK9xriroxihSBxyS79bpBg4OmR0h8QhrQREm6lFNdh6Jp8yCufL963HTL0GeDhjdlIoFlACkQmxPSAGiqzBwjU4bafxbgghcMrjwOj7lCcEEvf6U6dQLtyDSRAExQ00Vuy9vSlG4HL4330XnCIijFXj/UYI1FY04uTXV6RVoKbJuCVt5CA/jJkfg/CEXkqIY6S/vJsJ2BoBNa2BLRFObG0+uD9MwJL7WInP3zzT1ifAAqD1mXKLZhBQ4heQmhY/ZkwRV2ECTMDGCdQ212JH5g6ZOORUMcXR66JEe0Vjcfxi6SYc4kHCmZGSWkiWgZRJWIiBwkrQnOLn7oQ5JAaKbMKT4gLgZE3LwOyj5BoshMCvjXfFMxSY+gQw6l4SAl2N17PRI835+dI1uOKzz6CtrDTeS3IH9po1S7oHu402LOgaP5mPiDiBp7/Jxtk9OWhqMC64hsX7kEVgDKIT/Q2K5kySCfQ1AmpaA1sinPS1+ebx2j4BS+5jJT5/2/5MKK+HLAAqb85U0WMlfgGpafGjipuIB8EEmEAbgfTKdOkevCltE0obStv2G3pjb2ePyeGTZeKQaZHTSKzrmDik/TmXi2ukm7CIGXghv6r9IaPvfdycMDuR3ISHhWFyXCCcHa3kJpxN2XT3/Am4vMvoteEVRhaBQgi8R5FCoK6+HpWbN0urwMbUNOPjpCMuiYNJCLwH3vPnw97Z2WRdPtiRQGNdsxQBT3+Tg4ba5o4H230KivaSFoH9yUVYJA/hwgT6KgE1rYEtEU766rzzuG2XgCX3sRKfv213BpTbMxYAlTt3iu65Er+A1LT4UfTNw51nAkzAKIFmXTO+y/kO69PWywQi2hbjVk6iET8XPyyIXYClCUuR4JdgtF1xQCQN2XZO7yYsMgubU7xdHXFLosgmHIopCYFwcXQw5zTTda4c1guB6buN1/MKv2oRSEKgo4vxejZ6RLhl1x08SHEC30fNt98C9NlYcQgMhN+KFfC7YwUcgyhbMhezCTQ1aGTW4FM7r6CuyniGZv9wDxkjMH50COxZCDSbL1dUDwE1rYEtEU7UM5M8ErUQsOQ+VuLzt1rmy5bGwQKgLc1GH+qLEr+A1LT46UO3Gg+VCfRZAiX1JdhyeYvMIpxRmdElhyEBQ6RV4Nz+c+Ht7G2y/pXSOnxJYuA2chM+nWPCZbVdK14ujphFloELyTLwpgFB1+8mnHVQLwRmkEBmrHhH6oXAkSsVKQSKYTVlZaHsgw9R+cUX0NXVGRsp7JycpDWg/733wDUx0Wg9PtCZgKZZi+T9+TjxVRZqyho7V7i6xyfIDaPm9sPA8aFwsJZlq9Gr8QEmYDsE1LQGtkQ4sZ2Z4J4wAT0BS+5jJT5/83xbnwALgNZnyi2aQUCJX0BqWvyYMUVchQkwAZUQENZkp4tPSxfhbRnbUKcxLiKJIbs4uGBWv1lYGr8UY0LHQLgMmyrZZXXYTjEDt5IYeCq7wlTVtmOBns5YNCICy0dHYnCYabGx7SRjb7IOXBUC9xqrAQgh8KYngRFCCFSmu6y2uhqV69ZJMbA5O9v4WOmI94IFCH7icU4YYpJS54NajQ6XjhTg+LYsVBYbT7Dj6eeCUXP6YfCkMLqdrGDV2rkrvIcJ2BQBNa2BLRFObGoyuDNMgAhYch8r8fmbJ9v6BFgAtD5TbtEMAkr8AlLT4seMKeIqTIAJqJBAXXMddmbtlC7CxwuPdznCCM8ImThkUdwihHlSfL0uSm5FvbQKFElEjmeVd1FbfziRBMBlJAQuGhGOQM/rcNfN/I6EQEoWkrnP+HV9osgiUAiBdytWCGyhbME1e/ZI9+C6w+QObaTYUVxA/3vvRcBDD8LB09NILd5tiIBO14K044VSCCzLqzVURe5z93bGiFnRSLopHM7k7s6FCaiVgJrWwJYIJ2qdVx6XcglYch8r8flbuTNkuz1nAdB250bVPVPiF5CaFj+qvrl4cEyACZhFIKsqqy1xSFF9kclz7GCHieETsSR+CWZEz4CzQ9dWdPmV9dIyUGQTPkZioIkwdvLajhRbbdrAIGkVOH1QsOXxAjNIABTJQrL2Gx+TTzRZBD5FQuBdgIkkKMYbsI0jDSkpKHv/fVRt2oyWJsMx7Bz8/RH06KPwXb4Mdo4sUnVn5lpICMw4U4JjX2ai+IrxjNguHo4YMTMKQ6dFwoUyYnNhAmojoKY1sCXCidrm80aOp4n+Vn1BIS22bduGI0eOoLi4GFVVVfDx8UG/fv0wbtw4LFu2DDNmzKCYq4Y9EPbQj2Br1qzBoUOHIJ4p6yg8hru7O8LDwzFgwADZxqxZs+SrsTZaGVijP61tiVcxrvXr1+O7775DQUGB7FsQxeeNj4/HvHnzsHLlStnP9udY8t6S+1iJz9+WsOFzTBNgAdA0Hz7aQwSU+AWkpsVPD00rN8sEmIACCWh0GhzIOyDFwN3ZuyE+myo+Lj5Y0H+BjBc4yH+QqaptxwqrGqQYuOFULk5e6dpN2JdElNuGh2PZqEgMi/SBnV03M7AKtVFYAu4mIfDKgbZ+dHrjK4TAp4HhdypaCNSUlaHi009R+vY70JGrsKHiHB+HkGefhefUqYYO8z4TBIQbffaFMhzblon8NOMxL51dHaQIOJzEQDevrkVyE5fkQ0zApgioaQ1siXBiU5Oh4M6sozAWTz75JDIzM7schRDyXn31VSygkBatpaamBqtWrcKGDRtad5l8FWLc3Llzjda53v60b/j8+fP48Y9/jIOUwMtU8fDwwPPPP49f/vKX3V/btGvYkvtYic/f7YbMb61EgAVAK4HkZrpHQIlfQGpa/HRvtrg2E2ACfYVAWUOZTBwisginVaR1OezB/oOli7DIJCyEQXPK5eIarDuRQ1su8isbujwlPthTWgUuGRmBEG/XLut3qCCEwAyKDSgsAq+YWJT7xeiFwGErlC0Elpej5B//RPknnwAaw0Kux5QpCH7mabjSwxWX7hPISy2XFoHZycZd3B2d7ZE0NQIjb4mGh69L9y/CZzABGyOgpjWwJcKJjU2HIrvzwgsv4De/+U1b32+55RbcdtttSKSkVb6+viijH7JSyKp98+bN2LlzJ3Q6HYYPH45Tp061nSPEvB07dsjPwqJOCG5jx46Fn58famtrZVy8/fv3Y9OmTSgqKpLWeMYEQGv0p7Vj+/btk2OpqKiQu0aPHo17KQTHsGHD4EkhOHJzc7F9+3a899570iJQVLrjjjukFaMTJfCypFhyHyvx+dsSNnyOaQIsAJrmw0d7iIASv4DUtPjpoWnlZpkAE1AJAWHxdL70PNanrseXGV+iprnG5Mic7Z0xM3omFicsxoSwCV0mDhGNacm98uDlUnxBYuA2yijc0KwzeQ3yEMaUhCCyCozAnKRQuDp1I/mCEALT9+iFwGzjcfPg17+dEKhcd9nG9AwUvfIKanbtMsyU3Kp8ly8n1+BH4BgYaLgO7zVJoDCjSloEZpKLsLFi72hHiULCMWp2NLwD3YxV4/1MwOYJqGkNbIlwYvMTZOMdfOedd/DAAw/IXgYHB+Ozzz7DzTffbLTX586dw+OPPy7dg1sFwK1bt2LhwoXynDlz5mDjxo1wcTH8A4uWYuUKK8FBgwYhKSmp03Ws0Z/WRsUzrRAqhYApvBVeeuklaeVoyHMhIyMDixcvxpkzZ+TpzzzzDFavXt3aVLdeLbmPlfj83S0oXNksAiwAmoWJK1mbgBK/gNS0+LH2fHJ7TIAJqJdAvaYeX2d9LV2EjxQc6XKgYR5hWBS/CCJxSKRXZJf1RYWaRg1ErMC1x3NwJKOsy3O8XByxcHiYdBEe3c/PfDcaKQTu1rsG55gYi38sCYHPAENvJ4tA5QqBtYcOo5AeLhqTkw0ytaeYSQEPPUTJQu6BvWs3rSsNttj3dpbkVMtkIWknKI4m6cyGih2p1wPHhWDU3H7wC/UwVIX3MQGbJqCmNbAlwolNT46Nd05Yvwl3XhGnT7i/Hjt2TApzXXVbWAB+9NFHMmaeqPvggw/iv//9rzxNCISGhL2u2hTHrdWf1mvNnz9fWhqKzy+++CJ+9atftR4y+CosE0eOHIm8vDy5dhEuw+PHjzdY19ROS+5jJT5/m2LAxywjwAKgZdz4rOskoMQvIDUtfq5z+vh0JsAE+iiB7OpsbEzbKMXAwrrCLimMDxsvE4cI60BXR/MEpiuldVh3Uu8ifKWsrstrxAS4YynFClxKloGRfu5d1pcVhBB4+Ru9EJh7zPg5/nHAzSQEDlmuWCFQZA2u3LgJxa+9Bg0FWzdUHMPDEPz4E/BeMB92RoKuGzqP931PoLygFie2ZyHlSCFE8hCDhaxY40cHY/TcGARGcmZmg4x4p00SUNMa2BLhxCYnRSGdeuqpp/CXv/xF9la8PvHEExb1vL37b0NDg1Hrv64at1Z/xHVOnz6NESNGyEsOHToUJ0+ehIND194Jn1LMXuECLMrSpUtlUhT5oRv/s+Q+VuLzdzeQcFUzCbAAaCYormZdAkr8AlLT4se6s8mtMQEm0NcIaHVaHM4/jHVp67Dryi4065pNIvBy8sL82PlSDEwMSDTLYk+4IR/NLCerwGyyDiyQVoImL0IHJ8YGYNnoSMwbEgoPshLssgghMI2EwD1/JLOA48arB8RftQgkIdC+68W98YZu3BEdWV+UvvU2JQp5Gy319QY74krxikKeexbuo0YZPM47uyZQVVKPE19dQfKBPOg0RoRAaiZmWCDGzItBSH/vrhvlGkzgBhNQ0xrYEuHkBuNX7OXF33Hh8ltSUiKt//Lz8+Hl5WXReES8QBEfUBQhtLUKb91pzJr9EdcVYuZr9OOaKG+++aa0UpQfuvifcFEWGY+FNaLIUiz4iDiG3SmW3MdKfP7uDhOuax4Bw7m1zTuXazEBJsAEmAATYAJ9kIADiWCTIibhlZtfwa7bd+G5cc9hoN9AoySqm6vxaQr94r31DqzYsgJb0rd0KRqK+Dnj+vvjpeXDcfRXs/D6ihGYmhBI4qHRy+Bgeime+vw0xv7f13jis1M4kFZCgcSNizCysYRZwI9IBLzrcyB8pOHGSykhyvoHgX+Qm84ZqkcCqNKKcPcNeuTniNu+DT5LlsAQyAaKS5R1193IeewXaLpyRWlDtIn+ilh/0+4aiFUvTMLwGVFwdDK81BaxA9euPoZNb5yESCzChQkwASagNgIiM64Qt0SZShnoLRX/xPmj2v0w9fOf/1zGBxT7u1Os2R9x3b1797Zd/tZbb21739UbYSXYmt1YuDofOHCgq1P4OBOwGgEzfh632rW4oT5M4No4Dc3Npq1F+jAqHjoTYAJMQFEEfF19cffgu+WWXJoMkUF4a/pWVDVVGRxHclkynt/3PP564q9YOXgllg1YBg8n03HR3JwdsJiyAIstr6Ie60/myuQh6cW1Bq9R16SVWYZFpuEIXzfpHryM3IRjAo1cR6iKA2YDCbcAqV/pk4XknezcdmkqsO5HtOp/iVyDnwWSSEhTmEWgU0gIwv/0R/itvBtFq19C3ZHOsRCrKctiNSUQ8V+5EoE//QkcvNlKrfPNYHqPp58LpvwgQcb9O70rG2f35KC5obNwLLIJiy0s3kdaBEYl+ptlIWv66nyUCTCB6yEg3PgbavvWs4qrhxOFgDDxC5sFQIWLbGsRmXGvp/zwhz/Eyy+/LGMJiky/woJOiGgimYiIoScScTg7O5u8hDX7Iy7UmswjNDQUYWFhJq997cH2gqboV6sgeG09/swErE2ABUBrE+X2mAATYAJMgAn0UQKDAwZDbE+OeVK6BosswofyD1FuhM5WePm1+Xj52Mv495l/Y8XAFbhr0F0Icg/qklw4CXo/mx6Ph6fF4VR2hRQCN53KQ1WDxuC5uSQY/m1XmtxEwhAhBC4YFgYfN6fO9aUQOIeEQBIDL+3Quwbnf/8A03ZCySXgix8C35IQOI2EwEQhBBq29Go7x8beuFFmxOj33kXN7t0oeullNGVmduwh/VBXRpkbK9evRyBZW/it+AHsnAww63gWf7qGgLu3MyYujsPIW6KlCCjEwMbazvdqflolNv/tNIKivTBmfgz6k4uwtR/Gr+kaf2QCTMAIASH+vf30d0aOqnP3Ay9PgZuXaQGtuyMvLS1tO0W4Al9PiYqKgoidd+edd6KmpgbCLX3t2rVyE+2KjMDjxo3DErJwv/fee+Hv79/pctbsT2VlJVoNWoQA2N0SQj/GtZb2/Wrdx69MoKcIKGu12lMUuN0eJyBMrttvu8iygAsTYAJMgAmok4CLgwvm9Z+H/8z+D7Yv246HRzyMCM8Ig4OtbqrG/87+D3O+mIPfHvgt0ivSDda7dqdwER4Z7YcXFw/FEXIR/sddozBjUDAcTFgwHM8qxy/Xn8U4chF+5OOT2JNSBK0hF2EhBA6cS2kHvwXu+BgIHXbt5fWfS1KAtQ8A/5oInFtHrsE6w/VsdK9g6DVjBmI3b0IIZS508PHp1FNtRQUKKbNh+m2LyCpwN0QMJS7dJyCsa8Yu6I97/m8SJi6NgxsJg4ZK8ZVqbPv3WXzy4hFcOlpg2oXdUAO8jwkwASZgIwSqq6vbeiIyAF9vWbhwIZIpq/2jjz6KwMDADs01NjZi3759Mi5fXFwc1qxZ0+G4+GDN/rRvy9Oz+0md2p9TVWXYY6LTAHgHE7ACARYArQCRm2ACTIAJMAEmwAQMEwj3DMdPh/8UW5dsxevTXsfwoOEGK4pEIutS12HRxkV45JtHcLzwuNlik6sTxdMhq7637xuLg8/PwK8XDMagUOOBxhs1Omw+nYf73jmKiX/6Bn/alozUwu8fVNo6KITAQfOBh/aSEPgRCYFD2w51eFN8kYTA+4F/TwbOb1CeEEiWff6rViLuqx3wv+8+wIClX1NGBnIefhhX7n8ADfQAxsUyAs6ujhg1ux/ueXEipq4YAOEqbKiU5dVi51sX8NHvDsmEIlqtssRlQ2PifUyACfQtAu1j/tXWGg7Z0V0ikZGReOONN1BYWIjjx4/jH//4Bx544AEkJCS0NVVBP1wJK8B3yIq9fbFmf9q3JSwSu1van+PNYTa6i4/rXwcBFgCvAx6fygSYABNgAkyACZhHQCQOmdlvJj6Y/wHWzFuD6VHTjZ64J2cP7tt+H1Z+uRI7s3aSlV7n2GnGTg72csWPpsZi22NTseWRKbh/cgz8PQxbW4k2iqob8ea36bjltb247e/f4b0DmSivberYvBQCF5BFIAmBKz4AQoZ0PN76qegC8Pm9JAROAS5sVJwQKCwARRbguK1b4DV7duuoOrzWHTqEjKXLkPfLX6G5sKjDMf5gPgFHims5bHokVr4wEdNXDYJ3kJvBkyuL6rFrzUV88P8OShdiTbP5/xYMNsg7mQATYAK9RCAgIKDtSkKws2YR2XNFHL2H6Yept956C5cuXcKxY8cwZQr9/b1annzyyQ5Wf9bsjxDtHB310dQKCgpaL2n2a3se7ftldgNckQlYSMCOXDnYl8NCeHya5QSUmIZcxJr46isKDk9lNj0YubkZXqxbToXPZAJMgAn0LQLplelYc34NNl3eZDIrcLRXNO5JvAeL4hfB1dG125CayXpqT0ox1h7Pxq6LRWjWml76ODnYSXfi5aOjMG1gEJwcrvm9VLj6XtxCMQL/TArieeP9EUKhSBYyaKHiYgSKQdXRw1Thn1ej4dw5g2O0o7+DARSYPeCB+yGyDHOxnICO7tG040U4ti0L5fnGLWVETMERFE8waWo4hDUhFybQGwTUtAZOTU2FRqOR4k17q7H2HDkJSHsalr8/R387hg7VW87PnTsX27Zts7wxM88UloYjRoxAWlqaPGPdunUyLqD4YO3+iMQmJ06ckNfJz89Hd2IBPvTQQ/jPf/4jz92yZUu3k4CYcx/Lxtv9T4nP3+26z2+tRIAFQCuB5Ga6R0CJX0BqWvx0b7a4NhNgAkygZwmU1Jfgo+SP8GnKp0azB4se+Ln44c5Bd+KOQXfAz9XPok6VkXWfcP/94kQOzuRUdtlGAFkP3jYiXCYPSQr37pihVQiByZsoGchqEgLJ+s9YCaEHoGnPkRBIVoTCmlBBpYXGWLV1K4r+8io0RqwcHCm4e9AvfgGfxYsoccU1YqmCxmoLXRXCQ/rpYhwnIVDEAzRWREzB4TMjMXRaJFzcOTmLMU683zoE1LQGtkQ4sQ7FvteKsDMSyT9KSkogYgDm5eWhN9xdf/Ob3+CFF16QwF966SU8/fTT8r21+/P444/j9ddfl22/+eabePDBB82aZB39XRVZjMXzsLBkFElAfH19zTq3tZIl97ESn79bx8uv1iPAqzTrseSWmAATYAJMgAkwAQsIBLoF4tFRj2Ln8p14duyzCPcIN9hKeWM5/nn6n5i9djZePPQisquyDdYztVO4A987KQabfj4FXz1+Ex66KRbBXobjsIl2SkkwfGd/Jhb+7TvMe2Mf/rcvHcXkNiyLELuSFgM/2Q/c/i4QNFi//9r/F54FPr0beHMqWQ5uBQU3vLaGzX4Wgp7PrbcibtuXJPI9ZtDST1NUhPxf/hIZy5ej9tBhmx2LEjomMv/GjQzG7c+PwcKfD0dobOfELGIcIkvp4U0ZWPPLAzi+PROaJnYNVsL8ch+ZQF8iIBJNiVh8ogjLvP/973+9Mvzw8O/XEKIPrcXa/blPxMy9WkQsQq3WvO/hL774Qop/4tTFixd3W/xrvSa/MgFLCLAAaAk1PocJMAEmwASYABOwOgF3J3esTFyJrUu3YvXU1Rjsb1hQa9A2SGvBhRsW4ok9T+BsMQlsFpQBIV54fv5gHHhuBt69fywWUiIRZ0fjS6OLBdV4cWsyJlDikAfePYqtZ/LRIGKySSFwCfDTA8Dyd0gIHGS4NwXUz0/uAv5zM5BCrlAKEgLtyd038Cc/QdyO7fC9/XaDLs2NF5JxhR6Isn/2czRS0hAulhMQD6r9hgRg6dOjsPjxkYgcZNjitalBi0Mb0vEhJQtJOVwAYUHIhQkwASZgKwSElZz71RARwjLv4kVKmmVGEVZyH374YVvN7kQtE7EAW0tsbGzrW/lqrf6IxoYPH445c+bIds+cOYOXX35Zvjf1P2EN+cQTT8gq4nv+mWeeMVWdjzEBqxNgF2CrI+UGzSGgRBNkNbk/mDNHXIcJMAEmcKMJiAX/4YLDePfcu9ifR1Z2JsrokNG4P+l+TI2cCns74yKeiSbkocr6ZinsCRfh41nlXVWHj5sTbh0eJl2ER0T56l2ERdKS8+v1rsEll4y3ETaCXIOfBwbQA0Q7KwXjJ9jOkYaUSyhavRq1B0j0NFQoOLrfnXci8OGfwtHPsHhl6DTeZ5xAQXoluQZnIvNsqdFKwf28MGlZPCIGMHOjkPhAtwmoaQ1sietkt4HxCR0IiGy8IlOvKMIl+LPPPsPNN9MPYUbKhQsX8AsKK1FE1uWnTp2StX5CP0BFR0dDvPr7+xs5E9i5cyfmz58v4zwaczu2Rn9aO3DlyhUpBIrMw0LQe/XVV/HYY4/p1wKtla6+ZmVlYdGiRTh9+rTcI8S/1fR31JJiyX2sxOdvS9jwOaYJsABomg8f7SECSvwCUtPip4emlZtlAkyACfQYgZSyFLx3/j1sy9gGTYvG6HVifWJxX9J9WBC7AM4OzkbrmXMgvbgG607k0paDvMqGLk+JDfKQQuDSUREI83GjLMBXhUCRLKQ01fj54SP1QmDCbEUJgUKgrd23D4WrX0LT5csGx2dPmRKFCOh/112wc76++TB4gT64U8QGFG6/l08UGx19/+GBmLgkDn6hHkbr8AEmYC4BNa2BLRFOzOXE9YwTEDH5hAVgaxEJFYUYNnjwYOkCW1ZWJjP5bqWYs9u3b5futMLCrlUAXE4hJoTrrDP9HRECnxAQhwwZApFBVyR1EUk/Nm3aJMVFYT0oymuvvSaFxNZrtn+93v60b2vPnj1yLFVVVXL3mDFjpOvzsGHD2mIfijG999570hVaVFqxYgXef/99ODlZFsPVkvtYic/f7Tnze+sQYAHQOhy5lW4SUOIXkJoWP92cLq7OBJgAE7AZAgW1BfjgwgdYm7oWtc3Gs6UGuQXhrsF34QcDfwBvZ+/r6r+O3CoPpZdiLQmB284WoF64/ZoowphvSnygFAPnJIXCTSRrPfeF3iKwVJ+Z0ODpEaP1QmD8LGUJgfTwVbF2LYr/+jdo6SHOUHEiy43gJ5+E1+xbDFpGGDqH95kmUJRVhf1r05CXWmGwoj3FE0y6KQJjF8TAzYvFV4OQeKdZBNS0BrZEODELElfqkoDIyPsk/R3IzMzssm5SUpK0phNCoSjCqu6vf/1rl+eJCq6urvjDH/7QlvzD2EnX059r2zx79ix+/OMf4/Bh03FwhTv0c889h1//+tfX9bfQkvtYic/f13Lmz9dPgAXA62fILVhAQIlfQGpa/FgwZXwKE2ACTMCmCFQ3VePzS5/jwwsfoqi+yGjf3B3dsWzAMqwavAphnmFG65l7oKZRQyJgvswifCjdsNjVvi1PF0fMHxoqxcBx/SiL8Ll1eiGwzLDFnDw3YsxVIXCmooRAbXU1Sv/zH5S9twYtTU3tMbS9dxs9GiHPPQu3oZQZmct1ExBWmJlnSnBg3WVUFNYZbM/Z1QGj58Vg2IxIODo5GKzDO5mAKQJqWgNbIpyYYsPHukegif42rKUfjLZt24ajR49KN99q+tshsgPHxMRgwoQJENZ+06ZN6ySQCXdbYUm3jyzPz507B+FSK84VVnR+FGoiMTER06dPx6pVqxAVFWVWx66nP4YuICwY169fj/379yM/Px/i305gYCASEhIwb9482bf2SUoMtWHOPkvuYyU+f5vDgut0jwALgN3jxbWtRECJX0BqWvxYaRq5GSbABJjADSfQrKWYfRlbpXtwWoVx6zpHO0fM6T9Hxgkc6D/QKv3OLqvD+pO5UgzMKjUsvrS/ULS/O4R78LIRoYjKoWzAe18CytLbV+n4PnKsXgiMm6EoIbApJxfFFAep6ssvO46n3Sfv225FMAWHdwq7flG2XbN99q1Wq8OFfXk4siUDDTXNBjl4+btiwpJYJIwOgcg2zIUJmEtATWtgS4QTczlxPSbQWwQsuY+V+PzdWzz70nVYAOxLs21DY1XiF5CaFj82dCtwV5gAE2ACViEgLKH25e7Du+ffxdGCoybbnBg2EfcNuQ/iVQTtvt4irn2MEoZ8cTxHJhCpJivBrsq4/v64nYTAW+2/g+uBvwDlJrLmRo0nIfA5IHa6ooTAegreXvjn1RCvhoqdiwv8778PAT/6MRw8OVadIUbd3ddYr8EJig94+pscaDX6OFjXtiEShUxenoDwBN9rD/FnJmCQgJrWwJYIJwah8E4mcAMJWHIfK/H5+wYiVu2lWQBU7dTa9sCU+AWkpsWPbd8d3DsmwASYwPUROF9yHu+cfwc7s3ZC12JYBBFXGOQ/CPcm3Ys5MXPgZG9ZIO5re9pA8QF3nC8gq8BcfJdaTNe/tkbHz27kkjk/KRAP+R5DQsq/YFee2bFC+09RE4DplDW4/82KEQKFOFpNLltFr/wFzbm57UfT9t6B3KOCHn0EvsuWwc6BXVTbwFzHm6rSehzemI5LRwqNthI7IkgmCvENcTdahw8wAUFATWtgS4QTvguYgK0RsOQ+VuLzt61xV0N/WABUwywqcAxK/AJS0+JHgbcMd5kJMAEm0G0C2dXZeP/C+1ifuh4NWuNZfMM8wrBy8EoZK9DDyXqWaAWUObjVRTitqKbL/kd6O+JXUacxq5gyA1ZdMV4/epLeIrD/TYoRAnWNjSinjIcl/34TuhrDLFwGDEDws8/Ac/Jk42PnI90iUJgpEoWkIj+t0uB5MlHIzVcThXhyohCDkHgnC4B8DzABGyPAAqCNTYiCusMCoIImS01dZQFQTbPJY2ECTIAJ2DaBioYKfJLyCT6++DHKGown7vBy9sKKgStw16C7EOQeZLVBCSu4MzmVWEsuwptO56Gy3nCMttYLOkKDXwQdx32az+FZn9e6u/NrPxLKhGuwEAIVUjSUJbjk739H+aefAVrD2ZQ9br4JIU8/DZf4eIWMyra7Ke6/jNMiUUgaKovqDXbWmVJVj57XD8Omc6IQg4D6+E41/QhuiXDSx6efh2+DBCy5j5X4/G2D6BXfJRYAFT+FyhyAEr+A1LT4UeZdw71mAkyACVwfgQZNAzZd3oQ1F9YgqyrLaGPCHfjWuFtxb+K9iPWNNVrPkgONGi12JRfJxCG7U4qhNeEj7ERC4AqnfXjCZRP8NcZdOdFvit41OIZeFVIaL19G0Usvo+bbbw33mFyBfX9wO4J+/nM4BgQYrsN7u0VAJAo5vzcPR0WikFrDIrRXgCsmLo5D/Jhgq8TH7FYHubLNElDTGtgS4cRmJ4Y71mcJWHIfK/H5u89OcA8OnAXAHoTLTRsnoMQvIDUtfozPDB9hAkyACaifgFanxZ7sPTJO4Oni0yYHPC1ymkwYMip4lNUFkeLqRmw8JbII5yI5v8poP4QQuNzhWzzqtBFhKDFaDzFTSQj8JdCPXIQVUmoPHEDh6pfQmJJisMf2Hh4I+MlD8L/nHthT0hAu10+gsa4Zx7dn4fSubOg0hoNUBsd4U6KQeITHc6KQ6yeu/BbUtAa2RDhR/gzyCNRGwJL7WInP32qbN1sYDwuAtjALfbAPSvwCUtPipw/ecjxkJsAEmIBBAieLTuKdc+9gd/Zug8dbdw4LHCaFwBlRM+Bgb/1EFRfyqqRV4IaTuSitbWq9bIdXZzTjdhICH3bciAi70g7HOnwYtgKY80fAI7DDblv90EKuwJXr16PojTegLTYscDqFhyPoySfgPX++1YVYW+XS0/2qKqnHIUoUknrUuHVp7EhKFEIWgZwopKdnw7bbV9Ma2BLhxLZnh3vXFwlYch8r8fm7L85tT4+ZBcCeJsztGySgxC8gNS1+DE4K72QCTIAJ9GEC6ZXpWHN+jXQRbtYZdo8UeKK9onFP4j1YFL8Iro6uVifWTG6a35Jr8BcncvANuQo30edrixACf+CwRwqB4XZGYhq6+QGz/w8YcZdyEoXU1qL0rbdQ+vY7aGkwnLTFbfhwBD/3LNxHjrwWC3+2kEBhBiUK+cJ0opAh0yhRyPz+cPV0svAqfJqSCahpDWyJcKLkueO+q5OAJfexEp+/1Tl7N3ZULADeWP599upK/AJS0+Knz954PHAmwASYQBcESupL8FHyR/g05VNUNRl3y/Vz8cOdg+7EHYPugJ8riW09UCrqmrCZkoaI5CGnKYnItUUIgSscdpMQuAlhxoRA4Ra88HUgUDkJNZrz81H8+uuo3Ljp2iG3ffaePw9BTzwB58jItn38xnICMlHIqauJQooNJwpxcReJQmIwbFokHJzsLb8Yn6k4AmpaA1sinChuwrjDqidgyX2sxOdv1U/kDRggC4A3ADpfElDiF5CaFj98D/5/9s4DPopq++O/ZDe9kU5IIfReBEQQpBcVpKooT+lNfXZ9dv37sDyf+tSnPjug2BVQpEgvSu8ldEgnjfSezSb/O4uBbGZ2SdlNdnZ/8/nw2Zlz79x77vfOZ7hzcs49JEACJEAC5gkU6Yqw/OxyLD2xFBcLTWfidde4G7wBpYQhUOkzSgAAQABJREFUkb6R5httQOm59HxhCEzGikNJSMsrNWrJDWW4W7MZj2l/hq9TkVGZ4UIj9s4b9CQw4GFA6yovt1FJ8bHjSH/jDRTt36+ooZOLCwKmT0Pg/PnQ+Pgo1qGwbgT05RU4vj0Z+1bHorSwXPFm3yB39JMShfRmohBFQHYotKc1cH0MJ3Y4pRySygnU5zlW4/e3yqfJJtWnAdAmp8X+lVLjC8ieFj/2/4RxhCRAAiRgGQLlFeVYH7ceS2KW4GTWSZONOjs5Y3jUcMzsMhPdgruZrNfQAilr8J/nLmGZ8ApcF5OKUmGwqTqCkY2XXJZirGZ3lcjotyKoA5zH/VfEMfczktvyheSZlr9xI9LffAu6hARFVTX+/gh68O/wv/NOOGm1inUorBsBQ6KQtSJRyBbTiUJCW4lEIZPbIoyJQuoGV4W17WkNXB/DiQqnjCrbOYH6PMdq/P6282lskuHRANgk2NmpGl9A9rT44RNIAiRAAiRQNwKSIWpP6h4sOb4EOy7uMHtz79DeBkPgTRE3QTIMWuvIK9FhzdEUw36B++Kyr3Qz1PkQFrosRoSTckKN9PZTETzxNThJ+wSq5KgsK0P2d98h48P/oSJPOTTbtU0bhDz5BLwHD2aiEAvNqyFRyC/ncXZ/uskW20iJQia1gV+wp8k6LFA3AXtaA1+4cAGlpaWGd0SHDh34rlD3o+mQ2kvrkdOnT0P6dXV1RRvxf19tDjV+f9dmXKxTNwI0ANaNF2tbiIAaX0D2tPix0DSyGRIgARJwSAKns07jy5gvsTZ2LcorlcMkJTCt/VpjRpcZGNN6DFw11g29jbtUiOUicciyg8lIzimGJ0pESPBPmKn5HRqnStk8ZTr5Y0+Hp9Bt5DREBnrJym1VUJ6djUsffYTsb78DypXZe93YHyFPPQV38XHPwzIEUmNzsfPnc0g5L9+LUurBWeOEboMj0GdMNNy9mCjEMtRtpxV7WgNL3yD5+fkGuFFRUfDyUs/7z3aeCGrSlARKRJKs2NhYgwre3t6IjKzd9iNq/P5uSs722jcNgPY6szY+LjW+gOxp8WPjjwfVIwESIAFVEEgtTMXXJ77Gz2d/RqGu0KTOwR7BmNppKu7scCd8XX1N1rNEQYUIEd4Tm2XwClx7LAWtdGfxusvn6OYcp9j8Jv11WBH2CAb37Y1buoXB200dIbSl4uMn/e23UbBxk+K4hFsP/CZPQvBDD8ElJES5DqV1IiB5m1w4nIFdy88j10yikD63RhuMgUwUUie8Nl3ZntbAecKDODk52cBbMp5EiERCTuJ9wYME1EIgPT0dmZmZBnVDQ0MREBBQK9XV+P1dq4GxUp0I0ABYJ1ysbCkCanwB2dPix1LzyHZIgARIgASA/LJ8/HTmJ3xz4hukF5sOlfTUemJy+8m4t9O9CPMOszq6orJy/H48FSsOxKN9/Ld4XPMTPJ2ME4hIShRVuuHt8tvxg/MYjOoajsm9I9C/dSCcnW3/o7hw716k/+sNlJw4ocjTydMTwQ88YEgWwv0BFRHVWWhIFLJNJApZYz5RSP+JbdGmVzCNK3UmbHs32NMauKKiAmfOnDGET0qkJSOgZEDxFO8KGgJt79mjRlcJ6PV65OTkQDIAVh1S+K8UBlybQ43f37UZF+vUjQANgHXjxdoWIqDGF5A9LX4sNI1shgRIgARIoBoBnV6H1bGrDeHB53LOVSsxPtU6aTG61WjDPoEdAhonTPWiCAveuGs/2u3/P/TXK2fVPVYRjad1cxFT2Qot/NwxsVc4JvWKQJtgb+MB2NhVpfigz125EhnvvIvytDRF7dy7dEHYq6/AvWNHxXIK606gpFCHA2vjcHRrEirK5WHmUovNW4tEIbe3E79+de+Ad9gMAXtbA0shwJIXoOTVWnVIxj+NRlN1yV8SsCkC0rMqGQCrH8HBwQgKCqouMnuuxu9vswNiYb0I0ABYL2y8qaEE1PgCsrfFT0PnkPeTAAmQAAkoE5AW6n8k/2HIHLwvdZ9ypb+k/cP6Y0bXGZB+G8P7RDKWxW7/BsF/vgSf8sshRNUV1Fc6YbH+Zvyn/A4Uwd1QdF1UM4MhcFz3FvDztN393SqKi5G5eDEyP/scleJcdogMwYGzZyPo/vvg7OYmK6agfgSkcODdv57HOXOJQnqFoP/E1kwUUj/ETX6XPa6BlYyATQ6aCpBALQn4+fkhLCysTusGNX5/1xIHq9WBAA2AdYDFqpYjoMYXkD0ufiw3o2yJBEiABEhAiUDMpRgsjlmMDfEbUFFZoVTFIOsY0BHTu0zH6OjRcHFuBCNbcQ7K178E7aElijolVQbhBd1MbKm47kq5q8YZIzqHYLLwChzcPhhacW2Lhy4tHRn/fQ+5y1dAuPjIVHSNjkbYKwvh2aePrIyC+hNIvZCLHSJRiPSrdBgShQwRiULEHoFMFKJEyHZl9roGlsKBCwoKIO0LWCYyjdf0sLLdGaFmjkhA8lCVQtWbNWsGd/fLf6CrCwc1fn/XZXysWzsCNADWjhNrWZiAGl9A9rr4sfDUsjkSIAESIAEFAon5iVh6YilWnF2BEn2JQo3LojCvMNzT6R7DXoFeLo2QnTJ+F7DqESDjlKJOq/Q34GXdNGTA36g8yNsVE3pe3i+wU5h1E5sYdVyHi6KDB5Hy/Asou3BB8a5md01ByBNPQCP2AONhGQKS9+v5gyJRyIpzyLuk/Jy7eWoNRsBuwhio0dqmEdkyNOynFa6B7WcuORLHJaDG72/HnS3rjZwGQOuxZctmCKjxBcTFj5kJZREJkAAJkECtCOSU5OD709/ju1PfIasky+Q9Pq4+mNJhCqZ2nIpgz2CT9SxSUF4G7HgP2P4moJcnCcmr9MQb5XfhW/0wVEJusOksDIBS4pDxPVsgyNu2QmsrhFdP5scf49KnnwHl5TJcWpFBsflLIhx62FBZGQX1J6DXVeD4dpEoZLVIFFIk5y617BvsgYF3tEOr7rXfw6r+GvHOhhDgGrgh9HgvCdgGATV+f9sGOfvSggZA+5pP1YxGjS8gLn5U83hRURIgARKweQIl5SVYeX4lvjrxFeLz4k3qK4UD39bmNkzvPB2tm7U2Wc8iBZdE4hLJGzDuD8Xm9le0xzO6OThbGaFYrhVZg4d0CDaECA/rFAI3re1sqF9y+gxSXngBJUePKurue+stCH3uOWgDAxXLKawfASlRyH6RKOTYFpEoRC8Px5ZabSP2B7xpSjt4+dmW8bh+I7bPu7gGts955agci4Aav78da4YaZ7Q0ADYOZ/ZSg4AaX0Bc/NSYRF6SAAmQAAk0mIC+Qo+tiVsN+wQeyThitr0hEUMM+wT2Du1dp42/zTZas1DaM+/wt8D654Di7Jql0EGLj8rH4sPyCSiFq6y8SuDn4YJxPVoYPAN7RPhZT9+qDmvxWykyKGZ//TXS331PMUmIRmyqHvLM0/AbP94m9K3FkFRTJTejSIQFXxDhwemKOrt6aHHjpDboPKAFnIQhmYdtEeAa2Lbmg9qQQH0IqPH7uz7j5D3mCdAAaJ4PS61EQI0vIC5+rPQwsFkSIAESIAEDgUPph7D4+GJsSdxilki3oG6Y0WUGhkcNh8bZSl52hZeAdcIIePR7RV2y3CPxon4OVuW3UyyvLmwT7GUwBE68Lhxhfh7Vi5rkvCwpCakvvoTCnTsV+/caMADNX34ZrhHhiuUU1p9Ayvlc7Fx2ViQKyVNsJKytH4be0xH+zRth/0tFDShUIsA1sBIVykhAXQTU+P2tLsLq0JYGQHXMk91pqcYXEBc/dvcYckAkQAIkYJMELuRewFcxXxlChHUVOpM6RvpEYlrnaRjfdjw8tFYyrJ0XxshVjwLZsYp6pLWehPc10/HzqWKUiH3fzB1OwrFrYNsgTOoVjpu7hMHD1UrGS3NK/FUmJavI/eVXpP3rX6jIzZXd4eThgZBHHob/PffASWRe5GE5AhL707tT8efPZ1FaKN8f0FnrhN43R6P36JbQuMj3nLScJmyptgS4Bq4tKdYjAdsloMbvb9ulqV7NaABU79ypWnM1voC4+FH1I0flSYAESEB1BC4VX8K3J7/FD6d/QF6ZsseUNCh/N3/c1fEuw78A9wDLj1NXDGz7N7Dzv0CF3GADz0CUDFuIlZU3YdnBZOyJNZ3cpEo5L2H8u7VbmMEzsG90AJybKOyz/NIlpL76KvLX/l6lmtGve4/uCFu4EO7t2xvJedFwAsX5ZQYj4Jk9aYqN+Tf3xBDhDdiibTPFcgobjwDXwI3Hmj2RgLUIqPH721osHLldGgAdefabcOxqfAFx8dOEDwy7JgESIAEHJlCkK8Lys8ux9MRSXCy8aJKEu8bd4A04p9scNPdqbrJevQvSYoDfHgaS9ik30XoIMOY/SHQKw3JhCFx2MAkJWUXKdatJIwM8MPG6CJE8JBwtA5sm9DN/82ak/t/LKE9X2KPOxQVBc+cicMF8OLua3vew2pB4WgcCCTGZ2PrtaeRnlije1WVQOPpPaA03TxfFcgqtT4BrYOszZg8kYG0Cavz+tjYTR2yfBkBHnHUbGLMaX0Bc/NjAg0MVSIAESMCBCZQL77v1ceuxJGYJTmadNElC66zF5HaTYRVDoEhagv2LgE3/BEoVvBK17sDgfwA3PoRKocf++GwsO5CE1UdTkF+q4D1YYxTXR/sbsgjf2j0Mvu6Na/DR5+cj/e23kfP9DzW0unzp2qaNwRvQs9d1iuUU1p+ArlSPfaticXhTIiorRCKaGoennysGTWmP1tcFM0FLDTaNcck1cGNQZh8kYF0Cavz+ti4Rx2ydBkDHnPcmH7UaX0Bc/DT5Y0MFSIAESIAEBAFpD7U9qXuw5PgS7Li4wyQTqxoC84Qn4lph6Dv5m3L/IZ2B294DIvsaykt0eqyLSTWECP95NgMKNh6jdty0zhjdpbkhRFjaN1DTiCHCRfv2IeWFF1EWF2ekk+FCbGToP3Uqgh99FBrvpvFWlCtlP5KMhHxs+foUpF+lo1WPIAy6qz28/YWhmUejEeAauNFQsyMSsBoBNX5/Ww2GAzdMA6ADT35TDl2NLyAufpryiWHfJEACJEACSgROZ53GlzFfYm3sWpRXKnvYSYbASW0nGTwCw7zDlJqpv+zUGmDNE0BeskIbIuvH9bOB4S8C7n5XylNzS/DLYREiLDwDz6YXXJGbOgn1dcMEkUH49l4RaBfqY6qaReUVpaW49OH/kPnFF4BeeD3WOLRhYQh7+f/gPWhQjRJeNpRAhb4CR7ckYc/KCygvkyeWcXHXoN/4Nug6OLzJ9o5s6BjVdj/XwGqbMepLAnICavz+lo+CkoYSoAGwoQR5f70IqPEFxMVPvaaaN5EACZAACTQCgdTCVCw6vgg/n/kZpjIHW80QWCq8tTa/Auz5RIxUHr4Jb7Ef4a0iiUincRDxm1doSJ6Mx5JzDYbAX49cRE6R6YzHVTf1iPAzeAXe1r0F/L2svx9fycmTSHnueZScOFGlgtGv7223IfSZp6ENsELyFaOeHO8i71Ixtn13GgkxykllQlv5YqhIEhIY7u14cBp5xFwDNzJwdkcCViCgxu9vK2Bw+CZpAHT4R6BpAKjxBcTFT9M8K+yVBEiABEig9gRqawic2HaiwSOwhXeL2jd+rZrJB4CVIklI2jHlmh1uFYbANwG/CFl5WXkFNp9KNyQO2SJ+y68RI+yiccKwjiGG/QKHil8XjbOsTUsJKsvLkfXlV8h4/31UlsgTVWj8/RH67DPwHTuW+9NZCvpf7UhG4nP70/HHj2dQnC83EEvZo68bHYU+t0ZD66KxcO9srooA18BVJPhLAuoloMbvb/XStl3NaQC03bmxa83U+ALi4seuH0kOjgRIgATsikBaYdoVj8CyijLFsUkegRY3BOpFGPLu/wFbXgPKi+X9ugpvrWHPA33nAc7KBpvMglKsFB6BUhbh48kKiUZqtBogPAHH9WiB23tHoEsLX6sZ4coSEpDy4kso2r27hgaXL70GD0LYSy/BpYUFjaqKPTmesKRQh53LzuHkzhTFwfuFeGDI3zoiooO/YjmFDSPANXDD+PFuErAFAmr8/rYFbvamAw2A9jajKhmPGl9AXPyo5OGimiRAAiRAAlcINJkhMDsOWP04cG7jFV2MTlqITLpSkpCwHkbimhenU/MNhsAVh5KRkV9as1h23UHsETi5dzgm9AxHiK/lE0VIHmm5y5Yh7Y1/o0JkDa55OHt6Ivixx0SikLvh5Gw9r8Sa/TrKddLpbGz95hRy0xWMywJCxxvDMGByW7h7NW4GaXvnzzWwvc8wx+cIBNT4/e0I89LYY6QBsLGJsz8DATW+gLj44cNLAiRAAiSgVgK1NQROaDsBc7vNhUVCg4WxDMeXAb8/DRRmyNE5CQ/A/vcDQ54BXM1n1C0XiSH+OHfJsF/g+hNpkEKGzR1S0uBB7YMNIcIjO4fC3cLhobr0dKS98iry169XVMOjZ0+EvbIQbm3bKpZTWH8C5WV67F8bh0PrElChECru4eOCgXe2Q7s+oVbzBq2/9uq8k2tgdc4btSaB6gTU+P1dXX+eW4YADYCW4chW6khAjS8gLn7qOMmsTgIkQAIkYHME0ovSDaHBP53+CeZCgy1qCCwSSRw2vgQc/EqZR7MoYMw7QLsRyuU1pLnFOqw+mmLwDDwQn12jVH7p5+GCiSKL8B19pBBhP3mFBkjyNmxA6j//CX3GJVkrTi4uCFwwH0Fz58LJ1foJS2QK2LkgM7kAW74+hbRY5TDxqC6BGHx3e/gGedg5CesPj2tg6zNmDyRgbQJq/P62NhNHbJ8GQEecdRsYsxpfQFz82MCDQxVIgARIgAQsQqBJDIFxO4DfHgYyzyqPoetk4OZ/Ad4hyuUK0thLhVgu9gpcfjAZyTnKYaHVb5P2CLyzTyTG92yBZp6WMcrp8/KQ/uabyPnp5+pdXTl3a9fO4A3o0aPHFRlPLENA8gCM2Z6MXb+ch65EL2tU6+qMG8a1RvehEWLLSYZkywDVUsA1cC1BsRoJ2DABNX5/2zBO1apGA6Bqp07diqvxBcTFj7qfOWpPAiRAAiQgJyAZAhcfX4wfT/9o2iPQSYvxbcdjbve5CPcOlzdSF0m52Mfvz3eAP94G9ArJSdyFh97IhcB194okIbU32EiGoN2xmSJEOBlrj6egSISJmjtctc4Y3aW5MAZGYECbINGViBlu4FG4e49IEvIidCJZiOxwckLAtHsR/PDDkPYJ5GFZAgXZJdj+/RnEHpF7Yko9BUf5YOg9HQ2/lu3ZMVrjGtgx5pmjtG8Cavz+tu8ZaZrR0QDYNNwdvlc1voC4+HH4x5YASIAESMBuCTS6ITDjDLDqESBeeAUqHVE3iiQh7wrLTQelUrOywtJy/H48FT8fSMKuC5lm60qF4c08DBmEpSzCkQENM85VFBfj0ocfInPxEmHglBshXcLD0fzll+E9cMA19WKFuhGQErRcOJxhMAQW5cqNy07CyNtjeCT6jm0FFzflDNR169FxanMN7DhzzZHaLwE1fn/b72w03choAGw69g7dsxpfQFz8OPQjy8GTAAmQgEMQqDIE/nTmJ5TqlbPuai3lEVghEnkc/hpY/wJQkiPn6ywyud70GDBQ/HOpX0bfxKwi/LQ/0WAMvJhbIu+jhmRA20BDiLDkHdiQxCHFMTFIef4FlJ48WaOHy5d+EyYg5Kl/QOvvr1hOYf0JlBaXY/eK8zguQoOVDt8gdwye2gFRnQOViilTIMA1sAIUikhAZQTU+P2tMsSqUJcGQFVMk/0pqcYXEBc/9vccckQkQAIkQALKBDKKMi4nC6mFIXBOtzmI8IlQbqg20oJ0YN2zwLGflGsHtrvsDRg9ULm8FlK9CBHeIbII/yCMgRtiRBZhkVXY3OHrLsKee4ZjyvWRInGIb72yyVbqdAZPwEsffIDKMrlHmiYwEM2fexY+t9xSr/bN6c8yIOVcjiFJSHZqkSKO9jeEYuDt7eDhY5m9IBU7sRMh18B2MpEchkMTUOP3t0NPmJUGTwOglcCyWfME1PgC4uLH/JyylARIgARIwP4INKoh8NxGERYsvP1y4pVBXnfP5f0BPQOUy2spzS4sw6+Hk4UxMAknU/KueVenMClxSAQmCIOgv1fdjUWlsbFIfeFFFO3fr9iX99ChaP7Si3Bp3lyxnML6E9DrKnBwfTz2r41DRXmlrCE3L63BCNihX3MaYWV0rgq4Br7KgmckoFYCavz+VitrW9abBkBbnh071k2NLyAufuz4geTQSIAESIAEzBKQDIGLYy4nCzEXGjyu7TjM7Ta3/h6BZcJba5vIBLzzA6BSvocevIKB0a8D3W6HsNiY1flahdKecTEX8/Cj8Ar85VAy8krKzd7iKrLIjuwSaggRHtg2CJo6JA6pFOHOUpZgKVtwRUGBrB9nLy+EPPkEmt15J5zqkPxE1hAFigSyUwsN3oAp53IVyyM6+hvCgpuFNGwPSMXG7UDINbAdTCKH4PAE1Pj97fCTZgUANABaASqbvDYBNb6AuPi59ryyBgmQAAmQgH0TuFR8yRAaLGUNtqohMOUo8NvDwMWDykDbDAPG/AcIaKVcXkdpiU6PdTGpYr/AJPwpQoWvdbTwc/8rcUgkogJrbzTSpaUh9Z8LUbBpk2IXHn16I0yUu7W2zLgUO3FQYaUIAz+5MwU7l59DaZHc2Ktxccb1Y6LRc2QUNMLYy+MqAa6Br7LgGQmolYAav7/VytqW9aYB0JZnx45069Kli9FodGJfnLNnzxpkiYmJiIiIMCq3xQsufmxxVqgTCZAACZBAUxCoiyFQ2iMw0iey7mpWCA/AfZ8Dm/4JlMm95qD1AIY8DfR/ANCIhCEWOqTEIVIGYelfck7xNVu9sc3lxCE3d61d4hDJ8zB/3TqkLnwF+kx5lmInV1cE3X8/AmfPgpOL5cZ1zYE4SIXC3FL8+eNZnDsg9p5UOALDvTH0no4IbeWrUOqYIq6BHXPeOWr7IkADoH3NZ31HQwNgfcnxvjoRoAGwTrhYmQRIgARIgARUQUAyBC4+fjk0uESvnGVX46TBuDYiNLj73PoZAnOTgDX/AE6vVmYS2lUkCfkvENFbubye0gopccj5SyJEOMngHVhWbj5xiI9IHDKuRwtD4pBu4X7X3FNOn5ODtDf+jdwVKxQ1dOvYEWELF8KjmxgfD4sTiD16Cdu/O42CbIVs1yK6vPuQCNwwvjVcxbw6+kEDoKM/ARy/PRCgAdAeZrHhY6ABsOEM2UI9CKjxBcTFTz0mmreQAAmQAAk4BIFGMQSe/E0YAp8E8lMUmAqLTd95wLDnAXfLe27lFJVh5ZGL+GFfomHfQAUFjEQdm/vgjj6RmHhdOAKukTikcOdOpLz4EnRJwtBZ8xD7AQbMmIHgB/8OZw/h8cjDogTKxL6Pe1ZewNEtgr08Rwi8/d0w5G8d0bJroEX7VVtjXAOrbcaoLwnICajx+1s+CkoaSoAGwIYS5P31IqDGFxAXP/Waat5EAiRAAiTgQAQkQ+CS40vww+kfYBWPwBKRxGHTwsuhwUoWG58WwK1vAp3GWo368eRcQ3jwCpE4JLdYZ7YfF40TRnYONRgDB7ULNpk4pKKoCBn/fR9ZX30FiIQhNQ+XyEjhDfhPePXrV7OI1xYgkBabZ0gSkpmsEGou2u80IAwDbm8HNw/H9AbkGtgCDxmbIIEmJqDG7+8mRmaX3dMAaJfTavuDUuMLiIsf23+uqCEJkAAJkIBtEKitIfC2NrdhXrd5iPSt4x6BifsuJwlJj1EecEdhALzl34BfuHK5BaRS4pANJ9IMWYSlxCFiaz+zR3Pfy4lD7ugTgZaBXop1i48eRcrzL6D0zBnFcr/bJyP0ySeh8fNTLKew/gT0+goc2ZiIvatiodfJjbCSN+CwezshsnNA/TtR6Z1cA6t04qg2CVQjoMbv72rq89RCBGgAtBBINlM3Amp8AXHxU7c5Zm0SIAESIAESsKohUC+873a+D2x7AyhX2H/Q1QcY/iJw/WzAWWPVyUjKLsKyA8n46UAikrKvnTjkhlYBhr0Cb+kaBg9XY90qy8qQuWgRLn34P1SKpGk1D01wEJoLI6Hv6FE1i3htAQK5GUXY+s1pJJ3KVmyt800tMGByW4faG5BrYMVHgUISUBUBNX5/qwqwSpSlAVAlE2VvaqrxBcTFj709hRwPCZAACZBAYxGQDIFfxnyJ7099bzY0uF4egVkXgFWPARe2KA8nUoTNTvwICGitXG5BqZQ4ZNeFTINX4Nrjqbhm4hA3Lcb+lTikR4Rx4pDSCxcM3oDFBw8qaugzcgRChSHQJTREsZzC+hOQMjWf3JGCP38+C12JyEZd4/AJcMfQaR0R2dExvAG5Bq7xAPCSBFRIQI3f3yrEbPMq0wBo81Nknwqq8QXExY99PoscFQmQAAmQQOMRqK0hcGzrsZjXfR6ifKNqp5wUf3vsJ+D3p4GiTPk9LiLkdpTYO7DPLIj0vPJyK0hyi3QicUiyIYvwMbFv4LWO9qHeuFMkDpkgEocEebsZqleK/QCzv/8eGW+9DWmfwJqHs48Pmv/fS/AbM6ZmEa8tQCAvsxhblp4y6Q3YdXA4+k9sY/fegFwDW+BhYhMk0MQE1Pj93cTI7LJ7GgDtclptf1BqfAFx8WP7zxU1JAESIAESUAeBzOLMyx6Bp79HcblyyKzGSYM6GwKLsoD1LwCHv1YG0XYEME6EDfuKZCGNeJy4mGcID5YSh+QIw6C5Q+vshBGdQnHn9RGQEodoNc7QpaQg9f9eRsG2bYq3Nrv7LoQ+/TSc3S4bDhUrUVgvApI3YMwfF7Fj2TmUl8q9AX2D3DFsWieEt/evV/tquIlrYDXMEnUkAfME1Pj9bX5ELK0PARoA60ON9zSYgBpfQFz8NHja2QAJkAAJkAAJGBGorSFwTOsxmN99fu09AmO3A7/+HciJN+rPcOEuEmjc+jbQ7fZG8wasUqK0XI+NJ9INIcLbz2ZcM3FIqK8bJveKMGQRjg70RN7qNUh79VXos+X707l37ozw996Fq8gYzMPyBPIuFWPzVyeRfCZHsfHuQyPQb0IbuLgZ7+moWFllQq6BVTZhVJcEFAio8ftbYRgUNZAADYANBMjb60dAjS8gLn7qN9e8iwRIgARIgASuRcAqhsDSfOEN+DxwYIly953HA2PeAbwClcutLL2YUywShyThR5E4JDFL2Quyugp9owOEV2AkRke4Ie+tN5H322/Viw3nUkhw2GuvwnfkSFkZBQ0nUCn2eDy2LRm7VghvwDJ5pmC/YA8Mm94JLdo2a3hnNtQC18A2NBlUhQTqSUCN39/1HCpvM0OABkAzcFhkPQJqfAFx8WO954EtkwAJkAAJkIBEwGAIPHE5WYi50OA6eQSe3XDZG7AgVQ7ZSyTQkEKCO9wsL2skiZQ4ZHdsJn7an4Q1x1JQWi43LFVXxUtkDb5NJA65O/sY3D54C5Ul8gzIAdOnIeTxx+Hk6lr9Vp5biEBOepHBGzDlnMLejmKLyR7DI9FvXGtoa2R4tlD3jd4M18CNjpwdkoDFCajx+9viENggaADkQ9AkBNT4AuLip0keFXZKAiRAAiTggASySrKwJGaJIWuwKUOgs5PzlT0CW/q2NE9J2htwzZPA8Z+V6113LzD6NcDdV7m8kaS5xTr8duSiMAYm4kiSgnGphh6DtLl4ZMcSeKQk1igRQ+nRHRHvvAOXFo2736FMETsVSN6AR7ckYdcv56HXyY22zUI9MVx4AzZvLULOVX5wDazyCaT6JCAIqPH7mxNneQLOlm+SLZIACZAACZAACZAACZBA/QkEuAfgsd6P4ffJv2Nm15nw0HrIGquorMDK8ysx7pdxeO7P5xCfp7DfX9VdngHA7V+If4sBD4VkDYeWAh8NAGL/qLqjSX79PFxwT7+WYvvCgfj9kZswe2ArBHiZ9uLbXu6Hqb0X4M+WvWX6lhw5itiJk5C/dausjIKGE3ASyVokT7+7nu8rjHxyw3FOWhGWv3kAO6XkITp58pCGa8AWSIAESIAESKBuBOgBWDderG0hAmr8CwT/+mmhyWczJEACJEACJFBHApJH4JcxX+K7U9+ZzBpca4/AfBEKvPJB4Ox6ZS363Q8MfxFwkRsdlW+wrrRMhARvOpmGH4RX4PYzGRCOZ/JDZKq9JX4PFhz9Ba4V5bLywLlzEfzwQ3DSamVlFDScgBTGfWRjIvasvAC9Qgi3f3PJG7AzQlvJDYUN7936LXANbH3G7IEErE1Ajd/f1mbiiO3TAOiIs24DY1bjC4iLHxt4cKgCCZAACZCAQxOorSFwTKsxmNd9HqL9opV5CYMZDn4FrHsWKCuQ1wlqD0z8BAjvJS9rQklKbjGWH0w2ZBGOzyySadImJxnP7vsKLQozZWUefXoj/O3/wCVU7HvIwyoEslIKsenLk0iPy5O17yT2BrxudEv0HdMKGhd1BWFxDSybTgpIQHUE1Pj9rTrIKlBYXf/7qAAoVSQBEiABEiABEiABErAOASk0+NHej2Ld5HWY3XW2ydDg3y78hvG/jsczfzyD2NxYuTKSNab3dOC+HUBLEfpb87h0Bvh8BLBF7Auo19UsbbLrMD8PPDC0LbY+MQRLZ/fF0A7BRrqcbxaOB4c8gj9adDeSSxfF+w+IkOCJKNghxszDKgQCwrww+cle6DehNZy14hmrdhhszr/H48fX9yE9Xm4grFaVpyRAAiRAAiRgFQI0AFoFKxslARIgARIgARIgARKwFgF/d3880vuRaxoCV11YhQm/TsBT25/ChZwLcnX8o4Hpq4BRrwIaN+PySrFv27Y3hCFwOJB+0risia+chAHzpnbBWDyzLzY9PljsGxgFDxeNQasiEbr82vX34qNuE6BzuiyrUleflYXEOXOR8d/3UannvnRVXCz566xxRu+bo3HnM9cjOMpH1nTWxUL8/MYBk+HCshsoIAESIAESIAELEWAIsIVAspm6EVCjCzLDH+o2x6xNAiRAAiRAAo1FILsk27BH4LenvjW5R6ATnDA6ejTmd5+Ptv5t5aqlnwJWzAdSDsvLJOPg8BcAaX9AZ2Ojmrxy00hyisrw/b5EfLkzDim5JQYl2mcn4Nm9SxFanC1TStejNzp9ILIEBxt7EcoqUlBvAnp9BQ6tS8C+1bGo0Ms3bwwM98bwGZ0QHCk3FNa7UyvcyDWwFaCySRJoZAJq/P5uZEQO0R09AB1imjlIEiABEiABEiABErBfAtU9Aud0mwNPradssJWoxO9xv2PSykl4fOvjOJMtwnyrHyEdgTkbgSHPADU856AvBdY/DywZC2QphBRXb6eJzpt5umLB4DbY/o+heP/u69AzshnO+Efh70Mfxe7mnWVauRw5gAOjx2LVV6tQwiy1Mj6WEGiEN2CfW6Nxh/AGDIr0ljWZmVyAn1/fbzAQSsZCHiRAAiRAAiRgTQL0ALQmXbZtkoAa/wLBv36anE4WkAAJkAAJkIBNEZA8Ar868RW+PfktisrlyTKqlB0RNQLze8xHxwBh/Kt+JB8U3oALgEunq0svn7t4AaNFyHDvGcJQaLzPm7xy00oOJmRj0Z+xWHssBRPObMXME2ugqTQ2NOmFZ+TPPcbAd+Ys3HNjK4T4ujet0nbau2TgO7A2HgfWxEHKGlzzkAyEI2Z0huQVaGsH18C2NiPUhwTqTkCN3991HyXvuBYBGgCvRYjlViGgxhcQFz9WeRTYKAmQAAmQAAlYjUBuae4VQ2CBTiHb7189D40cajAEdgnsclUXXTGw+RVg14dCJjfYoO1IYNz7gG/Y1Xts9Cw5pxhf7YrDwVVb8dCOLxFUkivTdF9IB7zbdyqG9G2PWQNaoWu4n6wOBQ0nkJGQLzIFn0BmcqGsMWeNE64f2wq9RkWJSHPbCdTiGlg2VRSQgOoIqPH7W3WQVaAwDYAqmCR7VFGNLyAufuzxSeSYSIAESIAEHIGAZAj85uQ3+PrE18jX5Zsc8uCIwVjQYwG6BnW9WifuT+CX+4CchKuyqjP3ZsCYt4Fut1dJbPq3qKwcv245Dtc3F6Jz0gmZrhkefni9z704GRiNvq0CDIbAkZ1DoXG2bU9H2UBsXKAvr8B+4Ql4QGQFrlTwBgxp6YPh0zsjoIXwNrWBg2tgG5gEqkACDSSgxu/vBg6ZtysQoAFQAQpF1iegxhcQFz/Wfy7YAwmQAAmQAAlYk0B+Wb7BELj0xFLkleWZ7Gpg+EDc1+M+dA/ufrlOqTAarnsWOPiV8j1dJl02BHoGKJfbmFRfrseB19+F17dfwLnS2Lux3MkZi7qMwYo2gwwhzpEBHpghQoPv7BMBH3cXGxuJutVJj8/DxiUnkZ2i4A2odcINt7VGzxGRTe4NyDWwup8zak8CEgE1fn9z5ixPgAZAyzNli7UgoMYXEBc/tZhYViEBEiABEiABFRAoKCvAd6e+w5cnvoTkHWjquLHFjQZDYM+QnpernFkHrHwQKEiT3+IdKkKCPwDaj5KX2aikcPceJDz6GJCdJdNwZ/MueKfXFBS4Xk6o4u2mFUbASGEMjEZUoDzJiqwBCmpFQK+rwN5VsTi0XngDGttiDfeHtvIV3oCd4N+86bwBuQau1VSyEgnYNAE1fn/bNFCVKmc7m0uoFCDVJgESIAESIAESIAESUBcBb1dvzO0+F+smr8MjvR6Bv5u/4gB2XtyJe9feiznr5+BA2gFh3BsN3L8b6DJRXl8yCn57hzAQPgRIHoMqOLz63YB2K3+B5w03yLS9MTUG7299B+2yEw1lBaXlWLQjFoPf2oJ5X+3HnguZwmClYLGStUSBOQIaF2f0n9gGk/7RWxj55IbVtNg8/PDKPhzakKCYPMRc2ywjARIgARIggeoEaACsToPnJEACJEACJEACJEACDkPAS2T0nd1tNn6f/Dse7/04AtyVQ3j3pOzBjN9nYNa6WdiXdx64Ywkw+QtA2gOw5nHwS+CjAUDcjpolNnmtDQ5G1KIvEHS/2OewRlbj5kXZePuPDzD2ghjLX8Y+6Wf9iTRM+XQ3xr7/J5YdSEKpCCnm0TACzVv54c5nr0fPkVFiHozbkvYM3LnsHFa8dRA5aaazWhvfxSsSIAESIAESMCZAA6AxD16RAAmQAAmQAAmQAAk4GAFPF0/M6DrDYAh8ss+TCPIIUiSwL3WfwQgoGQN3B0Wg8r5dQNsR8ro58cCSMWLfwOcAXYm83MYkThoNgh96CJGffgqNv7E3pEuFHg8cXYGn938NzxpjibmYh8d/OoKBb2zBfzedRWZBqY2NTF3qaF01GDC5LSY90Rt+IR4y5VMv5OKHV/fi+PZkel/K6FBAAiRAAiRwLQI0AF6LEMtJgARIgARIgARIgAQcgoCH1gPTukzD2klr8dT1TyHYI1hx3FI48Nz1czF959PYOfQxVI55BxDehMaHcJXbJfYE/HQwcPGQcZGNXnnfNBCtViyHR69eMg0HJx/B+9veQ6vci7KyjPxS/GfDGfT/12Y89fNRnErNk9WhoPYEwtr4YcrzfdFjWKTMG7C8rALbvj2N1f87isJcGlxrT5U1SYAESIAEaADkM0ACJEACJEACJEACJEAC1Qi4a91xT+d7sHbyWjzT9xmEeIZUK716eij9EOZvXIB7Mjbhz8n/RWVU/6uFVWcZp4DPhZfg1n8Bel2V1GZ/XZo3R8svlyBwzmyZji0KMvDBnx/g7oyDV0KCq1cqE6GqP+xPxM3v/oG/fb4bm0+lcd+66oDqcO4ivAEH3tkOEx+7Dr5B7rI7449l4vuFe3HhcIasjAISIAESIAESUCJAA6ASFcpIgARIgARIgARIgAQcnoCbxg1TO001eAQ+f8PzaO7VXJHJ0YyjuG/Py/hbiD+23zgXlRpX43oV5cIA+DrwxUgg47RxmQ1eObm4IOSJJxDxv//B2c/PSENnXRmm7fgWP+esx8CIml6PV6vuOJeJWUv2Y/h/tuGrXXEoFElEeNSdQIt2/rjrhRvQdVC47OaSAh3WfnwMm786ibIS8pUBooAESIAESMCIAA2ARjh4QQIkQAIkQAIkQAIkQALGBFyFQW9KxylYPXE1Xuz/Ilp4tTCu8NfVsczjeCBlHe7qeiO2hHeCLEeuFAr88U0iNPhDCNc4xTZsSegzbChaL18G9+7dZWp5bduAl9e+hVW3hWNSr3C4aGpkrvjrjthLhXjx1xj0f30TXl9zEsk5xbK2KDBPwMVNg8FTO2DMA93h4VvDuCxuPbkzRWQK3ouUcznmG2IpCZAACZCAQxOgAdChp5+DJwESIAESIAESIAESqC0ByRB4R/s7sGriKrx848sI95Z7ZUltnci7gIdcC3Fn++7Y5OUFI1OfXuzbtu5Z4MvbgGyRLMTGD5fwcER/vRT+0+6VaVp2/jy0f5+Fl9zisePpYXhoWFsEeMkNVNKNecJD7ZPtFzDo31vwwLcHcSA+m4ksZETNC6K7BeHuF/qiVQ95kpq8SyVY8fZB7P7lPKSswTxIgARIgARIoCYBp0px1BTymgSsTSApKQmRkWJjY3EkJiYiIiLC2l02uP3i4mKsX7/e0M6oUaPg4SHPztbgTtgACZAACZAACZCAagjoKnRYdX4VPjv2GRLzE03q3V7vhPmX0jGiqBhGf3139QZuFqHB1wnjmpOyB53JRpugIG/deqQ89xwqCgpkvTe743aEirIyjQtWHr6IRTtiRTKQfFm96oIeEX6YOaAVbu0WBletEZnq1Xheg4D0+XZqVyr++PGMSDKtr1EKBEV6Y+TMLghoYTpEW3aTGQHXwGbgsIgEVEJAjd/fKkGrKjVpAFTVdNmPsmp8AXHxYz/PH0dCAiRAAiRAApYkUC72+FsTuwafHv0U8XmmvfralpVhfk4eRhYWQVNdgXajgXHvAz6h1aU2eV4WH4+kRx5F6cmTMv3cOnRA+LvvwK1VK4N3387zmVj0Zyw2nUqX1a0uCPZxwz03tMTUG6IgnfOoHYG8S8XYuOSECP3Nld2gcXFG/4lt0H1IBJycG2Zc5hpYhpcCElAdATV+f6sOsgoU5p/aVDBJVJEESIAESIAESIAESMB2CWidtRjXZhx+Hf8rXr/pdbTya6Wo7DlXVzwZEoRJ4WFY7eWJK75bZ9cB/+sHxKxQvM+WhK4tWyL6++/Q7K4pMrVKT59G3O13IG/tWuHQ6IQBbYPwxYzrsfnxwZjWvyU8XIzMnlfuz8gvxTsbz2DAvzbjsR8P41iS3KB1pTJPrhDwDfLAhMd6od+E1nCusQejXleBP388i9/eP4yCbBF2zoMESIAESMDhCdAD0OEfgaYBoMa/QPCvn03zrLBXEiABEiABElAbAX2FHuvj1+OTI5/gfO55k+pHl+kwLzcXtxQUQVtVq+vtwK1vAp4BVRKb/c39bRVSXnoJlUVFMh39p05FyNNPwVkYPauO3CIdftifgC93xl8zGUiflv6YMSAao7s0FwlG6LNQxdDUb0ZCPjYsPoHslEJZFTdPrSGJSLs+9fMw5RpYhpQCElAdATV+f6sOsgoUpgFQBZNkjyqq8QXExY89PokcEwmQAAmQAAlYj0BFZQU2xG/Ax0c+xrmccyY7itLpMFeEBo8tKLxsCPQJEyHBHwDtRpi8x1YKSi9cQPLDD6P0rHx87l26IPy9d+FaY6/ncr3gciINi3fEYW9cltmhhPm5455+LXF33yiTCUbMNuBAheVlepEE5AKObFbej7Ld9aEYfHd7uHm61IkK18B1wsXKJGCTBNT4/W2TIFWuFP+cpvIJpPokQAIkQAIkQAIkQAK2ScDZyRmjo0dj2bhleGfIO+jg30FR0QQXF7wQHIjbIsKwwtsLuvwU4JvJwG+PAKXyhBuKjTSR0K11a0T/+CP8Jk6UaVASE4PYiZOQv2mTUZlWePTdIhJ//LigP1Y9OBB39I4wmQQkJbcEb647jf6vb8JTPx/FyZQ8o7Z4cZWA1lWDgXe2w7iHe8KrmXwvxbP70vD9wr1IOmXe6Hq1RZ6RAAmQAAnYEwF6ANrTbKpoLGr8CwT/+qmiB4yqkgAJkAAJkIANEpA8ArcmbjV4BJ7MkifRqFI5XFeOOSI0eHx+IVz8o4EJHwMt+1cV2+xvzrLlSF24EJUlJTIdA2bMQMjjj8FJGDuVjsyCUny3NwFLd8cjLc/8nnX9WgcYsgeP6BQKTQMTXCjpYg+ykkIdtn93Gmf3Kydg6TEiEv3Gt4bWxL6M1RlwDVydBs9JQJ0E1Pj9rU7Stq01DYC2PT92q50aX0Bc/Njt48iBkQAJkAAJkECjEqisrMT2pO346MhHiMmMMdl3WLkwBIrQ4AnCEOh644PA0OcAF3eT9W2hoOT0GSQ/8gjKYmNl6nj06IHwd/4DlxYtZGVVAp0ID157PFWEB8fiUEJOlVjxN8Lfw5BcZEqfKPjVMaxVsUE7FJ7Zl4pt355BWXG5bHQBLbwwclZnBEX4yMqqC7gGrk6D5ySgTgJq/P5WJ2nb1pohwLY9P9SOBEiABEiABEiABEjAzghIGXIHRw7Gd2O+w4fDP0S3oG6KI0zRarEwKAC3Robhu2OLUPrpYCDliGJdWxG6d2iP6J9+gu+YMTKVio8cMYQEF2zbJiurEkgJP8b1aIEV9w/ALw8MwMTrwkUSEKeqYqPfpOxivLbmFPqJ8ODnVhzDufR8o3JeAO2vb467XuiL8A7+MhxZFwvx0+v7cXBdPCoqKmXlFJAACZAACdgXAXoA2td8qmY0avwLBP/6qZrHi4qSAAmQAAmQgKoISB6BOy/uNHgEHskwbeALER6Bs/IKMbnnArgP+geguZI72ObGK40p54cfkfbqq6gUSU5qHoHz5iH4oQfhJIyc1zrS80rwzZ4E8S8elwrKzFa/qV2QCA+OxpD2IXBmePAVVpXCwHd0SxJ2rTgPfXnFFXnVSYt2zTB8eif4BnlUia78cg18BQVPSEC1BNT4/a1a2DasOA2ANjw59qyaGl9AXPzY8xPJsZEACZAACZBA0xOQjGa7U3Yb9gg8mH7QpEJB5XrMhC/uGPMZPJorew+avLmRC4pFIpDkRx6FLlGemdZrwABEfPA+nD3kRiclNUvFuFcfTTFkDz6WnKtU5YosOtAT02+Mxu0iwYiPu/K+g1cqO9BJZnIBNiw+gcwkeXIZF3cNBt3VHh1uaA7JS7Xq4Bq4igR/SUC9BNT4/a1e2rarOUOAbXduqBkJkAAJkAAJkAAJkIADEZCMLv1b9MeSm5fgi1FfoE9oH8XRX9Jq8Ka2EDevuQtL1sxHkQ1nCvbo0gWtli+Dz6hRsrEU7tiBxLnzoC8olJUpCdzEuCf1isDKvw/Asvv6Y0z3MJNJQOIyi/DybydE9uDN+L+VMYi7VLs+lPq1J1lguDfueKoPeo2OAq7a+AxD1JXosWnJSaz79DiKr+FpaU9MOBYSIAEScBQC9AB0lJm2sXGq8S8Q/OunjT1EVIcESIAESIAEHIDAvtR9+OTwR9iTts/kaJvBGdM6TcPdPefD29XbZL2mLJC8G7OXfo20N98EaoQES8lBIj/7FBpf3zqrmJJbjKW74g0ZhLOL5KHGVQ1KDm1DO4QYwoMHtg0y8nCrquNovxfP5mCj8AbMz5Jnbfb0dcUwERLcsksguAZ2tCeD47VHAmr8/rbHeWjqMdEA2NQz4KD9q/EFxMWPgz6sHDYJkAAJkAAJ2ACBg2kH8fHef2NXlumswT4ad9zbdRamdpoKPzc/G9BarkLRoUNIXHAfKnKNQ3jdOndC1BdfQOsvT1Yhb0UuKdHp8evhZEN48KlU88lA2oZ4G8KDJ/cKh6frtfcglPdmPxIpO/AfP57BqV2pioPqOjgcvW4Nx+atmwzlo4Qnp0ctQ7YVG6SQBEigSQio8fu7SUDZeac0ANr5BNvq8NT4AqIB0FafJupFAiRAAiRAAo5D4PDFPfh429PYUXbJ5KC9Xbxwd8epuLfzvfB3r59BzWTjFigoOX0aCTNnQZ+VZdSaW7t2iFokjIDBwUbyulwY9lG8kIUlO2Ox4UQazCW39XXXYsr1kZjWPxqRAZ516cbu6p4/lI6tX59GSaHci9I32B3ubTPh2qwCNADa3dRzQA5CQI3f3w4yNY06TBoAGxU3O6sioMYXEA2AVbPHXxIgARIgARIggaYmcOzwEnyy701sczW9pbeH1gN3dbgL07pMQ5BHUFOrbNR/6fnzSJgxE+UZGUZy1+hoRC1ZDJfmzY3k9blIzCrC0t3x+H5vAvJKyk02ISULHtEpVIQHt0K/1gEOGx5cmFuKzV+dQkJMppyVUyV825Zh0vzB8PJ2bGOpHA4lJGD7BNT4/W37VNWnIQ2A6pszu9BYjS8gGgDt4tHjIEiABEiABEjAfggUZ+PkyvvwaeY+bPQybZRxF6HBt7e/HTO7zkSIZ4jNjL8sPh7xM4UR8GKKkU4uERHCCLgErhHhRvL6XhSVlWP5wWThFRiHc+ny7LfV2+3Y3MewT+D4nuFwd9FUL3KIc8mDMmZ7Mnb8fA7lugrZmEOivXHzvO7wCXCXlVFAAiRguwTU+P1tuzTVqxkNgOqdO1VrrsYXEA2Aqn7kqDwJkAAJkAAJ2C+BYz/jzLon8JmHM9YJQ2CllPFC4XB1dsXEdhMxu+tshHmHKdRofJEuOVkYAWdBl5Bg1LlWeAC2FJ6AkkegpQ7JuPXnuUtYsiMOm0+nQ1yaPPw9XXB33yjDP0cMD85OLTQkCEmPl++n6OalxYgZnRHdzba8Sk1OJgtIgASgxu9vTpvlCdAAaHmmbLEWBNT4AqIBsBYTyyokQAIkQAIkQAJNQyA3Gfj1AVxI/AOfNfPDGmEIrDBhCNQ6azG+zXjM6TYHET4RTaNvtV51aeliT8CZKLtwoZoU0AQHoeWiRZD2BrT0EXepEF/uisNP+5NQUGo6PFhCOKhdsMEQOLxTCFw0pkOuLa1jU7en11fgwJo47F8bh0q5MyCuGxWFG8a3hsaBmDT1nLB/EqgvATV+f9d3rLzPNAEaAE2zYYkVCajxBUQDoBUfCDZNAiRAAiRAAiTQcAIVwkqz7zNgw4tIQDk+b+aL37y9UG7CEKhx0mBs67GY230uWvq2bHj/DWihPDMTCbNmo1QkCKl+aERWYCkxiHunTtXFFjuXjH8/708UxsB4xAqjoLkjxMcNd/aJNCQOcSSvwPhT6Vj7yVHoi+XGz7A2fhg1pwu8/RkSbO7ZYRkJNDUBNX5/NzUze+yfBkB7nFUVjEmNLyAaAFXwYFFFEiABEiABEiABIOMMsHwukHIYyVoNvvDzxQofb5OGQGcnZ9zS6hbM6zYPrZu1bjKC+pwcJMyZi5Ljx410cPb1RdTnn8Gje3cjuSUvKkS64G1nMrBoRyz+OGs6w7LUp6N5BUpr4N9Xr0f2MXcUp7nIsLt7uWDEzM5o2TVQVkYBCZCAbRBQ4/e3bZCzLy1oALSv+VTNaNT4AqIBUDWPFxUlARIgARIgARLQ64Dtb4p/bwGVeqRqNFgkDIHLhCGwTEp7q3A4wQkjW47EvO7z0CGgg0IN64v0+flInDcfxYcOGXXm7OWFyE8+hmefPkZya1ycS8/HUuERuPxQMvLNZA+W+nYEr8CqNbC0Z2KkRzfsXRmPCr18A8VeN7fEDbe1gjNDgq3xWLJNEmgQATV+fzdowLxZkQANgIpYKLQ2ATW+gKoWPxKbUaNGwcPDw9qY2D4JkAAJkAAJkAAJNIxA4j5gxTwg6/L+ehnCOLNEGAJ/FIbAEmd5SGdVZ8Mih2F+j/noHNi5StRovxWFhUi8/wEU7dlj1KeTWHtFfvgBvG680UhurYviMj1WHb2I7/Ym4GBCjtlu7NkrsOYaOC9Nh3WfHUd+ZomMSVhbERI8u6sICXaTlVFAAiTQdATU+P3ddLTst2fT/+vb75g5MhIgARIgAa7OAywAAEAASURBVBIgARIgARJwDAKR1wML/gT6zDaMN1gkdngyKwe/J17ErJxceEr7BiocmxM3Y8qqKXhg0wM4mnFUoYb1RFXefl433WTUSaUIRU1ccB/yt241klvrwsNVgzvEnn/L7x+A3x+5CdP7t4SPu1axO8k7TgohXvD1AQz412a8te40ErOKFOuqXRga7Ys7n70erXrIswCnnMvFj6/tRcKJTLUPk/qTAAmQgN0RoAeg3U2pOgakxr9A1PzrJz0A1fGsUUsSIAESIAESIIG/CJzdYMgUjIK0K0hyhBfgUl8ffCu8AgtMhAZLlfuH9Td4BPYO7X3lXmufVJSVIfnRx1CwaZNxVy4uCH/7LfiKiIzGPhzRK9DUGrhSWD2Pbk7CzmXnIO2haHSIKPM+t0Tj+rEiJNjMc2V0Dy9IgASsRkCN399Wg+HADdMD0IEnn0MnARIgARIgARIgARJwIALtRgL37QI6jbsy6GbCA/BB4Qm4LjEJ92fnwtdJnuRBqrwrZRdm/D4Ds9bNwp6UPZCMP9Y+nF1dEfHuO/C99RbjrnQ6g2Ew97dVxvJGuKJX4FXITiLuucfwSEx8she8A2qE/IrHY/+aOKx89xAKc0uv3sQzEiABEiCBJiNAD8AmQ+/YHavxLxCm/vrp2DPJ0ZMACZAACZAACaiOgGS8O/oDsOZJoDTPSP0CYdT5vkUbfOXlhmxdvlFZ9YuewT2xoMcC3NjiRpEVVzmpSPX6DTmv1OuR8tzzyP3lF+NmRL9hryxEs8mTjeWNfGXvXoG1WQOXFOqw6cuTiDsqz6Ds4eOCkbO7ILJjQCPPDLsjARKoIqDG7+8q3flrOQL0ALQcS7ZEAiRAAiRAAiRAAiRAArZPQDLY9bhLeAPuAKKN99nzFsbBOcnn8Pv5s3gi8HoEugcqjudwxmEs2LgAU1dPxdbErVb1CHQSGYzDXnsVze6aYqyL0FUyDGZ9842xvJGv6BUIuHu54Nb7uuHGyW1lIb/F+TqsfO8w9q6KlYcKN/JcsTsSIAEScGQCNAA68uxz7CRAAiRAAiRAAiRAAo5LoFkUMG0lMOpVQONqxMFTX4bp+5fh90I3PN11HkI8Q4zKqy6OZx7Hg5sfNCQM2Ri/ERWVyklFqurX99dJ7FXY/KWXEDB9mqyJtIWvIPOLRTJ5Uwg6NvfFy+O7Yu+zI/Dm7d3RK6qZSTXS80vxwZZzGPTmFkxftBe/H0+FTiRpUesheYJeNzIKE58QIcE1swALp9N9wgD4238PoyivTK1DpN4kQAIkoGoCDAFW9fSpV3k1uiDXJvxBvTNCzUmABEiABEiABByaQNoJYPk8IO2YHIOrN8pGLcQv3l74/PgXSClMkdf5S9K2WVvM7z4fI1uOhMZZY7JefQukvQcz3n0PmZ98Imsi6KEHEXTffVYPSZZ1fA3BqdQ8fLcnAcsPJSO/pNxs7RAfN9wpMg9PuT4SkQGeZus2VmF91sAlBTpsXHIC8cfl2YA9fV0xSoQEh3fwb6whsB8ScHgCavz+dvhJswIAGgCtAJVNXpuAGl9A9Vn8XJsEa5AACZAACZAACZCAjRAoF8katr4O/PmuUEghyUeHW6Eb8zZ+S9uNz45+hqSCJJOKt/Jrhbnd5uKWVrdA66w1Wa++BZc++ggZ7/1XdnvgvHkIfvQRmzMCSoqqda/A+q6BK0Vm4EMbErD71wuQzqsfUhR639taoffN0XBiluDqaHhOAlYhoMbvb6uAcPBGaQB08AegqYavxhdQfRc/TcWY/ZIACZAACZAACZBAvQjEi0zBK4Q3YE6C/HbPIGDc+yhvPwprYtcYDIFxeXHyen9JIn0iDYbAsW3GwsXZxWS9+hRkLlqM9H//W3ar/7R7EfrMMzZpBKxSVk1egQ1dA188l4P1n8egMEeeDTiykz9GzOwCySuQBwmQgPUIqPH723o0HLdlGgAdd+6bdORqfAE1dPHTpMDZOQmQAAmQAAmQAAnUhUCpyAD8+zPAoaXKd/USe/GNfg16F0+sj1+PT458gvO555XrCmm4dzhmdZ2FCW0nwLXGfoMmb6pFQda33yLtnwtlNZtNmSL2DHxReJfZ9pbnVV6B3+5NwKGEHNk4qgskr7lB7YLxtxuiMKxjCLSaxhmbJdbAxfllhpDghJis6kMynHv5iZDgOV3Qoh1DgmVwKCABCxFQ4/e3hYbOZqoRoAGwGgyeNh4BNb6ALLH4aTzC7IkESIAESIAESIAELEDg1Gpg5UNA0SV5Y81aApM+BaL6GZJ/bErYZDAEns4+La/7lyTUMxQzu87E5HaT4a51N1mvLgU5y5Yj5fnnRdSycZip3/jxCHv1FThpLR+CXBf9alv3ZEoevheGwNrsFdjc192wT+BdfSMR5udR2y7qVc9Sa2ApDPjg+njskUKCjadKeGsCN4xvjV6jWjIkuF6zxJtIwDwBNX5/mx8RS+tDgAbA+lDjPQ0moMYXkKUWPw2GxwZIgARIgARIgARIoDEJFGQAvwkj4Ok18l6dhBfagIeBIc8CWldh2KnE1sSt+OToJ4jJjJHX/0sS5BGEed3n4fb2t1skNDh31WpcfOopQK836tPnlpsRLsKEnVwsG35s1ImFL+riFShtnzesY6jBK3BQ+2CReEUILHxYeg188Ww21omQ4KJceTbgqC4BIiS4Mzy8GRJs4Wlkcw5OQI3f3w4+ZVYZPg2AVsHKRq9FQI0vIEsvfq7FiOUkQAIkQAIkQAIkYDMEJJctKRxYCgsuK5Cr1bwbMFF4A4Z2NpRJhsAdF3fg4yMf40jGEXn9vyQtfVvi4V4PY0TUiAbv2Ze3YQOSH3sc0OmM+vMeNgzh774DZ1f1GZXq4hUY3swDdwuPQCmLcIjwELTUYY01cFGeCAleHIPEk9kyNb2auWG0CAkOa9tMVkYBCZBA/Qio8fu7fiPlXeYI0ABojg7LrEZAjS8gayx+rAaYDZMACZAACZAACZCANQhkxYoEIQuAxN3y1jVuwPAXgX73A3/tvScZAvek7jEYAg+kHZDf85ekR3APPN7ncVwXcp3JOrUpKNi2DUkPPoTKMmPvMq+BAxHx/n/h7GHdcNna6FifOkVl5Vh1JAXfiBDhI4nm9wrUCi/AkZ1DMVXsFTigTZCYioZ5BVprDVwhQoIPrI3DvlWx8pBgoXM/ERJ83cgohgTX54HhPSRQg4Aav79rDIGXFiBAA6AFILKJuhNQ4wvIWoufutPjHSRAAiRAAiRAAiTQhAQqRJjtjveALa8BFcbedgatom8CJnwENIs0UnJ/6n5DaPDuFAXj4V81h0cNN3gEtvJrZXRvXS4Kd+1C4v0PoLK42Og2z759EfnR/+Ds5WUkV9vF8eRcSElDfj2UjMIy45DnmmNpGegpvAKjcEfvCAR6CwNtPQ5rr4GTTmdj/RcxKBZegTWPlt0CMWJ6Z7h7qyeEu+YYeE0CtkBAjd/ftsDN3nSgAdDeZlQl41HjC8jaix+VTB3VJAESIAESIAESIIHLBFKOAsvnARkn5UTcfIFb3wS6T4Ehw0O1GvtS9+E/+/+D45nHq0mvnmqcNIa9ARf0WABpr8D6HEUHDiBx3nxUFBYa3e7RsyciP/sUGh8fI7kaLwpKy7Hy8EV8syceMRfzzA7BReOEm7uGYaowBvZrHVCncOvGWAMX5pZiw6ITSBbGwJqHt78bhk/vhIiOATWLeE0CJFBLAmr8/q7l0FitDgRoAKwDLFa1HAE1voAaY/FjOcJsiQRIgARIgARIgAQagYCuBNi8ENj1gXJnnccDY98FPI2NN1Jo8Lr4dXjvwHtIKkhSvNdT64kZXWdgeufp8HTxVKxjTlh89CgS5sxFRZ6xccy9SxdEfv4ZtP7+5m5XTZnE8miS8Arck4CVRy6iWGfeK7B1sJfBEHi78Aps5nntfREbaw0shQTvXx2LfWviREZnOf4ewyLRb0JrkWtGIy+khARIwCwBNX5/mx0QC+tFgAbAemHjTQ0loMYXUGMtfhrKlveTAAmQAAmQAAmQQKMTiN0u9ga8D8hTMOZ5NwfGfwi0GyFTS6fX4cczPxr2CMwpVd7bTvICvL/n/ZjYdiK0zlpZG+YEJSdPImHWbOizjT3L3Nq3R9SiL6ANqp+Hobk+m7Isr0SHX0Ro8De7E3A6Ld+sKq5aZ4ztJrwCxV6BvVv6m/QKbOw1cOLJLOENKEKC8+Xh5f5hXhgpsgQHR6nfg9Ps5LCQBCxMQI3f3xZGwOYEARoA+Rg0CQE1voAae/HTJBPDTkmABEiABEiABEigvgSKhQFv7VPA0e+VW+gzGxglvAVd5Xvw5ZflY9HxRVh6YilK9aWK90v7Aj7a61EMiRxi0lildGPpuXOInzkT+oxLRsWurVohasliuISGGsnt4ULyCjyYkC3CgxOw6mgKysorzA6rQ6iPwRA4sVc4fN2N99trijWwuZBgKanJ9WNbodfoKDhrnM2Oi4UkQAKXCajx+5tzZ3kCNABanilbrAUBNb6AmmLxUwuUrEICJEACJEACJEACtkUg5hdg1SNAsbHXnUHJgDbApE+BiD6KOqcWpuKDQx9g5fmVIgpUIQ5U3NUrpJchY3D34O6KbSgJy+LihBFwFspTUoyKXSIj0VIyAoaHG8nt6SKnqAzLDiaLEOF4nM8w3hOx5jg9XDS4rYfkFdgSPSL8DIbWploDV4qQ4KNbkrBrxXnoFQyYoa18MUJ4AzYLqXt4eM1x85oE7J2AGr+/7X1OmmJ8NAA2BXX2CTW+gJpq8cPHhQRIgARIgARIgARURyA/Ffj1AeDcRrnqIskHBj0h/j0JaIy9zaoqn846jXcOvoMdyTuqRLLfUS1H4ZFejyDS1zjbsKziX4KypGQkCE9AXWKiURVtWJjBCOjasqWR3N4uJK/APbFZhr0C1x5PgU6vbGCtGneXFr4Gr8DRHQKwY9tmg3jUqFHw8PCoqtIov1kXC7FxyQlkJMhDmrWuzhhwezt0ualFnbxCG0VxdkICNkRAjd/fNoTPblShAdBuplJdA1HjC4gGQHU9Y9SWBEiABEiABEigiQkIgxP2fwGsex4oL5Yr03YkcLcIF9aY3tdv18VdeOfAOziZdVJ+v5BIewJO6TAF87vPh7/7tZN66NLSkDBjJspiY43a0wYHI2rxIri1bWskt9eLzIJS/HwgCd/tTUBcZpHZYXqKpBs9/XUYEFqBWRMb3wAoKSd5AO4XyUEOrI2D9FjVPKK6BGLYtI7w8nOrWcRrEiABQUCN39+cOMsToAHQ8kzZYi0IqPEFRANgLSaWVUiABEiABEiABEigJoFL50SCkHlA8oGaJcAAESo88mW5vJqkorICa2LX4P2D7+Ni4cVqJVdPvV28MbvbbPyt09/goTXvoVZ+6ZIhMUjpmTNXGxBnGpEVWDICunfsaCS35wsp8+7O85n4dm881sekoVxcmzt6RPhi5oDWuKVbc7hphSdnIx+pF3KxcfEJ5GbIDcpuXloMmdoRbXuHNLJW7I4EbJ+AGr+/bZ+q+jSkAVB9c2YXGqvxBUQDoF08ehwECZAACZAACZBAUxDQlwN/vA1sewOo1BtrcOdSoPM4Y5nClZQc5PtT3+OTo59AShqidIR4huDvPf+OcW3GQeNs2kBVLrICJ86eg5ITJ4yacfbzQ9Tnn8GjWzcjuSNcpOeX4Kf9l70Ck7LlBrbqDIK8XXF33yhDiHCYn3mDa/X7LHGuK9Vjx7JziNmerNhc+76hGHRXe7h5KoeXK95EIQnYOQE1fn/b+ZQ0yfBoAGwS7OxUjS8gGgD53JIACZAACZAACZBAAwnE/Ql8NQGo0F1tyNUHmLcFCGp3VWbmLLc0F58f+xzfnPwGuurtVLunbbO2eKz3YxgYPtDk3nD6vDwkzpuP4sOHq90JOHt5IfLTT+DZu7eR3FEu9MIL8I+zGYYMwptOpsGcU6BGZOQd3SUU0/pH44ZWASZZW4Nd/PFMbF56EkW5ZbLmvf3dMGx6J0R2DJCVUUACjkhAjd/fjjhP1h4zDYDWJsz2FQmo8QVEA6DiVFJIAiRAAiRAAiRAAnUjsPczYI1IAlL9CBZht3M2AW7e1aVmz5MLkg0Zg1ddWGWy3g3Nb8BjfR5D58DOinX0BYVIuu8+FO3bZ1TuJBJdRH70P3j162ckd7SLlNxifLMzFl/vuoCcMiezw+8Q6oNpN7bEhJ7h8HIzva+j2UbqWFhSoMPWb0/j/MF0xTu7D41A/4ltoBX7GPIgAUcmoMbvb0eeL2uNnQZAa5Flu2YJqPEFRAOg2SllIQmQAAmQAAmQAAnUjoCUxWHFAuCoSABS/egyCbh9EYQbWXXpNc9PZJ7Afw78B3tS9pisO6b1GDx43YMI9w6X1akoLkbS3x9E4Y4dRmVOrq6I+PADeN90k5Hc0S6kNfDadetxPMsJMWWB2BOXYxaBj7sWd/SOxL39W6JVkJfZupYolLIbn9mbhu3fn0FZsQg1r3H4N/fEiJmdEdLSt0YJL0nAcQio8fvbcWan8Ubq3HhdsScSIAESIAESIAESIAESIAGHJyAZ+Ma+A4R2NUYRsxzY/ZGxrBZXknffZyM/w8cjPkY7f+Uw4tUXVuO2FbfhrX1vQQohrn44C2+/COHt5z1sWHUxKsvKkPTA31G4e7eR3BEvNGLKegRWYsn0Xlj/6CDc0y8KUnZgpSO/pByLdsRi6FtbMX3RXmw+JcKIzcURKzVSB5mTeJ463NAcd73QFxEd5Zmgs1OLsOyNA9i3OhYV+oo6tMyqJEACJGBfBGgAtK/55GhIgARIgARIgARIgARIwPYJuHoCU0TyDzc/Y13XPw/E7zSW1eJKMgINCB+An8b+hIUDFkJKBlLzkPYL/PLEl7hl+S1YcnwJpKQiVYez5O333rvwufnmKpHhVzICJt7/AIoOHjKSO/JFexHq+8qEbtj97HC8dFtns15+285kYNaS/RgijIGfbRdhxEXy/fosxdInwB3jHuqJgXe2g8bF+DNXMkDu/S0Wy986iJy0Ikt1yXZIgARIQFUEjN+MqlKdylqSwNdff4358+ejT58+cHNzM2zgu2TJEkt2wbZIgARIgARIgARIgARI4CqBgNbApE+vXktnUobgn2YA+anG8lpeSZl/J7SdgNUTV+PhXg/D20W+p6CUQfjtA28bPAJ/O/+bSHJx2SvMycUF4W+9Cd9xtxn1VllUhESxTi6OiTGSO/qFr7sLZg5ohU2PDcZXs/piRKcQk9HbCVlFeHXNSfR7fROeXnYUJy7mWQWfk0hK0mNYJO589noER4nkMjWOtNg8/PDKXhzbmgQpdJgHCZAACTgSARoAHWm2zYz1+eefx6effor4+HiEhYWZqckiEiABEiABEiABEiABErAQgQ7C427QP4wbK0i7bATUV8sUbFzjmlfuWnfM6TYHayatwT2d7oHWWZ6UIqUwBc/++SzuWnUXdl3cZWjTSatFi9dek3kCVuTnI3H2HJSePXvNvh2tgrMwug1qH4zPp1+P7U8OxfxBreHn4aKIoURXge/3JeLW//6BOz7eid+OXITOCmG5AWFemPxUb/QZEw3JKFj9KBc6SPsFrnr/CApzrnqBVq/DcxIgARKwRwI0ANrjrNZjTJ9//jni4uKQkZGBBQsW1KMF3kICJEACJEACJEACJEAC9SAw5GmgzTDjGxOEQW7Di8ayelz5u/vjqb5PYeX4lbg5WhgbFY6TWScxb8M8LNiwAKezTkMyAob/+w14Dx5sVFufk4OEWbNRJv5gzkOZQGSAJ565tRN2PzMc/57cHZ3DTCfe2BeXjQe/O4QB/9qMdzeeQXpeiXKj9ZRqNM644bbWmPRkL/iF/D979wEfVbG2AfxJr5CEJEBIKKFD6L2XgCBdlKIiV1DscLkiFtDrtYEF0Xv9VBBFxUoVEESko/QuSA0lhZAQEkJ62ezmm7OYZGd3E9JzzuY5/uKemdNm/rPG3TdT3CzuEnnmJn584yDCjoiAMzcKUIAC1UCAAcBq0MjFqeLgwYPRsGHD4pzKcyhAAQpQgAIUoAAFKFB+AmLYLu5bCng1kO954FPg1Go5r5Sp+jXrY0H/Bfhh+A/oXKez1bvsvbYX4zeMxyt7XsF13U0EijkB3Xv0kM7NEX8sj5g6Fbpr16R8JmQBN7FAyISu9fHLP/tg9ZM9Map9PdELU+6Jl3dFXEqWCACGoZcIBCoBwSPhN8t1eG7dYC9MfLkb2vS3XAE6Kz0HW744jS1LTyMzrfQ9TvPqwlcKUIACahZgALCMrRMXF4eNGzfi1VdfxbBhw+Dn52ecP0+ZiHjKlCklursy/Pa5555Dy5Yt4eHhgVq1aqFr165YsGAB0sXcI9woQAEKUIACFKAABShgkwLutYAJywAHF7l6P88A4s7KeWVItfVvi6+GfoWPQz9GY6/GFnfKRS7WX1qPkWtH4qPTi+Hz33fh1rGjdF7OtZjbQUDxPYBb0QLKd6IujWrh/x7oiH0vheLZwc1Ru4ZZG/99ixyxUIcyJHjc4v0Y8dEerDgciUydmBOyHDYnFwf0f6AFRs1oD3cvZ4s7hh2+juVvHkLU2ZsWx5hBAQpQwFYEGAAsY0vWqVMHo0aNwptvvonNmzcjISGhVHfcsGED2rVrhw8++ADnz583BvwSExNx5MgRvPDCC+goPnhcvHixVPfmRRSgAAUoQAEKUIACFFC9QGAnYMT7cjF14o/gKx4CMpPk/DKklKBU//r9sWb0GrzW8zX4u/lb3E1ZIXjpX0sxdssDCHt5Alxat5bO0UVEijkBH0WO+LzOrXgCtWu6YubgZtgrAoFKQLBrI59CLzwTk4wX15xC9/nb8ebGMzgfm1LouSU50CDEFw+82h1Nu9S2uEyZD/Dn/53AvjUXoc+5vTCMxUnMoAAFKKBhAQYAy7HxGjRogCFDhpT4jsePH8fEiRORnJwMT09PzJs3D/v27cP27dvx2GOPGe934cIFjBgxAiliAmJuFKAABShAAQpQgAIUsEmBTv8AlB/TLUH8EXzd02KF4PJdtVVZGOS+5vdh49iNmN5hOtwd3U2fatyPy4jDrKP/xv/9wxv2wfJ0OVlhFxE17THo+fncwq2oDCcxN58yJHjVk72MQ4TvF0OFXZ2sfy1NytBh6Z4rGPrf3zHm4z349kAElLyybK4eThg6rQ3uerQ1XNwtF4c5vjUSa947isTYtLI8htdSgAIUUJ2A5W881RVR3QVShv4qw3SVH6U3oLKQRnBwcIkKPXPmTGRkZMBRTDi8ZcsW9OzZM//60NBQNGvWzNgLUAkCLly4EK+99lr+8bwdZehwVlbxV7FSnqnclxsFKEABClCAAhSgAAVUJTBsARBzUvycKCjWuY3A3v8CfZ4tyCunPXcndzzR/gmMaz4Oi/9cjNUXViMnN0e6+7akQzg52gkLlnvD7fqt/GOZp08j6vEn0GDpF7B3twwg5p/IHasCIfW88I5YLOSlYS2x6shVfHMgHFE3M6ye++fVJCg/b4kegUND6mJ8lyD0buIHZRXi0mzNu9ZFvabe2PHNWTH0V+7JeSMyBSvnH0bfic3RqleAcYqn0jyD11CAAhRQk4BdrtjUVCCtl8U0APjwww/j66+/LrJKhw4dQvfu3Y3nPPHEE1i8eLHF+QaDAW3atMHZs2fh7e0NZd5BJycn6Tyl52BaWvH/SrVz504MGDBAukde4p133sGcOXPw1VdflXgew7x73On16tWrqF+/vvG0qKgoBAUF3emSKj+uBGmVAK2yKT093dwsVxOr8kKyABSgAAUoQAEKUMAWBG5FAp/1AzJMAjN2opfY5LVA4wEVWsPwpHC8c+gdKIuCmG9+SbmY/70dvJPkAKF7zx6oLz7H27tYn9/O/D5aSlfmZ2C9mAdw94U4LNsXIV5v3JEp0NsN93UKxLjO9dHAt3QB2FzxzJM7r2Lf2osw5Fh+NW7SyR8DJrWE0nOQGwW0KqDF799atVZzua33tVZziW2sbOvWrcuv0VSxopi1zd7eHv/4x+2hELdu3YISvDPfUlNTjatlKfHc4vwUFvwzvy/TFKAABShAAQpQgAIUqHQB7wa3VwaGSe+uXAOw+hEg6WqFFqeRVyMsGrzIuGqw+fyA8V52ePX+XCR6yEVI338A0TP/hdzsbPkAUyUScBC9+UJb1sGyR7ph5+wBeGpAk0IXDVFuHH0rAx/tuIh+C3bi/iX78dOxq8jILtnCIXbime0H1cf4l7rAp65lEPHSsRtY8dYhRF8wCUaXqFY8mQIUoIA6BBgArOJ22LNnj7EEyqq/nTt3LrQ0/fv3zz+2d6/lXyPzD3KHAhSgAAUoQAEKUIACtiDQdBAQ+rJck3Sx4N7Kh4Gc4k99I9+geClloZC7G92N9fesxwMtHxBhyIJAZGwtO7z1gAOSzQaDpO7ahegXXkSuvmQBqOKVqPqdFezngRfvbmlcPfirKV0xvG1dODkUtIO5yIHLNzFr5Z/oOm8b5vx0EkcjEo0dI8zPKyztF1QD4+d2RUi/QItTUhOzsO7D4ziw/hL0ehGI5kYBClBAgwIMAFZxoynDepWtadOmxjkACytOy5Yt8w/lXZOfwR0KUIACFKAABShAAQrYokCf54Dmw+SaRR8BNs+R8yooVcO5BuZ2n4sfR/yIVrVa5T8lyt8O8yY6IN1sxG/K5s2IeeXfyBVT+HArHwFHsWjIwJa18emkzjg4dzD+M6o1WgXULPTmqVk5+PFQFO5btA+DP9iNxbsvIS45s9DzTQ84OTtgwIMtMOzJtnDxMJsuX4wOPvprBNa+fwxJN8Tq1NwoQAEKaEzA7Leaxkqv8eJmZmYiPj7eWIs7zYHn4+MDpZegMs+fMmdeeW9ffPEF8nojnjp1ynh7JW+X+EumsvXp0wfTpk0z7hfnX8ocA0VtMTEx+YeVeUWUH7VvSnvlbab7eXl8pQAFKEABClCAAhSoAIFh/4VL3FnY3wovuPmRpciu3Q76thML8ipwr7FHY3wZ+iVWX1yNxX8tRlpOGq4E2GH+BAe8slwPV5OFaZPWroXByRG1XnrJJhaPMP3ca7pfgdyF3tpNdF+5v1Nd48+ZmBT8dDwGG0/FIilTnpMx7waXbqThnV/PYcHm8+jbrBbGdgjAgOZ+cBZBxaK2gBaeuGd2O/z+w0XEhCVLp16/kozlYkhwr/sao0kXP5toY6mCTNikgBa+b9skvMoqxQBgFTZISkpK/tOVRTzutOUFAJX5/sp7U4J/y5Ytk26rDDU2HW5ckgBg3gIf0g0LSfz+++/w8/Mr5Kg6s5Uyc6MABShAAQpQgAIUqByBmnWnoW/SG3DMLZhjz+HX2dh7KRnJ7g0rpxDiKT7in2fcn8GmjE34S/cXLgTZ4b1x9nhppQHOJiN/U1auQsT164gfJnoviuHEtrKp7TNwN/FttlN74FSiHQ7G2eHcLTvkmgzXznPXi3nSd11IMP54OOaii38uuvsbEGg2l2Pe+Xmv9k0AL3snJF0QXT1zC9oxJ8tgDA4e3nkWPiGZEKdwo4CqBfI6Hqm6kCxchQsU/aePCn989X6A6V/QnJ2d74jh8veqYhURvVdWKy5q8ZA7rWZ8x8LzBApQgAIUoAAFKEABCpRSINmtAf5sMFW62iFXh25XPoKT6I1XmVtN+5q43+N+TPaYDG97b/zVyB4L77VHjtk3q1q7f4fHtl8rs2jV8lmOwr2jby6ebGXAa530GFFfDz9XMV63kC0txw67Y+zx3klHvH/SAX/E2iHNpAen6WVK7LZGYx1q90yHo7vlsO6MGCdc3+uBrESzxje9CfcpQAEKqESAPQCrsCFcXV3zn55djBXDsrJuT3bs5mY243D+XdSzc6dhysoQ4G7duhkL3K9fP9xpCLQaaqYEbPP+6qmU2bT91FA+loECFKAABShAAQrYtsAQ5GzVwfHYl/nV9Mi+gaFpq5E97lvR065ygzBDMASP5TyGL898iW/tvsVHo3X413oD7E1iT4HbfkdEPQf0mfkeHOwd8sutpR2tfQaeIHCVjg1HI5Ow9kQMNp+OQ7rOpHumCX5Umh2irjhgfaQdBrX0x9BWtdGnaS14ulh+TdaN1uPA2isIO3jD5A6APsMe8Yc80GFIENoPDoJ9EQuVSBcyQYFKFLjTFF2VWBQ+qgoFLH+zVWFhqtuja9SokV/l4gzrVeb/U7biDBfOv3EV7ZQkoKcENLUQ1DSlVIJ/Wiuzafm5TwEKUIACFKAABTQpMPxdIO4v4Oqh/OI7XN4Ot0MfAwNezM+rrB3xKRbPdX8OY5qPwZv+b2KR7gie+UXuKdbwm534NO0ejHj2fwjxC6msolXIc7T0GbhvS3f0bRmAN8WiIL+cisHqI1dxKPymVRedPtcYKFSChcpKw92Ca4mAYB0MEgHBhr63xwkrfTCGTG2Lxu3isOv7c8hKL5h3MFc0+fHNVxEbloLBj7RGTV/1d9iwCsFMmxXgd1ebbdoSVaxy/0xWoqLZ/snK/0B9fX2NFb1TRD4xMdG4AIhycknm17N9RdaQAhSgAAUoQAEKUKDaCDiKaXPGfw14+MtV3vU2ELZVzqvEVFOfpvj67q8x6Mm38P1wy4nlRqyJxqfvTMT8g/ORkl0wD3glFrHaPspD9Oab0KU+Vj7ZEztnD8AzA5ugbs2CkVjmMEowcO/FBLyx8Qz6L9iFQQt34e1NZ3HwcgJy9AY07VwbE1/phoCmXuaXIuZSEla8dRhhR65bHGMGBShAgaoWYACwilugdevWxhJcvHgROTkFf0UyL9a5c+fys1q1apW/zx0KUIACFKAABShAAQpUKwGvQGDcV2LIr+mQWjHuds00IDG8yijsxIRxY5uNxXPzt+LY+LZSOZQvXU//osfldd9jzLox+C38N+MwVekkJipcINjPA88PbYm9L4Xi66ldMaJdwB1XBFZWEv7s98uYuOQAOr+1DTOXH8eOyAQMfLItuo8OFiPPCxYHUSqQnZGDLV+cxvZlZ5BdyOrEFV5RPoACFKCAFQEGAK2gVGZWnz59jI9ThvcePXq00Efv3r07/1jv3r3z97lDAQpQgAIUoAAFKECBaicQ3BcY/Jpc7cxbwIrJgC5Dzq/klI+rDya9uRLZD4+VnqzMDThTzBEYdOo6Zu+ejae2P4WolCjpHCYqR8BBBO0GtKiNTx7shINzB+HNMSHo19z/jsHApAwd1p+4JoKAJ9Bl/jYsiIqF/eA6cPMRqwSbbef2x2LFvMO4fiXZ7AiTFKAABapGgAHAqnHPf+o999yTv//VV+IvmVY2g8GAb775xnjE29sbAwcOtHIWsyhAAQpQgAIUoAAFKFCNBHrNAFqNliscexL4ZbayCoScXwWpdi/Ng/eUf0hPdhRzxT231oCQCAP2Ru/F2PVj8fnJz6HTF7IMrXQ1ExUh4OPhjMk9G+GbR7rh2Kt3YfFDnTG+cxD8PMVw8yI2vSEXB6/cxDuHruAdwy1EeFqenHwjAz8tOIqjm8NhEOdzowAFKFCVAgwAVqW+eLayEm7fvuIvmGJbunQp9u/fb9w3/dfChQtx9uxZY9bMmTPh5ORkepj7FKAABShAAQpQgAIUqH4CYsgt7vkU8Gsu1/3Ed8CxZXJeFaSUIcF1X3wJ3vdPlJ7uLGb9eXGVAc2u5iJLn4WPjn+EcRvG4XDsYek8JipfQFn99+42dbFgfHscmjsY657pjX+GNkXrgJpFFiZbvBVXOmZgo3s2siAH+pTA34F1l/Hz/44jNTGzyPvwIAUoQIGKFLATS6TLv6Eq8mk2eO89e/ZAmb8vb4uPj8fzzz9vTCpDdadNE3ORmGxTpkwxSd3ePX78OJRzMzIyjCv8zp0719jLT0kvX74cS5YsMZ7YvHlzHDlyBKarB1vcTCMZyqIneYuZREVFoSSrBldVFZX22LJli/HxQ4YM4SrAVdUQfC4FKEABClCAAhQwFbhxHvg8VEy+llqQ6yB6bz2yGQjsXJBXRXu5YjRPzJw5SFr/s1SCNDFq9PUHHRBeV0SP/t7GNBmD57o8B2UYsVo2fga+3RIxSRnYfjYOO87FiUVC4pGVI6/2nNdeXno7jEx3Rj29ZV8bR1cH9L63KUL61LOYOzDver5SoCIEtPj9uyIcqvs9GQAs4ztACegtW1b8vzAWFm/dsGEDHnroISQnW58jQgn+/fLLL2jatGkZS6yOy7X4C4gfftTx3mEpKEABClCAAhSggIXA6bXAqilyds0g4InfxYrBvnJ+FaRyxWJ/0bOeQ8rff0zOK0KyG/CfhxwQ7VcQBPRy8cJznZ/DmKZjYG9nGUTKu7ayXvkZ2FI6I1tvDAJuF8HAHeeu43pylnSSMt9jz0xH9MhyhD0K2jbvpIwaDvAfEID+3QPR0NcdSm9RbhSoSAEtfv+uSI/qeu+q/z9KdZU3q/eoUaNw8uRJPPvss1CCfe7u7lDm++vSpQveffddKL0EbSX4Z1Z1JilAAQpQgAIUoAAFKFA2gRCx4EbP6fI9kq+KlYEfAQx6Ob8KUnaOjgh8fwE8+veTnl5TrFfy7x/1qJNYMCgrKSsJr+57FVM3T8XFxIKRRtKFTFSpgJuzAwa3roO3722LA3MGYeOMPnh2cHO0D/Iylssg4nl73XKw3DMbyXaWPQXdUvRI2RCFt97Yi4HzdmD2qj/x07GriE3iEOEqbVg+nAI2LsAegDbewGqtnhb/AsG/fqr13cRyUYACFKAABShAASGgF5PrfTMGiNgjc/R9Dhj0qpxXRSlDZiainngS6QcPSiWI97LDvyfZI0G8mm6Odo54OORhPNH+Cbg5iu6CVbDxM3DJ0OOSM7HzfJxxuPAfYfEwZOkxOMMJrXWOVm+UbpeL3a46/OUsAtWi+Rv7e6B3Ez/0auKLnuLH273oxUis3pSZFDAT0OL3b7MqMFkOAgwAlgMib1FyAS3+AuKHn5K3M6+gAAUoQAEKUIAClSqQGgd8JnrZpcTIj73/B6DlCDmvilKGtDREPjoNGSdOSCVIqu2B2RMzkeQpBwGVkwI9AzG3+1z0C5J7EEo3qKAEPwOXHjZTp8eBywnGeQPPHo9DuzgDahmsD8K75mDAVrdsxDkW9AZVRgYrC5D0bupnDAZ2a1QLHmKhEm4UKKmAFr9/l7SOPP/OAgwA3tmIZ1SAgBZ/AfHDTwW8EXhLClCAAhSgAAUoUN4CkaJ33dfDxdBf0SMwb3MRq7g+vgvwbZKXU6WvejHvd4SYSzzrzFmpHDnBgXjtQXtcMJgFMP8+a1CDQXi+6/PGgKB0YQUm+Bm4/HDD41Kx6+dLSDmWAHvLkcEwiBWET4iegHtEj8AsK3FCR3s7dKjvbewd2EsEBTs28IaLo0P5FZB3slkBLX7/ttnGqMKKMQBYhfjV6dEhISFSdXU6HcLCwox5XAVYomGCAhSgAAUoQAEKUKCsAgeXAL8+L9+ldmtg2jbA2UPOr6JUTmIiIiZPRvbFS1IJnFu3wm+zeuLzKz8gxzSI+fdZLg4umNZ2Gqa2mQplv6I3BgDLXzjlZib2rAzD5RM3rN48TRkW7KbDaafbw4KtniQyXZ3s0VX0ClSGCvcRAcGQel5wEEFCbhQwF2AA0Fykeqat/F2hekKw1hSgAAUoQAEKUIACFKCAjQh0ewxoO0GuTNwZYMNMILdgiKV8QuWmHH180GDpl3Bq0EB6cLboFTj80xNYPfh7dKnTRTqmJLL0WfjkxCe4Z9092B212+I4M9QvUKOWK4Y92RYjZ7SHl7/l3I4euXYYnu6MB1Kd4a8vPKCXqTNAmWfwvc3nMfrjvej05lY8+e1RfHsgAlfi08RbXR3vdfW3CEtIgeohwB6A1aOdVVdLLf4Fgn/9VN3biAWiAAUoQAEKUIAChQtkpwFfDAaUwJ/pNmwB0P1x05wq3ddFRyP8ocnIiZGH/Xr06onATz/Fhqub8cGRD5CYlWi1nP2D+uPFri+ifs36Vo+XNZOfgcsqWPT1ehHEO741Ekd/DUeO2DfflBBetJ8D1uekIV0MES7JFujtJuYP9DXOIdhLLCziX6Pie4yWpHw8t/IEtPj9u/J0qs+TGACsPm2tqppq8RcQP/yo6i3EwlCAAhSgAAUoQIE7CySI4bVLBohuc8kF59qLRRSmbAIadC/Iq+K97PBwYxBQHx8vlcRz4EAEffQ/JBvSjb3+VpxfAUOuZZDI2d7ZOCT40baPlvtqwfwMLDVJhSWS4zOwZ1UYrvwpvwfyHuhawwm+vWrjjJgjcJ9YWOT0teQSd2ZtWbeGMRioDBfuFswFRfJsq8OrFr9/V4d2qew6MgBY2eJ8nlFAi7+A+OGHb14KUIACFKAABSigQYFzvwDLH5QLXiNALAoihs/WqCPnV2Eq88IFRE7+B/RJSVIpag4fhnoLFsDOwQHnbp7D/IPzcTzuuHROXqKeRz280PUFhDYIhZ2yhGw5bPwMXA6IJbhF+Kl4/LHiApLjM61eFdDUC/0faAF7b2ccvJKAvRfFz6V4XL4heryWYFMWFFEWEVFWGFYCgu3F4iJODpwhrASEmjpVi9+/NQWskcIyAKiRhrK1YmrxFxA//Njau5D1oQAFKEABClCg2ghsfwP4Y6Fc3Ya9gX+sBxyc5PwqTGX8dRqRYnVgQ2qqVAqvsWMRMO8t2NnbG+d123h5IxYeWYiEzATpvLxEr3q9MKfbHDTyapSXVepXfgYuNV2pL8zR6XF8ixgWvDkCyhBh881OBO/aDQxCt5HBcHYTPVrFFpOUcTsYeDEee8TPjZQs88uKTHs4O6B749vDhZWAYPM6nuUWRC7ywTxYKQJa/P5dKTDV7CEMAFazBldLdbX4C4gfftTy7mE5KEABClCAAhSgQAkFDGI11e/uBS7vki/sOR0YOk/Oq+JU+rFjiHx0GnIzMqSS+EyahDqvvJwflEnJTsGiPxfhh7M/QJ8r6me2OYqhzg+3fhiPt3sc7k7uZkeLn+Rn4OJblfeZSTfEsOCVFxB+ynqg172mM3qPa4pmXevkvy+UMiiLf4TFpWKPWCBkn+gdeODyTaRm5ZSoeH6eLri7TR1M6RWMprU9S3QtT1afgBa/f6tPUfslYgBQ+22oyRpo8RcQP/xo8q3GQlOAAhSgAAUoQIHbAmkiiLKkP5AUJYuM/xoIGSvnVXEqbf9+RD3xJHKzs6WS+D7xBGo/+y8pLywxzDgs+Mj1I1J+XqKOex3M7jobQxsOlYJEecfv9MrPwHcSqvjjV07eHhackmB9WHC9Zt7o90Bz+NazHqjT6Q04efWWCAjeHi58PDIROn3xFxTp39wfj/YJRt9mfqV6D1W8EJ9wJwEtfv++U514vOQCDACW3IxXlIOAFn8B8cNPOTQ8b0EBClCAAhSgAAWqUiD6KPDl3YDeJLDm5CHmA9wJ+LeoypJZPDtlx05c/ec/gRy551bA/PnwvlcOWCo9vjaHb8b7h99HXEacxb2UjO51u2NO9zlo4t3E6vHCMvkZuDCZys3Pydbj6G8ROP5bJPQ5lsOC7R3s0HVEMDoNbQD7O8zllyZ6Ax4Kv4l9xuHCCTgbY7JIThHVaiZ6Ak7tHYyxHQPhJoYMc9OOgBa/f2tHVzslZQBQO21lUyXV4i8gfvixqbcgK0MBClCAAhSgQHUVOPo1sGGmXHvfZsBjOwDXmnJ+FaeSf/0V0c/NBgwFAR87Jyc0WPY13Dt1sihdui4di08uxrenv0VOrhw4VE52tHPEg60exFPtn4Kns/XeYuY35Wdgc5GqTd+KSxeLhIQh8rT1YcG1G9ZA6MOtCu0NaK308alZYqiw6B0ohgwr8wdG35KHn5tf4+PuhAe7N8DkHo1Q18vV/DDTKhTQ4vdvFTJqvkgMAGq+CbVZAS3+AuKHH22+11hqClCAAhSgAAUoIAmI3nL4eTpw/DspG61GAxO+gRjjKOdXcSpx5UrEvvofqRQOtWqhkch3DgqU8vMSl5Mu4+2Db+NAzIG8LOnVz80PszrPwsjGI+84pJOfgSU6VSSUHp9X/hTBupVhSLlpOSzY3tHOuEBIx7vu3BvQvELKvSMS0rHqaBS+PxiJW+k681Py08pKwiPaBRiHB7cL8s7P5476BLT4/Vt9itovEdf51n4bsgYUoAAFKEABClCAAhSgQHEFlADf8PeBgPbyFWd/BvZ/LOepIOUzYQJqPfywVBL9zZu4+vTT0KemSfl5icZejbHkriX4cMCHCPAIyMvOf43PiMfcPXMxZfMUnL95Pj+fO9oQsBPv4cYd/PHAa93RcUgDi5i1IScXB9Zdxpr3juLmNevvkcJqqty7kZ8Hnh/aEvtfGoT5Y9sWughIjiEX609cw+iP92Lcon3YdCoGOWK+QW4UoIA6BRgAVGe7sFQUoAAFKEABClCAAhSgQEUJOLmJ3n7fAm4+8hO2ip52V/6Q81SQqv38bHj07SuVJOvCBVx74QXkmgwPNj1BCeQMbjgY6+9Zj8faPgYneyfTw8b9Y3HHMGHjBGNvweTs4s0DZ3ETZlSZgJOYh6/XvU1x7wud4VPXcqXnuIgUrJh/CEc3h8NQisCcMs+fMtR367P98M0j3TCghX+hdT0SkYinvz+G/gt24fPfLyMpo/Ceg4XehAcoQIEKFWAAsEJ5eXMKUIACFKAABShAAQpQQJUCPg2Be78QRTMZ8purB1ZPBZKvqarIdo6OCPxgIZybyAt4pO7YgRsf/rfIsro5uuGfnf6JdWPWoW+gHERULjTkGvDDuR8wau0orA1ba0wXeUMeVJ1A3WAvTHi5K5QhvyLuK235vQEXHCtxb8C8GynB5H5iJeCvp3bDtln9MEkEBV2drIcSlPkD5206i55vb8d/1v+FK/El64GY90y+UoAC5S9g/b/a8n8O70gBClCAAhSgAAUoQAEKUEBdAs0GAwPnymVKuwGsFENuc0xWCpbPqJKUQ40aqL/oUzh4eUnPT/j8cyStXy/lWUs0qNkAnwz6BP8X+n8I9LScO/Bm5k28uu9VTP51Mk4nnLZ2C+apWMDRSfQGvE/0Bny+M7zrWOkNGJ6MlfMP45hYSbg0vQHzqt60dg3ME8OCD8wZhBfvbom6Na0vApIuVi1etj8CoQt34dGvD2OvWFxEL4YMc6MABapOgIuAVJ19tX6yFich5QTI1foty8pTgAIUoAAFKGCrAsoQ2h/vB8J+k2vY7XExV+ACOU8FqbQDBxE5bZoIUBas8mtcGfibZXDv2LFYJczMycRXf32FpX8tRZY+y+IaO9Ercnzz8ZjRcQZccl2wZcsW4zlDhgyBm5sYPs1N1QI5Ivh2cMMVnNgWCViJudUJrolBYqVgn7oeZa6HTgwt/vWvWCzdcwV/Rt0q8n4eYkhxSKAX2omftkHiVSwc0sjX/Y4L0RR5Ux4sloAWv38Xq2I8qUQCDACWiIsnl1YgJCREulSn0yEsLMyYFxUVhaCgIOm4GhMMAKqxVVgmClCAAhSgAAUoUA4CGYnAkgFAYrh8s7FLgPYT5TwVpBKXr0Dsa69JJXHw9UXwqpVwqldPyi8qcTXlKt47/B52Ru20epqXixeebvM03C66wd7OHgwAWmVSbWbs5SRsX3YWt66nW5TRwdEe3UYHo8NgsVKwWM23PLZjkYnGQOBmERAsbm+/Gq6OIhAoAoKB3sZXZT/Q241BwfJoEJN7MABoglGNdxkArMaNX5lVZwCwMrX5LApQgAIUoAAFKECBEgvEnASW3iV61mUWXCrmz8OjovdbQLuCPJXsxb41D4nffSeVxqVVKzT6/jvYu1sOAZVONEv8cfUPvHPoHUSmiB5jVrZAh0CMdBuJR4c/yh6AVnzUnGXsDfjzZZzYHmW1N2DdxjUR+o/y6Q2Y56DMA/jN/nD8eDASyZkFPVXzjt/ptZaHswgIKj0Eb/cSVF7rFDLU+E734vHbAgwA8p2gCDAAyPdBlQho8RcQewBWyVuFD6UABShAAQpQgAKVJ3DiR2Ddk/LzvBsAj+8G3GvJ+VWcyhVDgKOeeBJpe/dKJalx12AE/u9/sLMv2XTv2fpsLDu9DEtOLkGm3iQIanL3QUGDMClkErrU6cIeWiYuWtiNuXgL2785i6S4DIviOogFPbqPboz2g+qXW29A5SHp2TlYcywa3+wLR1hcqsVzS5JRu4ZLfkAwpF5NNK9Tw9hTsLx6L5akLFo8V4vfv7XorPYyMwCo9hay0fJp8RcQA4A2+mZktShAAQpQgAIUoICpwMZZwJGlpjlA4wHApDWAg6OcX8UpfXIywifej+wrV6SS+D75BGr/619SXnETMakxWHBkAbZGbC30kiZeTTC+xXiMbjIaNZxrFHoeD6hLQKfMDbj+Mv7cUVhvQC/j3IDWFhEpa01upGThVPQtnLyahFPi50/xE59qOf9kSZ7jJhY+aVrbE83qeBoDgs3EPgOD1gW1+P3bek2YWxYBBgDLosdrSy2gxV9ADACWurl5IQUoQAEKUIACFNCOgLL677KRQNRBucy9ZwJ3vSHnqSCVHR6OKyIIaEhKkkpTb8ECeI0S9Sjltv/afrx96G1cSZKDi6a3cxNDpIcHD8eEFhPQ2re16SHuq1jgmugNuEPMDZh0w3pvwB5jGqNdaPn2BjTnyM3NRWxypklA8JYIECbhVrrO/NQSp93FYiNKYFD5UQKCzUWAsJlYvViZW7C69hjU4vfvEjc8L7ijAAOAdyTiCRUhoMVfQAwAVsQ7gfekAAUoQAEKUIACKhRIiQU+6w+kilfTbdxXQJt7TXNUsZ+2bx8iHxOrFuv1+eWxc3ZGw+++hVu70s9fqNPr8PXJr7H45GJki3+K2tr6tTUGAu9udDdcHV2LOpXHVCBg7A24TvQG3Gm9N2BAEy/j3IAV0RuwsOorQcGriRnGoODJq7d7C/4lgoIpWSWfR9DaM/ICg0owUAkKdm7oI4YVe8NZLIhi65sWv3/beptURf0YAKwKdT4TWvwFxAAg37gUoAAFKEABClCgGglEHQK+Gg4YTHokOYnFNaZtA+qEqA7i5g8/4Pobb0rlcvD3EysDr4JT3bpSfkkSymfgdZvX4Xj2cZx2Ol3oQiF596zpXBNjmo7BhOYT0MirUV42X1UqcC1M9AZU5gYspDdgt5HKSsGiN6BD1QTJDIZcXElIMw4bNg4fFsOIL1xPRVKGyX+XZbBVhhF3aeSDHo19xU8t42rEthgQ1OL37zI0Ky8tRIABwEJgmF2xAlr8BcQAYMW+J3h3ClCAAhSgAAUooDqBI6LH30azufR8gsWiIDsBNx/VFTf2jTeQ+INYyMRkc23d2tgTsKQrA+fdwvQz8F133YWTSSex8vxK7IjcAX1uQY/DvPNNX3sE9MDEFhMxoP4AONqra/5E03JW932lN+CBdZdwcudVqysF+9X3ROjkVvBvoI75HpWegsqcgkogMCwu5fbrdeU1pVSrDpu2vxwQ9DUuPOJURcFP03KVdV+L37/LWmdebynAAKClCXMqQUCLv4BMP/wMGTIEbm5ulSDFR1CAAhSgAAUoQAEKVKnAzzOAY9/IRWh6F/DgCohuUXJ+FadydTpEPv440vcfkEpSY+hQBH74QYlXBlZuUthn4Lj0OKwJW4PVF1ZD2S9qq+1WG/c1vw/3NbsPdTzqFHUqj1WhwLWwRGwXcwMmx1uuAm1nb4eOd9VH1xHBcBRz7KlxMw0MKsFAJTgYJoKEZQkMKsOGuzSqZewdqPQSbBvoBS0GBLX4/VuN7zGtl4kBQK23oEbLr8VfQIV9+NFoE7DYFKAABShAAQpQgALFEcgRK5UqQ4Gjj8hn950NDPq3nKeClP7WLbEoyEToIiKl0vg98wz8Z0yX8oqTuNNn4BxDDnZf3W3sFbjv2r4ib+lg52DsDagsGqL0DrS3q5phpUUWspof1GX93Rtwl/XegF613TDwoZYIbK6+HrCFNZ0SGIwTPQbzgoGVmsh0AABAAElEQVRKYPB45C2ci00p7JJC8z3yA4J5Q4a94KiBHoJa/P5daCPwQKkFGAAsNR0vLIuAFn8B3enDT1k8eC0FKEABClCAAhSggIoFkq/dXhQkzayn28TvgFajVFfwrMuXEa6sDJwiBziUXoA1hw0rUXlL8hk4MjkSqy6swtqLa5GUJa9KbP7QBjUaGBcNGdNkDLxdvc0PM13FArGXk7Dj23NIjEmzWpLWfeuh19gmcHF3snpcC5mJadk4eOUmDlxOMP6UNiD46UOd0b+5v6qrrMXv36oG1WjhGADUaMNpvdha/AVUkg8/Wm8flp8CFKAABShAAQpQwEwgQvRuWyaCfaLHW/7m7Ak8tgPwb5GfpZad1D17EfXEE/LKwC4uYj7A7+DWtk2xi1maz8CZOZnYErEFK86vwMkbJ4t8lrO9M+4OvtsYDGzn1w52dnZFns+DlSeg1xlw9LcIHP01HAZ9rsWD3b2c0f+BFmjcQd3BL4uCF5JxUwkI/h0MPHD5Js6LYcTF2f54YSDq1xILBKl40+L3bxVzarZoDABqtum0XXAt/gIqzYcfbbcSS08BClCAAhSgAAUoIAkcXAL8+ryUBd+mt4OArl5yvgpSN7/9DtfnzZNK4li7NhqtWgmnOsWbi6+sn4HP3TxnDAT+cvkXZORkSGUxT7Sq1coYCBwePBzuyorL3FQhkHAtFTtFb8DrV5KtlqdJJ3/0ndgcHl4uVo9rNTMhNUvqIagsOmK+BXq7Ye9LoebZqktr8fu36hBtoEAMANpAI2qxClr8BVTWDz9abCeWmQIUoAAFKEABClDAREDMJYZ1TwN//mCSKXabi2G194s8e3XNaafMfRb7n9dwa+VKqbyubdrcXhnY1VXKt5Yor8/Aqdmp2HB5g3GuwIu3Llp7VH6ep5MnRjUZhQnNJ6CpjwiwcqtyAYMhF3/tvor96y4jR8wTaL65uDui131N0apXgM324oxXAoKiZ2DekOGwuFTc1ykICye0N+dQXVqL379Vh2gDBWIA0AYaUYtV0OIvoPL68KPF9mKZKUABClCAAhSgAAX+FtCJXmxf3g3EnJBJBswBBrwk56kgZVwZ+NFpSD90SCpNzeHDUW/h+3cM1pT3Z2AlKHks7pixV+DWiK1QFhEpauvg3wEjGo/AkEZDUMu1VlGn8lglCCQnZGD3D+cRefqm1acFtvARi4S0gJe/7ffgvCEWFsnU6VU//FdpKC1+/7b6BmNmmQTU9SeqMlWFF1OAAhSgAAUoQAEKUIACFKhgASc3QFn8w91XftCut4Hzv8p5KkjZOTkh8H//hVODBlJpkjdtQvyiRVJeZSSUOf461+mM9/q9h23jtmFmp5kI9Aws9NEnbpzAvIPzELoyFE9tewobLm1Ams76whSF3oQHyk2gpq8bRk5vj8FTW8PVw3IBkOjziVj+xiEc3xIp5g00lNtz1Xgj/xoumgj+qdGOZaoaAQYAq8adT6UABShAAQpQgAIUoAAFtCrgXR8Y/zVg5yDX4KfHgfiih7fKF1ROytHHB/UXfQp7T7FoickW/9H/Ifm3LSY5lbvr6+aLaW2n4Zexv+CTQZ+gf1B/2Il/rG36XD32RO/B3D1zMWDFALyw+wXsitoFnV5n7XTmVaCAEsRt0b0uHnytO5p1rWPxpByxeMi+ny5izXtHEX+1eAtpWNyEGRSgQLkLMABY7qS8IQUoQAEKUIACFKAABShg8wLB/YAhb8rVzBKLJCx/EMhSX9DDpUkTBH6w0GKewmsvvojMM2fkelRyysHeAf2C+uHjQR9j832b8Vjbx4oc7pupz8Sv4b9ixo4ZGLByAF7f/zoOxx6GIde2e5xVcrPc8XFuNZwx5NEQjHimHTx9LBcAiYtIwar5R3Bg3SXkiKGy3ChAgaoVYACwav35dApQgAIUoAAFKEABClBAqwI9xIIgbcfLpY8/D6x9EjCoLxjl2a8far8gr2Kcm5mJqKefgS4uTq5HFaXqedbDPzv90zg8+OPQjzEseBhcHQpfrCQ5OxmrL6zGI789giGrh2DhkYVQVh5W5hrkVjkCjdr64YH/dEfbAUGiV6z8TGXxkKObI7DircO4dvGWfJApClCgUgW4CEilclffh4WEhEiV1+l0CAsLM+ZFRUUhKEj8z0LlW3lPgKzy6rJ4FKAABShAAQpQgALFEchOB5YOAa6fks8O/TfQb7acp4KUEhiL+fe/kbR6jVQa1/bt0PCbb2DvIvfkUsNn4HRdOnZE7cCmy5uw79o+KMOB77Q19mqM4cHDjT/1a4oh29wqRSDmUhJ2fnsWibHivwuzTYwcRo97mqDjXQ1gZ28WKTQ7l8nyFeAiIOXrqdW7sQegVluO5aYABShAAQpQgAIUoAAFql7AWax2er9YFMTNRy7LjreAsG1yngpSyvxtAa++CvcuXaTSZP55EjEvv6LKnnPuTu4Y2XgkPh38KXZM2IFXur+CTrU7SeU3T1xOuoyPT3yM4WuHY9Ivk/D92e8RnxFvfhrT5SwQ0MQLE1/uhi7DG8HeLMindMrcv/YSNi06icw0zt1YzvS8HQXuKMAegHck4gkVIaDFv0Co4a+fFdEWvCcFKEABClCAAhSgQDkIXNoBfHcfYDoPnasX8PguoFbjcnhA+d4iJzER4ePGQxcdLd3Y/9ln4feEWMzk703Nn4GjU6Px65VfsenKJoQl3h5dlFdua6/2dvboEdDD2CtwUINB8HSWF0Wxdg3zSi+QEJ2KHd+eQ1y4mBvTbKvh64q7H2+D2g1rmh1hsiIEtPj9uyIcqvs92QOwur8DWH8KUIACFKAABShAAQpQoOwCTUKBQf+R75OZJBYFmSQWBUmV81WQUlYGDlJWBnYXPRhNthsffoiUberruWhSxPzdQM9A4yrCP43+CWtGrzHu1/Ool3/cfEdZJEQZQvzK3leMi4fM2jUL2yO2I1ufbX4q0+Ug4Bvoifte6Ixuo4It5gZMScjEmgVH8dfuq6rsdVoO1ectKKA6AfYAVF2TVI8CafEvEGr+62f1eNewlhSgAAUoQAEKUEDlAsoYx1VTgDPr5IKGjAXGfSWCIOqb9yxl505cFYuAiChMfpntRFCw0Q/fw7VlS2jtM7Ayx+GfN/7ExssbsSV8CxKzEvPrVdhODacaGNhgIIJqBMHX1Re+buLH5FUZgsytbAJRZ29i65enkZFiOfS3Wdc6GDCpBZxdHcv2EF5dqIAWv38XWhkeKLUAA4ClpuOFZRHQ4i8grX34KUv78FoKUIACFKAABShAgVIKKL39lt4FxJ2Rb3DXG0DvmXKeSlIJS79E3IIFUmkcAwIQvGoldB4e2LJli/HYkCFD4ObmJp2n5oTOoMOBaweMQ4S3R25HRk5GqYrr5ugmBQSNAUKTIKGfm1/+cXdHdxHnVV+gt1QVL+eLUhOzsOWLv6AsFGK++dR1F0OC26JWPQ/zQ0yXg4AWv3+XQ7V5CzMBhtjNQJikAAUoQAEKUIACFKAABShQagEXMa/cRLEoyOcDAWUIcN627TWgbjugichX2VbrkanIungRSWvX5pcsJyYGV6fPgP/iRfl5WttxsndC36C+xh8l+Lc7ajd+ufIL9kTvQY4hp9jVUa69mnrV+HOni1wdXPN7ENZyq5UfGFR6FCo9CZ3tneHs4AylbE4OTvlpJU85puQpx/LSeefaQlDR08cFY2Z1xAGxEMiJbVESpbJq8Kp3DmPgQy3RvFtd6RgTFKBA+QiwB2D5OPIuJRTQ4l8g2AOwhI3M0ylAAQpQgAIUoEB1Frgges39MEEIFAytNa4U/PguwKeRyFfXZsjORuSUqcg4dkwqmMfIkTjep7dx+LLWegBKFTFJ3Mq8ha2RW7Hp8iYcuX7E5Ih6d40BQ5PAoDF4+HfQ0NXRFXU96iLIM8g4jFkZyqzsK3mO9urs83P5+A1sX3YG2Zl6C/Q2/QLRZ3wzODhxyQILnFJmaPH7dymrysuKEGAAsAgcHqo4AS3+AmIAsOLeD7wzBShAAQpQgAIUsEmB38Ww2h1vyVWr2xZ4RAQHndU3r1xOQgLCx0+A7to1qcw3hg1D4oD+sJUAoGnlYtNisTViKy7duoSEjAQkZIqfv1+z9Fmmp2pu39HOEQGeARaBQWOAUAQJazpX7Qq8STfSsXnJX4iPslwkp3bDGhj6WBvU9NPOkHM1v0G0+P1bzZ5aLRsDgFptOY2XW4u/gBgA1PibjsWnAAUoQAEKUIAClS1gMAArJwPnNspPbit6Bt67RJWLgmSeP4/wBx5Ebnp6fplzxZx21/4xGb3/9S9NzQGYX4FS7CiLiaTp0qSAYF5g0PhqFiws7fyCpShauV2iBADzegvmBQXzehEqvQeVXocVveVk6/HHyjCc2SMHnZXnurg7YvCU1mjUzq+ii2Hz99fi92+bb5QqqCADgFWAzkcCWvwFxAAg37kUoAAFKEABClCAAiUWyEwGvhgExF+QLx36NtDzaTlPJamUHTtw9Znp0srABmdnBH33LbzaiXkMuVkIpOvS83sOSoHCv3sU3sy8CaVHYbYhGzq9Dtn6bOO+8qosVqK86nMth8NaPKiSMpTeg70Ce+GZDs+gtW/rCn/quQMx2P39eeToRNDcbOs0tCG6jw6GvQOHBJvRFDupxe/fxa4cTyy2AAOAxabiieUpoMVfQAwAluc7gPeiAAUoQAEKUIAC1UggPgxYIhb/yE4pqLSdA/CP9UBw34I8Fe3Ff/45biz8QCqRg1gZuPFPa+Do4yPlM1E+AnqD/nYwUAQJjYFBJVD4975p4DAvYJh3LEW8r66lXru9UEnKVUSlRCE9p6AHZ1lLNzx4OGZ0nGHsLVjWexV1fUJ0qnFI8K3rlmWv18wbQ6aFwMPLpahb8FghAlr8/l1IVZhdBgEGAMuAx0tLL6DFX0AMAJa+vXklBShAAQpQgAIUqPYC5zYByx+QGdzF0MbHdwHe9eV8FaSUIbAxL72EpPU/S6XxDA1F0CcfwxZWpZUqZkMJpe1uZd3CVREMNK5ebPoq9mPTY2HItexpVxSBMhx4YouJeKLdE/B29S7q1DIdy87Iwc7vzuHi0TiL+7jVdMbQR0MQ2IIBaAucO2Ro8fv3HarEw6UQYACwFGi8pOwCWvwFxABg2dudd6AABShAAQpQgALVWmCnGPa7+x2ZIKCDWBRkM+CkvsUODFlZCH9oMrJOnZLKXOfll1Fr8kNSHhPaEVCGIMekxVgNECq9B1N1loty5NXO08kTj7Z9FJNaTYKbY8W8Z5UA5qldV7F39UUY9CaraItCiOko0X1MY3Qa0hB29iLBrVgCWvz+XayK8aQSCTAAWCIunlxeAlr8BcQAYHm1Pu9DAQpQgAIUoAAFqqmAsiiI0gvwggj4mW4dJgFjPlHloiApkZEIH3svHNPS8kts5+SERiuWw7V1xc8Nl/9Q7lSKQF7vwTVha7D01NJCg4G13WtjeofpGN1kNBzsxXD2CthiryThN7FKcGqi5WrMLXvWRejkVgwCFtNdi9+/i1k1nlYCAc6iWQIsnkoBClCAAhSgAAUoQAEKUKDUAvbi65ey+m+tJvItTnwPHP5CzlNJytHfH7ETJkilydXpEP3sLOhTC4KC0glMaFZAGdrt4+qDaW2nYdO9m/BQq4fgaO9oUZ+49Di8uu9VjNswDrujdkMJHJb3VjfYCxNf7oYGIbUsbn1ufyx+X36hQp5r8TBmUMBGBBgAtJGGZDUoQAEKUIACFKAABShAAQ0IuHoB9/8AOHvKhd38EhCxX85TSSq9ZQvc7NdPKk12RASuv/mGlMeEbQkogcAXu72IDfdsgLIQiLXt4q2LmL5jOqb+NhUnb5y0dkqZ8lw9nTDymfZiFeDGxuG/pjf76/do7F1zkUFAUxTuU6AIAQYAi8DhIQpQgAIUoAAFKEABClCAAuUuULslcM8i+baGHGDlP4Dka3K+SlLxQ4fAuU0bqTTKAiG31q2T8piwPYGgGkF4t9+7WDFyBboHdLdawaPXj2LSpkmYtWsWIpIjrJ5T2kxlrr8uwxth2FPtYO8gz/v357YoHNpwpbS35nUUqFYCDABWq+ZmZSlAAQpQgAIUoAAFKEABVQi0Hg30fU4uSppY+XTFZCDHcs4z+cQqSDk6wv/t+bD3lHsuxr7xJrIuMwBTBS1S6Y9s7dsan9/1ORYPXowWPi2sPn9rxFbcs+4ezDswDwkZCVbPKW1mcDs/DBGrACsLgZhuRzaF48iv4aZZ3KcABawIMABoBYVZFKAABShAAQpQgAIUoAAFKlxg4MtAk0HyY6KPAJuel/NUknIKCkLAG69LpclNT0f0rFlQVgzmZvsCyhyBvQN7Y+WolZjfZz4CPAIsKp2Tm4Pl55dj+E/DsejPRUjXpVucU9qMJp1qY9AUsfiMWRDw4PrL+HN7VGlvy+soUC0EGACsFs3MSlKAAhSgAAUoQAEKUIACqhNQVk+9Tyz+4dNILtqxZcCRr+Q8laRqDh8O7/HjpdJknTuHuPcWSHlM2LaAvZ09RjUZhQ1jN2B2l9mo6VzTosLpOen49MSnxkDgyvMroTPoLM4pTUaL7nUxcJIYRm+27VkVBmVeQG4UoIB1AQYArbswlwIUoAAFKEABClCAAhSgQMULuIsVTieKVYCd3OVnKb0Aow7JeSpJ1Zk7By7NmkqlSfz+e6Rs2yblMWH7Ai4OLng45GHjisFT20yFs72zRaUTMhPw5oE3MWHDBByJFT1cy2Fr3ace+k5sZnGn3T+cx7n9MRb5zKAABQAGAPkuqBSBkJAQmP6EhoZWynP5EApQgAIUoAAFKEABCqheoK5YXGPMx3Ixld5SynyAKbFyvgpS9m5uCPzgA9i5ukqlufbyK9BdU+ciJlJBmSh3AS8XL8zqPAsbx27E6CajxQhdszG64onKisHKasFz/5iL+Iz4Mpeh3cD66Dm2icV9dnxzFmFHrlvkM4MC1V2AAcDq/g5g/SlAAQpQgAIUoAAFKECBqhdocx/Qa4ZcjlQR/Fv5sFgUJFvOV0HKpVkzKD0BTTdDUhKin5uN3ByxojG3aikQ4BmAeX3mYdWoVegT2MeqwYbLGzB67WgsP7cceoPe6jnFzew0tCG6jmgknZ6bC2z78gwun7gh5TNBgeouwABgdX8HVFL9T58+DdOfHTt2VNKT+RgKUIACFKAABShAAQpoRGDQa0Bwf7mwUQeA3+bKeSpJKXMB1hw+TCpNxvHjuPGxWW9G6QwmqoNAi1otsGjwIiwdshStarWyqHKKLgXzDs7Dg5sexKkbpyyOlySj68hgdBzSQLrEYMjFb1/8hcjT5bsSsfQQJiigMQEGADXWYCwuBShAAQpQgAIUoAAFKGCjAg6OwDix+IeXHMzA4c+B49+prtLKirB1X38dyurAplvCZ0uQtn+/aRb3q6lAt4Bu+HHEj5jTbQ48nTwtFM4knMGkTZPw5v43kZSVZHG8OBnK+1AZCtx2gPw+NOTkYtPiU4g+n1ic2/AcCti8AAOANt/ErCAFKEABClCAAhSgAAUooBkBD1/gfhHsc5Tn18PGWUD0UdVVw6FGDTEf4EJRXhG8zNvEGMzoF15ATnzZ53nLuyVftSvgIFa7frDVg8YVg0c2HmlRkVzkYuWFlRi1dhTWXVyHXGUMbwk3JQjYd0IztO4dIF2p1xmw8dOTiL1cuuCidDMmKKBxAQYANd6ALD4FKEABClCAAhSgAAUoYGMCAe2BUR/JldJnAaumALpMOV8FKbd27VD72WelkuhvxOPaS3OQazBI+UxUXwE/Nz+83fdtfDn0SzT2amwBkZiViH/v/TembJ6CC4kXLI7fKcPO3g79J7VE8251pFNzsvTY8NEJxEUkS/lMUKC6CTAAWN1anPWlAAUoQAEKUIACFKAABdQv0H4i0P0puZy3IoFjy+Q8laRqTZ0Cj359pdKk7dmDm1+JIc3cKGAi0LVuV6wetRr/6vQvuDm6mRy5vXss7hgmbJiABYcXIE2XZnG8qAx7EQQc9HArNOnoL52WnanHzyIImBCdKuUzQYHqJMAAYHVqbdaVAhSgAAUoQAEKUIACFNCOwJA3gaBucnn/EMNtdRlyngpSdvb2qPf223D0lwMvcR/+FxknTqighCyCmgScHJzwaNtHsX7MegxqMMiiaPpcPb45841xteDfwn8r0bBgewd73PVoCBq2FcPpTbastBys/+9xJMaWLKhocgvuUkDTAgwAarr5WHgKUIACFKAABShAAQpQwGYFRJAEd70uVy/1OnB4qZynkpSjry/qLXgPEPOx5W85OYh+bjb0yRx+mW/CnXyBAM8A/Hfgf/HJoE8Q6BmYn5+3E5cRh9m7Z+OJrU8gPCk8L/uOrw6O9rj78TYIaukjnZuRosP6D48j6Ua6lM8EBaqDAAOA1aGVWUcKUIACFKAABShAAQpQQJsCDXsBjQfKZd/zIZClzqGMHj16wO+pJ6Xy6qKjEfPqf0rUi0u6ARM2L9AvqB/WjVmHJ9s/CSd7Efg22/bH7Me9P9+Lj49/jMyc4s2D6ejkgOFPtUNAUy/pbmlJ2SIIeAIpN4t3H+liJiigYQGTpZo0XAsWnQIUoAAFKEABClCAAhSggK0KhL4CXN5ZULt0sbruoSVAX7EysAo3v6efRtrBQ8g4WrBqccrmzbjVsyd8Jk5QYYlZJDUIuIqVr5/p8AyUlYLnH5yPfdf2ScXSGXT47ORn+PbMt/By8TLOH6jMIaj8KNfm7Suv7o7u+Xkud7nDKbUWdLEO+fdTgn/KcOBxL3aBq4dlwDH/RO5QwIYEGAC0ocZkVShAAQpQgAIUoAAFKEABGxQI6gI0GwqE/VZQuX1ileCu0wDXmgV5Ktmzc3RE4PsLcPmesTAkJeWX6vr8+XDr0AGuLZrn53GHAuYCDWs2xOLBi7E1YivePfwu4tLjpFPSc9Kh/JRkcwlyx6jkZ+CXHpR/WVJcBr75/Dc89Ewo3J3c8/O5QwFbFeAQYFttWdaLAhSgAAUoQAEKUIACFLAdgYFz5bpkJAIHF8t5Kko5BQSIRUHmSyXKzcpC9KxZMKSXLHgj3YSJaiFgJ+aRHNJoCH6+52c83PphONgV9N4rDUCWUzo2tv4UN91ipct159wxddFMfHTsI8RniJ613ChgwwIMANpw47JqFKAABShAAQpQgAIUoICNCNTrALQcKVdm38eAEghU6VYjNBQ+kydLpcu+dAnXxWrB3ChQHAEPJw/M7jobK0etROc6nYtzSaHnZDql4ZdWi5DlIAegu14YhW+P/Yihq4fi9f2vl2ixkUIfxgMUUKEAhwCrsFFYJApQgAIUoAAFKEABClCAAhYCA+YA5zYWZGeJ4bX7PwVCXy7IU9le7ednI/3oEWSdOZtfslurVsNdLBbiNWJEfh53KFCUQHOf5vj67q8RmRyJ2LRYZORkIEMvfnTiR9kXP5n6TCld2DlHm25Cr/Pj8h/nluOJ/pcm4rcWS7H6wmqsubAGA+sPxNQ2U9Ghtgi8c6OAjQgwAGgjDclqUIACFKAABShAAQpQgAI2LlC3DRAyFji9tqCiBxYBPZ4C3GsV5Kloz97ZGYELF+LKfeOQazL0N1asCuzWti2cGzRQUWlZFLULNKjZAMpPWTaDwYDlH+1B4rmc/NsEJ7ZDs/guCPM/glzxz46oHcafjrU7YkrIFAyoPwD2dhxAmQ/GHU0K8B2syWZjoSlAAQpQgAIUoAAFKECBainQ/yVRbbuCqmenAHv/V5BW4Z5LcDAC/vOqVDJDWpqYD/A55GZnS/lMUKCiBezt7XHPIz3g6imv/tvnyn3wyPKWHn887jhm7pyJMevGGHsGZumzpONMUEBLAgwAaqm1WFYKUIACFKAABShAAQpQoHoL1G4JtB0vGxxaAqTKK6XKJ1R9ymvMGHjdc49UkMy//kLch/+V8pigQGUIuNd0xoBJLaRHuejdMfiymLMyV8o2JsKTw/Ha/teM8wR+fvJzJCnD77lRQGMCDABqrMFYXApQgAIUoAAFKEABClCgmgsMEL0ATVdF1YlFDVTeC1Bpsbr/fgXOjRpJjXfzq6+QsmuXlMcEBSpDoEnH2mjevY70qIBbTTHT9T+o4y7n552UkJmAj45/hLtW34V3D72La6nX8g7xlQKqF2AAUPVNxAJSgAIUoAAFKEABClCAAhQwEfBtArR/wCRD7B7+AkiOkfNUlrL38EDghx/ATswLaLrFzJkL3fXrplncp0ClCPSd0Bwe3i7Ss/T7/LC870+Y32c+mvk0k47lJZQFRr47+x2G/zQcK86tyMvmKwVULcAAoKqbh4WjAAUoQAEKUIACFKAABShgRaD/84C9yZqOOZnAng+snKiuLNdWrVD7xRekQukTE3Ht+ReQq9dL+UxQoKIFXD2cEDpZDKs32XKyDfj92zCMCB6JNaPWYPHgxehet7vJGQW7+lw9Vwou4OCeygUYAFR5A7F4FKAABShAAQpQgAIUoAAFLAR8GgEdH5Kzj34N3IqS81SY8nnwQXgOHiSVLP3QIcR/9pmUxwQFKkOgQYgvQvoFSo+KuZSEE9siYWdnh96BvfHF0C+wfORyDGs0TFoNuGdAT7SoJc8lKN2ICQqoSIABQBU1BotCAQpQgAIUoAAFKEABClCg2AL9RC9AB5PhtHqxou4fC4t9eVWdqARV6r31FhwDAqQixH/8CdIPH5bymKBAZQj0urcJavq5So86+PNlJESn5ueF+Ibgvf7v4Zexv+DBlg/CzdENU9tMzT/OHQqoXYABQLW3EMtHAQpQgAIUoAAFKEABClDAmoBXENB5inzk+LdAYricp8KUg7c3Ahe+LwKYDgWlMxgQPft55IghwdwoUJkCzq6OGPRwa7G4TsFTDTm52Pb1Gej1hoJMsRdUIwhzus/B1nFb0SOgh3SMCQqoWYABQDW3DstGAQpQgAIUoAAFKEABClCgKIE+swBHk55Lhhxg94KirlDNMfdOneA/Y4ZUnhyxGEjM3JeRm5sr5TNBgYoWqNfMGx0GN5AeEx+ViiObwqW8vISXi5dxiHBemq8UULsAA4BqbyEbKV9ISAhMf0JDQ22kZqwGBShAAQpQgAIUoAAFqlCgphhG2+VRuQB//ggkXJLzVJryfWwa3HvKvahSd+5E4reiJyM3ClSyQPfRwfAJ8JCeevTXCMRFJEt5TFBAiwIMAGqx1VhmClCAAhSgAAUoQAEKUIACeQJ9ngWc3PNSEMvpil6A7xakVbxnJ4YA13v3XTjUqiWV8vqC95Hx12kpjwkKVLSAo5MDBk9pBXv7grHAuQYxFPirM8jJ5irVFe3P+1esAAOAFevLu/8tcPr0aZj+7NixgzYUoAAFKEABClCAAhSgQHkIePoD3R6X73RyJXDjvJyn0pRT7drGIKBUPJ0O0c/Ngj41TcpmggIVLVC7YU10Ht5IekxibDqURUG4UUDLAgwAarn1WHYKUIACFKAABShAAQpQgAKKQK9/As6eJhZiDr1db5uk1b3r2bcPfKfJQ5l1EZGIff11zgeo7qazydJ1HtYQ/g1qSHU7sT0K18K4QI2EwoSmBBgA1FRzsbAUoAAFKEABClCAAhSgAAWsCHj4Aj2ekg+cXgtc184wWv+ZM+Havp1Uh+QNG5Dy2xYpjwkKVLSAg4M9BomhwA6OJiETEVPfvuwssjPFQjvcKKBBAZN3swZLzyJTgAIUoAAFKEABClCAAhSgwG2Bns8AYmVSads5X0qqOWHn5ITAhQthX0PueXXjo4/EtIacf03NbWeLZfOt54nuoxtLVUuOz8S+NRelPCYooBUBBgC10lIsJwUoQAEKUIACFKAABShAgaIE3HwAJQhoup3bCFw7YZqj6n3noCDUffVVqYzZly8jedOvUh4TFKgMgfaD6yOgqRxUP/3HNUScTqiMx/MZFChXAQYAy5WTN6MABShAAQpQgAIUoAAFKFCFAsowYCUQaLppqBegUuyaI0fApVUr0xog/tNP2QtQEmGiMgSU1YAHPdwKji4O0uN2fnMWmWk6KY8JCqhdgAFAtbcQy0cBClCAAhSgAAUoQAEKUKC4Aq41by8IYnp+2G/A1SOmOaret7Ozg/90uSdj9pUrSP7lF1WXm4WzTQEvf3f0vq+pVLm0pGz8seKClMcEBdQuwACg2luI5aMABShAAQpQgAIUoAAFKFASgW6PA+5+8hU758lplac8Q0Ph2rq1VMr4TxchN4cLMEgoTFSKQEjfemjQupb0rAuHruPSsTgpjwkKqFmAAUA1tw7LRgEKUIACFKAABShAAQpQoKQCLp5An3/JV13aAUTsl/NUnFJ6AfpNny6VMDs8nL0AJREmKktAeT8OnNwKLu6O0iN3/XAe6cnZUh4TFFCrAAOAam0ZlosCFKAABShAAQpQgAIUoEBpBbo8CnjWka/WWi/AgQPgGhIi1YG9ACUOJipRwNPHBX0nNpeemJmqw5FN4VIeExRQqwADgGptGZaLAhSgAAUoQAEKUIACFKBAaQWc3YG+z8lXh/8BXPldzlNx6nYvQLO5ACMikLRRrGzMjQJVINC8Wx007uif/+T2ofXR694m+WnuUEDNAgwAqrl1WDYKUIACFKAABShAAQpQgAKlFej0sFhSN1C+eoeYCzA3V85TccpzgOgF2KaNVML4RZwLUAJhotIElKD0gAdboHajmrjn2Y7oM6EZHJ0dKu35fBAFyiLAAGBZ9HgtBShAAQpQgAIUoAAFKEABtQo4uVr2Aow6AFzartYSW5TLWi9AXUQkkjawF6AFFjMqRcCthjPGvdgZgS18KuV5fAgFykuAAcDykuR9KEABClCAAhSgAAUoQAEKqE2g42TAu4Fcqp3ztdULsH9/uLZtK9WBvQAlDiYqWUAJTHOjgNYEGADUWouxvBSgAAUoQAEKUIACFKAABYor4OgM9HtBPjv6KHDhNzlPxSkl2OI/XZ4LUBcpegH+vEHFpWbRKEABCqhLgAFAdbUHS0MBClCAAhSgAAUoQAEKUKB8BdrfD/gEy/dUVgTW0FyAHv36wbVdO6kOxl6AOp2UxwQFKEABClgXYADQugtzKUABClCAAhSgAAUoQAEK2IaAgxMw4CW5LrEngbPa6UFntRdgVBR7AcqtyhQFKECBQgUYACyUhgcoQAEKUIACFKAABShAAQrYiEDb8YBfc7kyu94GDAY5T8Upj7594dqevQBV3EQsGgUooGIBBgBV3DgsGgUoQAEKUIACFKAABShAgXIRsHew7AUYdwY4s7Zcbl8ZN7ndC3C69Cjd1atIWr9eymOCAhSgAAUsBRgAtDRhDgUoQAEKUIACFKAABShAAdsTaD0WqN1arteud0QvQL2cp+KUR58+cGvfXiph/KLFyOVcgJIJExSgAAXMBRgANBdhmgIUoAAFKEABClCAAhSggC0K2IuvfwPmyDWLvwCcWiXnqTil9AL0mzFDKqEuOhq31q2T8pigAAUoQAFZgAFA2YMpClCAAhSgAAUoQAEKUIACtivQahRQV55HD0ovQH2OZurs0bsX3Dp0kMqbsPgz5GZnS3lMUIACFKBAgQADgAUW3KMABShAAQpQgAIUoAAFKGDbAqIHHQa+LNcx8Qrw549ynopTt3sBms0FyF6AKm4xFo0CFFCDAAOAamgFloECFKAABShAAQpQgAIUoEBlCTQfCgR2lp+2+z0gRzs96Dx6iV6AHTtKdYhfLOYCZC9AyYQJClCAAnkCDADmSfCVAhSgAAUoQAEKUIACFKBAdRAw9gKcK9c0KRI48Z2cp+KUcUXgGXIvwJxrMbi1lnMBqrjZWDQKUKAKBRgArEJ8PpoCFKAABShAAQpQgAIUoECVCDQZBNTvIT/69/cBXaacp+KUe8+ecOss92SM/4y9AFXcZCwaBShQhQIMAFYhPh9NAQpQgAIUoAAFKEABClCgSgSs9QJMjgaOLauS4pTmocZegNOfkS419gL8aa2UxwQFKEABCgAMAPJdQAEKUIACFKAABShAAQpQoDoKNO4PNOor1/yPhUB2upyn4pR7jx5w62LeC/AzGDgXoIpbjUWjAAWqQoABwKpQ5zMpQAEKUIACFKAABShAAQqoQcB8ReDU68CRL9VQsmKV4XYvQLO5AGNikPTTT8W6nidRgAIUqC4CDABWl5ZmPSlAAQpQgAIUoAAFKEABCpgLNOwJNAmVc/d8CGSlynkqTrl37w73Ll2kEsYvZi9ACYQJClCg2gswAFjt3wKVAxASEgLTn9BQsw8ZlVMMPoUCFKAABShAAQpQgAIUMBcw7wWYHg8cWmJ+lmrTSi9AvxkzpPLlxMbi1urVUh4TFKAABaqzAAOA1bn1WXcKUIACFKAABShAAQpQgAJBovdcs6Gyw76PgMxkOU/FKY/u3eDetatUwoTPlnAuQEmECQpQoDoLMABYnVu/Eut++vRpmP7s2LGjEp/OR1GAAhSgAAUoQAEKUIACRQoMnCsfzkgEDi6W81Se8ptuNhfg9eu4tWqVykvN4lGAAhSoHAEGACvHmU+hAAUoQAEKUIACFKAABSigXoF6HYCWI+Xy7ftY9AK8JeepOGXsBditm1TChCWfw5CVJeUxQQEKUKA6CjAAWB1bnXWmAAUoQAEKUIACFKAABShgLjBgjpyTlQTHQ5/JeSpP+c+w1guQcwGqvNlYPApQoBIEGACsBGQ+ggIUoAAFKEABClCAAhSggOoF6rYBQsZKxXQ8ugROOSlSnpoTyjyA7j16SEVMWCLmAmQvQMmECQpQoPoJMABY/dqcNaYABShAAQpQgAIUoAAFKGBdwNgL0C7/mF12Gppd35Sf1sKO//RnpGLmxMXh1krOBSihMEEBClQ7AQYAq12Ts8IUoAAFKEABClCAAhSgAAUKEfBvAbQdLx0Mjt8KF12SlKfmhHuXLnDvaaUXYGammovNslGAAhSoUAEGACuUlzenAAUoQAEKUIACFKAABSigMYEBLwF2DvmFdjRko+n1jflpLez4m68IfOMGewFqoeFYRgpQoMIEGACsMFremAIUoAAFKEABClCAAhSggAYFfJsA7R+QCh4cvwNIiZXy1Jxw79wZHr16SkWM/1zMBchegJIJExSgQPURYACw+rQ1a0oBClCAAhSgAAUoQAEKUKB4Av2fB+wd8891yNXB6cBH+Wkt7PiZ9QLU34gXvQBXaqHoLCMFKECBchdgALDcSXlDClCAAhSgAAUoQAEKUIACGhfwaQR0fEiqhMOf3wFJ0VKemhPunTqJXoC9pCLGf/45ewFKIkxQgALVRYABwOrS0qwnBShAAQpQgAIUoAAFKECBkgj0ex65Ds75V9jps4GTy/PTWtix1gswcbm26qAFZ5aRAhRQvwADgOpvI5aQAhSgAAUoQAEKUIACFKBA5Qt4BUHfZoL83NNr5bTKU+6dOsKjd2+plAlfLIUhI0PKY4ICFKCArQswAGjrLcz6UYACFKAABShAAQpQgAIUKKWAvvW98pWxp4D4i3KeylP+M6ZLJdTHxyNx+QopjwkKUIACti7AAKCttzDrRwEKUIACFKAABShAAQpQoJQChqDuyHT0kq8+o61egG4dOsCjb1+pDglffMFegJIIExSggK0LMABo6y3M+lGAAhSgAAUoQAEKUIACFCitgL0Drnl3la8+vU5OayDlP/0ZqZT/3959wOlVlYnjfybJpPeEhBBCExCICEpRpAkoiogigiCyClJ1/S0i4vrXXTu6iLiiWwRCFZCqKCAKSgdXiiC9Q0gghPReJjPzv3fCTN4zJXXeedv3fj7j3HPufc89z/cEfPNwzj2Ns2bFnF97F2CCokCAQFULSABW9fAKjgABAgQIECBAgAABAhsm8NqI96QNTH8iYsZzaV2ZlwbstFMM2qeTWYCLF5d5z3WPAAEC3SMgAdg9jlohQIAAAQIECBAgQIBAVQrMHrRNtgx4eBrbU5U4C7DduwBnzzYLMB1VJQIEqlhAArCKB1doBAgQIECAAAECBAgQ2GCBul7x2ojd02YqbDfgvPMD3vnOGLTvPkkcsy7MdgQ2CzAxUSBAoDoFJACrc1xFRYAAAQIECBAgQIAAgW4TeH14uwTgm09FvPlMt7XfUw1t9KXOZgH+uqce7zkECBAomYAEYMnoPZgAAQIECBAgQIAAAQKVITB70NbRPHhc2tkKXAY8YMcdY/C++yZxzJqUzQJctCipUyBAgEC1CUgAVtuIiocAAQIECBAgQIAAAQLdLZAtA27c7pC01QpcBpwHMLr9LMA5c7J3AZoFmA6uEgEC1SYgAVhtIyoeAgQIECBAgAABAgQIFEGgcbuPpa3OyJYAv/l0WlcBpQE7viMGv//9SU9nXXiRWYCJiAIBAtUmIAFYbSMqHgIECBAgQIAAAQIECBRBoGmTd0cM3TRtuYpmAc6+4so0NiUCBAhUkYAEYBUNplAIECBAgAABAgQIECBQNIFsGXBMPDRtPk8ANjendRVQGvCOiTF4//2Tns6+6KJoXOhdgAmKAgECVSMgAVg1QykQAgQIECBAgAABAgQIFFlg4ifSB8x8LlsGnO0IXIHH6H/+YtLrxrlzY+411yR1CgQIEKgWAQnAahlJcRAgQIAAAQIECBAgQKDYAuN3iRg2IX1KhS4DHjCxk1mAF18cTcuXp/EpESBAoAoEJACrYBCFQIAAAQIECBAgQIAAgR4RqKurmmXAudfok09K2FbMmBHzfntDUqdAgACBahCQAKyGURQDAQIECBAgQIAAAQIEekqg/TLgWS9ETH+ip57erc8ZsNNOMfC9703anDVpUjSvWJHUKRAgQKDSBSQAK30E9Z8AAQIECBAgQIAAAQI9KZDvBjx8s/SJFboMOA9i9EknJrE0TJkS8//4p6ROgQABApUuIAFY6SOo/wQIECBAgAABAgQIEOhJgZZlwO02A6nQ3YBztoF77BH9d9wxEZx1/vnZ5saVt7txEoQCAQIECgQkAAswnBIgQIAAAQIECBAgQIDAWgi0XwY8+6WINx5biw+W3y11WUKz/bsAlz33XCy8887y66weESBAYD0FJADXE87HCBAgQIAAAQIECBAgULMC43aOGLFFGv4Tv0nLFVQavP/+0XfrtyU9nvXL88wCTEQUCBCoZAEJwEoePX0nQIAAAQIECBAgQIBAKQTyZcA7HJo+uYKXAdf16hWjT0zfBbjkH/+IxQ88mMaoRIAAgQoVkACs0IHTbQIECBAgQIAAAQIECJRUoP0y4LmTI15/pKRd2pCHD/3IR6J+/PikifxdgA4CBAhUg4AEYDWMohgIECBAgAABAgQIECDQ0wLjdsqWAW+ZPrWCdwOuq6+Pkcd/Poln0X33xZLHn0jqFAgQIFCJAhKAlThq+kyAAAECBAgQIECAAIFSC3S6G/ANkb04r9Q9W+/nDz/ssOg9enTyebMAEw4FAgQqVEACsEIHTrcJECBAgAABAgQIECBQcoH2y4DnvRrx2t9L3q317UCv/v1j1LGfSz6+4M9/jmUvvpjUKRAgQKDSBCQAK23E9JcAAQIECBAgQIAAAQLlIrDxjhEj091z48nK3Q04Zx1+1FHRa+jQVcLZjMZZF0xaVXZGgACBChSQAKzAQavELk+cODEKf/bff/9KDEOfCRAgQIAAAQIECBAoFKjCZcC9Bw+OEZ85ujDKmHfTTdHw2mtJnQIBAgQqSUACsJJGS18JECBAgAABAgQIECBQbgLtlwHPnxox9aFy6+U69WfkZz8bdQMGrPrMihUx66KLV5WdESBAoMIEJAArbMAqtbtPPvlkFP7cfvvtlRqKfhMgQIAAAQIECBAgUCgwdmLEqG0KayIqeDfgPJA+I0bE8CMOT2Kae911sWLmzKROgQABApUiIAFYKSOlnwQIECBAgAABAgQIEChHgc6WAT+V7Qbc1FSOvV3rPo067riI+vq2+5uXLYvZl/2qreyEAAEClSQgAVhJo6WvBAgQIECAAAECBAgQKEeBDsuAs/flTX2wHHu61n2qHzcuhn3skOT+OVdeGY3z5yd1CgQIEKgEAQnAShglfSRAgAABAgQIECBAgEA5C4zZPmL029MeVvgy4DyYUSecEJHPcHzraFq4MOZc+evWot8ECBCoGAEJwIoZKh0lQIAAAQIECBAgQIBAmQpU6TLgfltuGUM+/KEEffZll0XTkiVJnQIBAgTKXUACsNxHSP8IECBAgAABAgQIECBQCQITD017uWBaxJS/pXUVWBp94olJrxtnz465112f1CkQIECg3AUkAMt9hPSPAAECBAgQIECAAAEClSCQLwPeKPspPKpgGXD/HXaIQfvsXRhVzLroomhevjypUyBAgEA5C0gAlvPo6BsBAgQIECBAgAABAgQqSaD9ZiBP/S7bDbixkiLotK+jTz45qV8xbVrMu+nmpE6BAAEC5SwgAVjOo6NvBAgQIECAAAECBAgQqCSB9suAF74R8er/VVIEnfZ14C67xIDsp/CYdcEF0dxY+cnNwpicEyBQvQISgNU7tiIjQIAAAQIECBAgQIBAzwps9PaIMRPTZ1bBMuA8oNEnn5TEtfzll2PBbX9O6hQIECBQrgISgOU6MvpFgAABAgQIECBAgACBShSo0mXAg/beO/ptn77jcNb550dzc3MljpI+EyBQYwISgDU24MIlQIAAAQIECBAgQIBAUQXaLwNe9GbE5PuL+sieaLyuri5Gn5TuCLz0qadi0b339cTjPYMAAQIbJCABuEF8PkyAAAECBAgQIECAAAECicDobSLG7phURZUsAx5y4IHRd/PNk9jyWYAOAgQIlLuABGC5j5D+ESBAgAABAgQIECBAoNIE2s8CfPr3EY0rKi2KDv2t6907Rp14QlK/+MEHY/Hf/57UKRAgQKDcBCQAy21E9IcAAQIECBAgQIAAAQKVLtD+PYCLZmTLgKtjqeywj30s+my8cTJCs84zCzABUSBAoOwEJADLbkh0iAABAgQIECBAgAABAhUuMOptERu/Mw2iSpYB1/XtG6M+f1wS28K77oqlzzyT1CkQIECgnAQkAMtpNPSFAAECBAgQIECAAAEC1SLQfhZglSwDzodn+OGHR+/hw5ORmnX+BUlZgQABAuUkIAFYTqOhLwQIECBAgAABAgQIEKgWgfbvAVw8K+KVe6oiul4DB8bIz302iWX+H/8YyydPTuoUCBAgUC4CEoDlMhL6QYAAAQIECBAgQIAAgWoSGLlVxLid04iqZBlwHtSIo4+OXoMGrYqvqSlmTbpwVdkZAQIEykhAArCMBkNXCBAgQIAAAQIECBAgUFUCHZYB35jtBtxQFSH2HjYsRnz6qCSWuTfcEA3Tpyd1CgQIECgHAQnAchgFfSBAgAABAgQIECBAgEA1CrRfBrxkdsTLd1dNpCM/97nINwVpOxoaYvZFF7cVnRAgQKBcBCQAy2Uk9IMAAQIECBAgQIAAAQLVJjBii4hN3p1GVUXLgPtstFG2Icgnk/jmXHNNrJgzJ6lTIECAQKkFJABLPQKeT4AAAQIECBAgQIAAgWoWqOJlwPmwjfz88RG9e7eNYPOSJTHnV5e3lZ0QIECgHAQkAMthFPSBAAECBAgQIECAAAEC1SrQfhnw0rkRL91VNdH23XR8DPvowUk8s6+4IhoXLkrqFAgQIFBKAQnAUup7NgECBAgQIECAAAECBKpdYPhmEeN3TaOsomXAeWCjTjwxia9p3ryYe/XVSZ0CAQIESikgAVhKfc8mQIAAAQIECBAgQIBALQi0Xwb8TLYb8IrlVRN5v623jsEfOCCJZ9YlF0fTsmVJnQIBAgRKJSABWCp5zyVAgAABAgQIECBAgECtCOzw8TTSpfOyZcB3pnUVXhp90klJBI0zZsa8396Q1CkQIECgVAISgKWS91wCBAgQIECAAAECBAjUisDwCRGb7p5GW2XLgAe8850xcI/3JjHOmjQpmlesSOoUCBAgUAoBCcBSqHsmAQIECBAgQIAAAQIEak2gwzLgm7NlwNW1RHb0yScno9owdWrMv+WWpE5hAwSamyOaGjegAR8lULsCEoC1O/YiJ0CAAAECBAgQIECAQM8JtF8GvCxbBvziHT33/B540sD3vCf67/TO5Emzzr8gmpuakjqF9RC492cRPxwf8fOdIyb/dT0a8BECtS0gAVjb4y96AgQIECBAgAABAgQI9IzAsCx5MyFdIhtVtgy4rq4u2r8LcNnzz8fCO+/sGeNqfcpLd0X8+dsRDYsi5r4accvXqjVScREomoAEYNFoNUyAAAECBAgQIECAAAECiUD7ZcDP/iFL6ixNbqn0wuD99ot+22ydhDHzvPOiOV++6lh3gXz25G3/nn7ujccilsxJ65QIEFitgATganlcJECAAAECBAgQIECAAIFuE9jhY1lTdauaWzY/WwZ8+6pyFZzV9eoVo9rtCLz0H4/F4r89UAXRlSCEJ66PmPaPjg9+4/GOdWoIEOhSQAKwSxoXCBAgQIAAAQIECBAgQKBbBYZuErHZHmmTVbYMOA9u6EEHRf2mmyZxzjr/vKSssBYC+ezQv3yv8xs7Swp2fqdaAgQyAQlAfwwIECBAgAABAgQIECBAoOcEOl0GvKTnnt8DT6rr0ydGnXB88qRF9/81ljxu1lqCsqbCgxdEzHu187skADt3UUugCwEJwC5gVBMgQIAAAQIECBAgQIBAEQTaLwNevjDihb8U4UGlbXLYJz4RvTcanXQifxegYy0FFs+OuPvsrm+WAOzaxhUCnQhIAHaCoooAAQIECBAgQIAAAQIEiiQwZOOIzfdMG6/CZcC9+vWLUccem8S58M9/iXxXYMdaCNz704il87q+cWbmuCxLHjsIEFgrAQnAtWJyEwECBAgQIECAAAECBAh0m8DEQ9Omnr0l2w24upYB5wEOP/Ko6DVsWBLrzPOzZa2O1QvMmRzxt3azJd/+kWz/mN4Fn8t2VZ7+REHZKQECqxOQAFydjmsECBAgQIAAAQIECBAg0P0C22e7AdcV/HW0YVHE87d1/3NK3GLvwYNi5DHHJL2Yf/PNsfzVV5M6hXYCd5wZ0bh8VWWv+ogPZXVjtl9Vl59ZBpx6KBFYjUDBv3FXc5dLBAgQIECAAAECBAgQIECguwSGjK2JZcA518h/OibqBg5cJdfUFLMumLSq7CwVeP3RiMeuTut2OyGD3Cpi3E5pvQRg6qFEYDUCEoCrwXGJAAECBAgQIECAAAECBIok0H434Of+GLF8cZEeVrpmew8fHiOOOirpwNwbboiG6dOTOoVMoDlb1nvbv6cU/YZG7HPGyjoJwNRGicA6CEgArgOWWwkQIECAAAECBAgQIECgmwQ6LAPOkn/P39pNjZdXMyOP/VzU9e27qlMNDTH7ootWlZ2tFMh3g3757lRjr9MiBo1aWdc+Afjm09m7I5em9ysRINCpgARgpywqCRAgQIAAAQIECBAgQKCoAoM3ithi7/QRVbgbcB5g/ZgxMfzwTyaxzrn6mlgxe3ZSV9OFpsZs9t+3UoKh4yPe+4VVdWPfkZ3XrSo3Z59588lVZWcECHQpIAHYJY0LBAgQIECAAAECBAgQIFBUgQ7LgP+ULQPONgSpwmPU8cdH9OnTFlnz0qUx+7LL2so1f/KPqzom8/b7ZpY9HbCKpt/giNHbrCrnZ94DmHooEehCQAKwCxjVBAgQIECAAAECBAgQIFBkge0PySZ09V71kBVLIp7LkoBVeNSPHx/DPvrRJLI5V1wZjQsWJHU1WWjIxv32H6Shj5kYsVP67sSWGzZ+Z3qfBGDqoUSgCwEJwC5gVBMgQIAAAQIECBAgQIBAkQUGjY7Ycp/0IVW6DDgPctRJJ2UJz1VLWJuy5F+eBKz54//+N2LB6ynDB78X0asgOdx6tf17ACUAW2X8JrBaAQnA1fK4SIAAAQIECBAgQIAAAQJFFWi/i4b74QAAQABJREFUDDjfCGTZwqI+slSN99tqyxjyoQ8lj5996aXRtLj6dj9OglxdYdHMiHv/M71jq/dHbH1AWtdaap8AnJ69A7CxofWq3wQIdCEgAdgFjGoCBAgQIECAAAECBAgQ6AGBDsuAs11dn/tjDzy4NI8YfdKJyYMb58yJudddl9TVVOHus7OE7/w05Hz2X8FMyeTiuHZLgBuXR8x4JrlFgQCBjgISgB1N1BAgQIAAAQIECBAgQIBATwkMHBmx1fvTp1XxMuD+O+wQg/ZNlz3PuvCiaFqeJbJq7Zj1YsSDk9Ko33lkRPtZfoV3DBgRMXzzwhobgaQaSgQ6FZAA7JRFJQECBAgQIECAAAECBAj0mECHZcC3ZbPCqndzjNEnn5LQrpg+PebdcENSVxOF278f0bRiVai9+0bs/2+ryl2dtU8Qeg9gV1LqCbQJSAC2UTghQIAAAQIECBAgQIAAgZIIbHdwtuFDn1WPblwW8Wz1LgMe+O53xcDdd18Vb3Y264JJ0byiIBmWXK3CwtSHItrP9HzPydnsvs3WHKwE4JqN3EGgnYAEYDsQRQIECBAgQIAAAQIECBDoYYGWZcD7pQ9tnxxKr1Z8adTJ2Y7ABUfDlCkx/5bqTXoWhBrR3Bxx27eSqug/PGLv09O6rkrjdk6vvPF4NpOwMa1TIkAgEZAATDgUCBAgQIAAAQIECBAgQKAkAu2XAb+QLQNe2m5ziJJ0rDgPHfS+90X/HXdMGp91/nnR3NSU1FVl4dlbIibfl4a2z1cj8vf7rc3RfiOQhmwX5VkvrM0n3UOgZgUkAGt26AVOgAABAgQIECBAgACBMhLY7iPZMuD6VR3Kd3fNE0VVetRlu9yOPiVb8lpwLHv+hVh4++0FNVV42pgtc/7zt9PA8mW/u6czItMb2pUGj4kYskla6T2AqYcSgXYCEoDtQBQJECBAgAABAgQIECBAoAQC+eyvt+2fPrjKlwEP3m+/6LfN1knMM3+ZzQLMl8hW6/HIryJmPpdGt3+2HLhPv7RuTSXvAVyTkOsEEgEJwIRDoVgCEydOjMKf/fdv93/sxXqwdgkQIECAAAECBAgQqByB9suAX/xLxJK5ldP/dexpXa9eMeqkdBbg0ieeiEX337+OLVXI7csWRtz5o7SzeSLvHZ9M69amJAG4NkruIdAmIAHYRuGEAAECBAgQIECAAAECBEoq8PaDInr3XdWFfBnwUzesKlfh2dCDPhz1m2VLYAuOWdkswKo8/vrfEQunp6F98PvZ0u/1SE10lgCshfcnpnpKBNZaYD3+KVvrtt1IoE3gySefjMKf26v9vRZtkTshQIAAAQIECBAgQGCtBQZkO8G+7YD09tvPzGYBzknrqqhU16dPjDrxhCSixQ8+GIv//vekruILC9+MuO/cNIytPxix1b5p3dqW2icAl2Ubxsx9ZW0/7T4CNScgAVhzQy5gAgQIECBAgAABAgQIlLHALp9LO7coSxzd+u9pXZWVhn3849Fn7Ngkqpm//GVSrvjCnf8R0bCoIIy6iA9+t6C8jqdDs01ABo5OP2QjkNRDiUCBgARgAYZTAgQIECBAgAABAgQIECixwLYfjsh/Co9844iX7ymsqarzXn37xqjjP5/EtOjue2LpU08ldRVbmJFt+vHwJWn3d/5MxNiJad26lLJdlKP9LEAJwHURdG+NCUgA1tiAC5cAAQIECBAgQIAAAQJlLZAndg4+J6Lv4LSbN56azSBbktZVUWn4EUdE75Ejk4hmnnd+Uq7Ywl+ymX7Njau632dAxH7fWFVe3zMJwPWV87kaFJAArMFBFzIBAgQIECBAgAABAgTKWmDYphEHfCvt4uwXI+4+O62rolKvAQNi5OfS5c8Lbr01lr2YxV3Jx+S/RjxzUxrBHl+MGDY+rVufUmcJwObm9WnJZwhUvYAEYNUPsQAJECBAgAABAgQIECBQgQK7ZRtjjN817Xi+icQbT6R1VVQacfSno9eQIasiypJZs86/YFW50s7yZNxt7d7fOHBUxJ7ZbM7uONonABfPipj/Wne0rA0CVScgAVh1QyogAgQIECBAgAABAgQIVIFAr94RH/t5RK8+q4JpWhFx479ENBUsJ111teLPemfJvxGfOTqJY95NN8XyqRWW1Mp3bX70yogrjoiY+mAST+z7rxH9h6V161sasUVEv3ZteQ/g+mr6XJULSABW+QALjwABAgQIECBAgAABAhUrkG8SseeX0+6/9nDEAxU8Ky6NpkMpXwZcly0HbjsaG2PWhZPaimV7snBGxEMXR/zqExFnbx1xwxciXrgt7e6ILSN2OS6t25BSy0Yg70xbkABMPZQIvCUgAeiPAgECBAgQIECAAAECBAiUr8A+Z0SMyhJKhcdfvhcxd0phTdWc9xkxIkZ8Kps5V3DMu/430fDmmwU1ZXI6//WIv2UblVx8cMQ520bclCVrX7w9m6GZzdTs7PjAtyP69O3syvrXtV8GLAG4/pY+WdUCEoBVPbyCI0CAAAECBAgQIECAQIUL1PePOCR791/h0bAo4ubTs51lq3PDh5Gf/3zU1de3Rdy8fHnMvviStnJJT+ZMjrj/FxGTPhjx0+0jbskStJPvzcaiqetu9cpiyWdy7nBo1/es75VxO6eflABMPZQIvCVQ8DIFJgQIECBAgAABAgQIECBAoAwFttgr4t2fjfj7Zas69/yfIp78TcQ7PrmqrkrO6seOjWGf+ETMveaatojmXH11jDrpxMhnCPb4MfP5iKd+F/H07yPWNsHWJ0vcvu2ALOn3sYhtPxwxYHhxut1+BuCCaRELpkcMGVuc52mVQIUKSABW6MDpNgECBAgQIECAAAECBGpK4IPZst9n/xixqGAp7C3ZhhJb7RcxcGTVUYw68YSYe9112XLalTPrmhcvjjm/ujw2+pf/V/xY85mVbz6VJf2yhF+e9MvP1+aoH5Ql+w6M2D5L+m2T/e43eG0+tWH3jHpbRP7cfFZo6/HGY1kCMJuh6CBAoE3AEuA2CicECBAgQIAAAQIECBAgULYCA7KZbx/5cdq9RdnGE7f9e1pXJaW+EybE0IOzd+sVHLMvvzwaFy4sqOnm04alEff+LOIXu0T87/si7vqPNSf/8l1433lkxJFXRHztxYgjLslmZR7WM8m/PPx8t+iNd0whpj2alpUIEAgJQH8ICBAgQIAAAQIECBAgQKAyBPJ3yG17UNrXRy6PePnutK5KSqOzJb+FR9P8+THn178urOq+8+eyJdX/896IP2cbdczOEnmrOwaMjHjXP0V8JpuheMYLEYdlG4Fs/9FsJt6A1X2qeNfaLwNe22XKxeuRlgmUnYAEYNkNiQ4RIECAAAECBAgQIECAQKcCdXURB/8kom+7paU3npotAV3S6UcqubLfNttkK1k/kIQw+5JLo2lpNlOvu445r0T8+tMRV34qYs7LXbc6OHun3q7HR3w2exfgV7N3An78v7Jlvtky2+7e1bfrHnR9RQKwaxtXCLwlIAHojwIBAgQIECBAgAABAgQIVI7AsE0jDshmqRUes1/Klqu2Wx5ceL2Cz0eddHLS+8ZZs7J3A16f1K1XIU+Y3pkt8f3v92TvVvxD500Mzazf+8WI47J3L37l6YiP/jR75+L7I3qX2XYC7ROAc1+NWDy785jUEqhRAQnAGh14YRMgQIAAAQIECBAgQKBiBXbLZqJtulva/ft/HvHGE2ldFZQG7PiOGLTnnkkksy68MJqXL0/q1qmQb6aSL/e980cRKzqZTTghSwp+PlsSfFrm+eHsns33WPmuvXV6SA/evNHbs6Rkv/SB+UYgDgIE2gQkANsonBAgQIAAAQIECBAgQIBARQjkGz8ckiX8etWv6m7TiojfZzvkNjWuqquSs9GnnJxEsmLatJh3441J3VoVZmdLfK88Mlvym/3kS3/bH4M2ijj0f1fO+NssSxDmS64r4eid/TkYOzHtqfcAph5KNS8gAVjzfwQAECBAgAABAgQIECBAoAIFxu4QsdeX046//veIB7INKarsGLjbbjFgl2xn3oJj1vkXRHPjWiY78+W+d2Qz+fLlvs9ls//aH3VZauA9p0R86aGInY/OEqsVmCpovwxYArD9KCvXuEAF/lNd4yMmfAIECBAgQIAAAQIECBBYKbD3VyNGbZNq/OX7Efk74KrsGH3ySUlEyydPjgV/ypbprul49paVib+7svf9NS7rePdm2fLek++OOOisiAHDO16vlBoJwEoZKf0skYAEYIngPZYAAQIECBAgQIAAAQIENlCgvn+2FPjctJGGRRE3nx7R3JzWV3hp0N57R/8dslmPBcfM887PwuwiznxjlCuynX1/fVSWEJ1c8Km3TgeNifjEedly3yxBuPGOHa9XWk37BOCsFyKWzq+0KPSXQNEEJACLRqthAgQIECBAgAABAgQIECi6wBbZBhnv/lz6mOdvjXiiG3bKTVstaakuex/fqJPTdwEue/bZWHjHnWm/li+OuP3MbNZf9g6/5/+UXstLddn7E9/zhYj/ly333SlLDlbKe/46RpLWjMmSo73a7U48vfo2hUmDViKw9gISgGtv5U4CBAgQIECAAAECBAgQKEeBD34vYvDYtGd//HrE4tlpXYWXhnzwA9F3q62SKGae98uVswDzmYDP3Lxyue/dP+5iue/73lrumy0H7j8saafiC/ls0I22T8PwHsDUQ6mmBSQAa3r4BU+AAAECBAgQIECAAIEqEMjfXXdQlvQqPBbNiLj13wtrKv68LtucY9RJJyZxLP3HY7H4tt9my32PiLgq28Bj3qvJ9ZZCnhw97IJsue8fsuW+7+h4vVpq2i8DlgCslpEVRzcISAB2A6ImCBAgQIAAAQIECBAgQKDEAjt8POLtH0k78ejlES/dldZVeGnYwQdH/fjxSRQzzzwj4oXbkrqWQr7c973/vHJ333dm7wOsluW+HSNdWSMB2JWMegIhAegPAQECBAgQIECAAAECBAhUvkCe3PrITyL6DkljufHUiIYlaV0Fl+rq62PUCccnESye3jeWzKpP6mLz7N2Ip9wb8eEfZst9h6bXqrXUPgE445mI/J2IDgIEJAD9GSBAgAABAgQIECBAgACBKhEYls2M+8C302DmvBxx11lpXSWXZj4fw5ZeFX36NyZRzHlh0Mry4I2z5b6TIo7N3gc4NtsYo5aOluXNWSK49WhuinjzqdaS3wRqWsAMwJoefsETIECAAAECBAgQIECgygR2zWbHbbp7GtR9P4944/G0rtJKyxZE3PatiP/ZI3pNvjNGbLsoiWD+qwOicedsl+AvPRjxzux9gNW+3DeJ/q1C3ywJOnrb9Mq0R9OyEoEaFZAArNGBFzYBAgQIECBAgAABAgSqUiDbKCM+liX8ehUsiW3OZsv9/l8imtJZcxURf7677+PXRfzXbhH3nZvF0NDS7eFbZktb67Jrbx3NjXUxf+m7a2e5b2vg7X+3XwZsI5D2Qso1KiABWKMDL2wCBAgQIECAAAECBAhUrcCY7SP2Oi0N7/W/R/ztvLSu3EvTs+Wrl3w04vpsVuOCaUlv+wxoiiGbFyx3za7Oveba5J6aLEgA1uSwC3rNAhKAazZyBwECBAgQIECAAAECBAhUmsDep0eM2ibt9e0/yLJkr6Z15VhaOi/ilq9H/HKviMnZRh7tj3x2455fjuFn/Cy5svSpp2LJk08mdTVXaJ8AzJOoK5bXHIOACbQXkABsL6JMgAABAgQIECBAgAABApUvUN9/5VLgwkgasvfm3fSViHxZbTkeTdmmFY9eGfGLXbLZiv+b9bOTJctv2z/ii3+N+OB3Y9D7PxB9xo1LIpl7XbZcuJaPjXdMo8+XTM94Oq1TIlCDAhKANTjoQiZAgAABAgQIECBAgEBNCGz+vohdjk1DfeG2iCeuT+vKoZS/q+7iD0fc8IWIRTM69mjYZhFHXh5xzG+yjS5Wzmys6907hh92WHLv/BtviqYlS5K6mioMGB4xYss0ZO8BTD2UalJAArAmh13QBAgQIECAAAECBAgQqBGBD3w3YvDGabB/zJbXLp6d1pWqlPcjn5V43r4RU/7WsRe9+0Xs+68R/5xd2/6QDrv7Dj/sE0ld08KFMf+Pf+rYTi3VtF8GLAFYS6Mv1i4EJAC7gFFNgAABAgQIECBAgAABAlUgkM8I+8iP00DyGXa3/nta19OlfEfihy9Zudz3oQuzp3eyLHnbg1Ym/vb7RkTfgZ32sH78+Bi0V/auwIKj5pcBSwAW/GlwSmClgASgPwkECBAgQIAAAQIECBAgUN0C238s4u0HpzE+mi2nfemutK6nSlMfiph0QMSNp0Ys6WQmYr6E9ehrsp+rIka2W87aSR+HH354Urvk4Ydj2YsvJnU1VWifAHzjiYjGFTVFIFgC7QUkANuLKBMgQIAAAQIECBAgQIBAdQnU1UUc/JNsFt2QNK48AdfQg+/LWzQz4nf/vDL59/ojaV/yUp8BEftnMxO/+H8R236o4/Uuaobs9/7oPWpUcnXudWX4nsOkh0UstE8ArsjGeNbzRXygpgmUv4AEYPmPkR4SIECAAAECBAgQIECAwIYKDN0k4gPfTluZ83LEXWeldcUo5bPP/nZ+ttz33RGPZDMPOzt2+HjElx6M2OerEfkOxutw1PXtG8MOzT5fcMy74YZoWr68oKaGTgeNjhi6aRqw9wCmHko1J9Cn5iIWMAECBAgQIECAAAECBAjUpsCux0c8fm262cZ9P494xycjNt5x/U2as/f3LV+UbSwya+VPvqw339yj5Sere/aWiOmPd97+6G0jDsreUfi2/Tq/vpa1+TLg2Rde1HZ345w5sfD222Pohz/cVldTJ/kswPlTV4WcJwB3OmpV2RmBGhOQAKyxARcuAQIECBAgQIAAAQIEalagV7YI7pAs4ffLbNOMpoaVDM3ZZhy//5eIE/4c0at3thdHnsxbuCp5lyfxWhJ6byX3WpN6ebJvyZyVCb/8vHEdZ9v1HRzx/mw34t1Pzpb+9t3gIem35ZYxcNddY/FD2fsF3zrmXnNtbScAn725lSLCDMBVFs5qUkACsCaHXdAECBAgQIAAAQIECBCoUYEx20Xs/ZV06e/rf1+5G++KpSsTeuuazFtXyh0/FfHB72XLVMet6ydXe//wTx2RJAAX3X9/LJ86Nfpu2m457GpbqZKL7d8DOO2xLOnblCV5vQmtSkZYGOso4E/+OoK5nQABAgQIECBAgAABAgQqXGDv0yPypbeFR/4+wAXT1n0mX2EbazofMzHi2D9EfPKCbk/+5Y8ecuCB0Wvo0KQXc6+/PinXTKF9AnD5goh8jB0EalRAArBGB17YBAgQIECAAAECBAgQqFmBPv1WLgUuBkCv+ojBG0fkyb4t9o7IN/fY9fMRh02KOPnurG7PYjy1pc1e/fvHsEMOSdqf95vfRvOKFUldTRSGZGMwaEwa6rRH07ISgRoSsAS4hgZbqAQIECBAgAABAgQIECDwlsDme0TseWrEfed2TZIn8waOyn5Gpr8HtJZbr71Vzuv7DYmoq+u6zSJfGX7E4THniivanrJi+vRYeM89MWS/DdtkpK3BSjnJxyCfBfjCbat6nL8HMN/wxUGgBgUkAGtw0IVMgAABAgQIECBAgAABApnAB74bseW+EbNeiOg//K1EX/kk89ZnjPpvt13033HHWPr4420fn3vd9bWXAMyj7ywB2KbihEBtCUgA1tZ4i5YAAQIECBAgQIAAAQIEWgXyWWJbH7Dyp7WuCn4PP/zweKMgAbjwzjuj4c03o35MuyWxVRDrakNo/x7AfAZgvstzCWdorra/LhIoooB3ABYRV9MECBAgQIAAAQIECBAgQKCnBYYefHDUDRy46rGNjTHvtzesKtfKWfsE4JI5EfOm1Er04iSQCEgAJhwKBAgQIECAAAECBAgQIECgsgV6Dx4UQw/6cBLE3Ouui+ampqSu6gvDN1u5tLsw0HwWoINADQpIANbgoAuZAAECBAgQIECAAAECBKpbYMQRRyQBNkyZEosfeCCpq/pC60YghYFKABZqOK8hAQnAGhpsoRIgQIAAAQIECBAgQIBAbQj032mn6LfN1kmwc6+5NinXRKH9MmAJwJoYdkF2FJAA7GiihgABAgQIECBAgAABAgQIVLRAXTb7bXi7WYALbrstVszJ3oNXS4cEYC2NtlhXIyABuBoclwgQIECAAAECBAgQIECAQKUKDD3kkKirr2/rfnNDQ8z//e/byjVxMm7nNMyF0yMWvJHWKRGoAQEJwBoYZCESIECAAAECBAgQIECAQO0J9BkxIoYceGASeMtmIM3NSV1VF0ZuFdF3cBqiZcCph1JNCEgA1sQwC5IAAQIECBAgQIAAAQIEalFg+BGHJ2Eve/6FWPLoo0ldVRd6ZWmPjd+ZhigBmHoo1YSABGBNDLMgCRAgQIAAAQIECBAgQKAWBQbuvnvUb7ZZEno+C7CmDu8BrKnhFmznAhKAnbuoJUCAAAECBAgQIECAAAECFS9Ql82AG/7JTyZxzP/DLdG4cGFSV9UFCcCqHl7BrZ2ABODaObmLAAECBAgQIECAAAECBAhUpMCwTxwa0bt3W9+blyyJ+Tf/oa1c9SftE4DzpkQsmlX1YQuQQKGABGChhnMCBAgQIECAAAECBAgQIFBlAvVjxsTg978/iWrutdcm5aoujN42ok//NMQ3/pGWlQhUuYAEYJUPsPAIECBAgAABAgQIECBAgED7zUCWPvFELH366dqA6d0nYuw70lhtBJJ6KFW9gARg1Q+xAAkQIECAAAECBAgQIECg1gUG77VX9Bk7NmGYe20NbQbSfhmwBGDyZ0Gh+gUkAKt/jEVIgAABAgQIECBAgAABAjUuUNenT7YZyGGJwrwbb4ympUuTuqotSABW7dAKbO0EJADXzsldBAgQIECAAAECBAgQIECgogWGHZbtBlxX1xZD04IFseBPf2orV/VJ+wTg7Jcils6r6pAFR6BQQAKwUMM5AQIECBAgQIAAAQIECBCoUoG+m46PQe97XxJdzSwDHrN9RK/6JPZ44/G0rESgigUkAKt4cIVGgAABAgQIECBAgAABAgQKBdpvBrL4oYdi2UsvF95Sned9+kWM2S6NbcYzaVmJQBULSABW8eAKjQABAgQIECBAgAABAgQIFAoM2X//6D1iRGFVzL2+RjYDGTAyiTsaG9KyEoEqFpAArOLBFRoBAgQIECBAgAABAgQIECgUqOvbN4YdemhhVcz77Q3RvHx5UqdAgEB1CUgAVtd4ioYAAQIECBAgQIAAAQIECKxWoP0y4MbZs2PBHXeu9jMuEiBQ2QISgJU9fnpPgAABAgQIECBAgAABAgTWSaDfVlvFgF12ST4z99prk7ICAQLVJSABWF3jKRoCBAgQIECAAAECBAgQILBGgfazABfdd180vPbaGj/nBgIEKlNAArAyx02vCRAgQIAAAQIECBAgQIDAegsM/dCHoteQIas+39ycbQbym1VlZwQIVJWABGBVDadgCBAgQIAAAQIECBAgQIDAmgV6DRgQww75aHLj3N/8JpobG5M6BQIEqkNAArA6xlEUBAgQIECAAAECBAgQIEBgnQSGH354cv+KN96IRffem9QpECBQHQISgNUxjqIgQIAAAQIECBAgQIAAAQLrJNB/hx2i/8SJyWfmXnddUlYgQKA6BCQAq2McRUGAAAECBAgQIECAAAECBNZZoP1mIAvuuDNWzJixzu34AAEC5S0gAVje46N3BAgQIECAAAECBAgQIECgaAJDP/rRqMveB9h2rFgRc2+4oa3ohACB6hCQAKyOcRQFAQIECBAgQIAAAQIECBBYZ4HegwfH0A9/OPlcvgy4OdsV2EGAQPUISABWz1iKhAABAgQIECBAgAABAgQIrLPA8COOSD7TMPnVWPzAg0mdAgEClS0gAVjZ46f3BAgQIECAAAECBAgQIEBggwQGvGvn6Pu2tyVtzL322qSsQIBAZQtIAFb2+Ok9AQIECBAgQIAAAQIECBDYIIG6urrosBnIrbdG49y5G9SuDxMgUD4CEoDlMxZ6QoAAAQIECBAgQIAAAQIESiIw7OMfj7r6+rZnNy9fHq+d8bVoXLiorc4JAQKVKyABWLljp+cECBAgQIAAAQIECBAgQKBbBPqMGBFDPviBpK1F99wTk485JhqmT0/qFQgQqDwBCcDKGzM9JkCAAAECBAgQIECAAAEC3S4w+otfjLr+/ZN2lz3zTLzyqSNjafbbQYBA5QpIAFbu2Ok5AQIECBAgQIAAAQIECBDoNoF+W28dm110UfQePjxpc0U2A3Dy0Z+JhdmMQAcBApUpIAFYmePWrb1+7bXX4mc/+1kceOCBsdlmm0Xfvn1j4403jk9+8pPxt7/9rVufpTECBAgQIECAAAECBAgQKF+Bge9+V2xx9VXRd/PNk042LV4cU075Qsy5+pqkXoEAgcoQkACsjHEqai9/8YtfxGmnnRYvvfRSSxLw9NNPj7322it+97vfxfve9764+uqri/p8jRMgQIAAAQIECBAgQIBA+Qjkyb/Nr/p1DNhll7RTjY3xxre/HW/+5CfR3NSUXlMiQKCsBfqUde90rkcEdt9997jzzjtj3333TZ53Tza9+4ADDogvfOELceihh0a/fv2S6woECBAgQIAAAQIECBAgUJ0C+aYgm110YUz7xjdj/s03J0HOmnRhLJ/6WmzyHz+KXu3eGZjcqECAQNkImAFYNkNRuo4cdthhHZJ/eW/23nvv2G+//WLOnDnx+OOPl66DnkyAAAECBAgQIECAAAECPS7QK5sEssnZP45Rp5zc4dkL/vjHePXY42LF7NkdrqkgQKD8BCQAN3BM3nzzzbjpppviW9/6Vhx00EExevToqKura/k59thj16n1yZMnR778drvttotBgwbFyJEjY7fddouzzz47FmfvWyjFUV9f3/LYPn1MFi2Fv2cSIECAAAECBAgQIECglAJ1vXrFmC9/Ocb94PsRvXsnXVny6KPxylGfjmUvv5zUKxAgUH4CsjobOCZjx47dwBZWfvzGG2+MY445JubPn9/WXp70e+ihh1p+Jk2aFDdn0663znZl6qnj1VdfjT//+c8xbty42HHHHXvqsZ5DgAABAgQIECBAgAABAmUmMPzww6NP9nfD1079cjQtXNjWu4bs742TsyTgpv/9XzFw113b6p0QIFBeAmYAduN45Dvo5jvpruvxyCOPxJFHHtmS/Bs8eHCceeaZcf/998df/vKXOPHEE1uae+655+Lggw+OBQsWrGvz63V/Q0ND/NM//VMsW7YszjrrrOw/9KT/pWe9GvUhAgQIECBAgAABAgQIEKhYgcF77hmbX3FFSyKwMIjGefPi1eM+H/NuvKmw2jkBAmUkYAbgBg5GvvQ3X6ab/+SzAV955ZXYcsst16nVU089NZYsWRL5Mttbb7019thjj7bP77///rHNNtvE1772tciTgOecc0585zvfabveepIvHc6TdWt75M/M2+3saMp2c8qXL999990tCcg8EeggQIAAAQIECBAgQIAAAQL9375tbHHVVTE12yxy6VNPtYE0Z5NIXj/jjGh4bWqMOvnkltditV10QoBAyQUkADdwCL773e9uUAsPPPBA5Lvt5sfxxx+fJP9aG86TexdffHE8/fTTce6558Y3v/nNaH03X+s95513XixatKi1uMbfh2fTtztLAObJv89//vNx5ZVXtixJ/uUvf7nGttxAgAABAgQIECBAgAABArUjUD92TGz+q8vitdO/GgvvvDMJfMbPzo3lU6bEuGziSt1b75RPblAgQKAkApYAl4R91UNvuOGGtsJxxx3Xdl540it76epnP/vZlqq5c+fGHXfcUXi55Xxh9g6G5ubmtf55//vf36GNPPmX9+HSSy+NT3/603HJJZdE/mwHAQIECBAgQIAAAQIECBAoFOiVbVyZv/dvxGc+U1jdcj7v+t/ElJNPicYeeoVVhw6oIECgg4DsTgeSnq249957Wx6Y7/q7yy67dPnwfffdt+3afffd13beXSetyb/LLrus5X2Ev/rVr7z3r7twtUOAAAECBAgQIECAAIEqFKjL3hU/9t++GWP/v69HtuY3iXBR9l77yUd/Jhpefz2pVyBAoDQCEoClcW97ar6sNz/y3X3zdwB2dWy33XZtl1o/01axgSety37z5N8RRxwRl19+ueTfBpr6OAECBAgQIECAAAECBGpBoC5L/I383Odi/M/Pjbr+/ZOQlz3/fLycbXi55Iknk3oFAgR6XqDrjFPP96Xmnrh06dKYOXNmS9ybbrrpauMfMWJE5LME8/f8Tcnep9Cdx/e+972WZb/5DsTbbrtt/OAHP+jQ/KGHHho777xzh/quKqZOndrVpZb6adOmtV3PN0DJf8r9yMer9Sg8b63zmwABAgQIECBAgEC1CRR+7y08r7Y4xbPhAvV77RUbX3B+TD/1y9E0e3Zbg40zZsbkY46Jjf7jRzGwYGVb2w09eNI3e+1V74LnLc82LmmsgL+LFnR5vU4r4e/b6xWYD62TgATgOnF1780LCt6HkCff1nS0JgDz9/1155HvXJwfebtnnnlmy3n7/9liiy3WKQE4YcKE9k10Wc53Gx49enSX18vxQt5nBwECBAgQIECAAIFaEvAduJZGe/1j7XPiCTH+oouj34wZbY00Z5Mppp/2lXjzYx+Lee/bo62+p0/eN2tWjG6KaGqsi971zfHsM8/ES7Nv7elu9PjzWice9fiDPbCsBCwBLuFwFP4XtL59+66xJ/369Wu5p7uz9/lmH2vaQOTYY49dY//cQIAAAQIECBAgQIAAAQK1LbBi5MiY8sUvxOKttkog6rJNK8f+7ncx+qabsgxcloUrwdEwbUW8+Icx8dz142LKPSMiGhpL0AuPJFAaATMAS+Pe8tT+Be9HWL58+Rp7smzZspZ7BgwYsMZ7S33DmpYp50uAd99995Zu7rPPPrGmJdCljid/fp6wbf2vnnmfC8evHPqnDwQIECBAgAABAgS6W8B34O4WrZ32mg8+OGZ+7/uxKE/4FRwj77k3Nq3vG6N/8P3o1YN/t13w2xti1i3ZarqmlWmQha8NiBEPvxkHHndgQe+q83RNr+iqzqhF1V5AArC9SA+WhwwZ0va0tVnWm7//Lz/WZrlwW8MlOlmXhF6e0KyEpGYhZZ78q7Q+F/bfOQECBAgQIECAAIF1FfAdeF3Favz+7O95E87+cczMXic187/+K8FYfPvt8eYXZsaE//mf6DNqVHKtuwvNjY3x5o/PjtmXXtqh6QX3PRODb/ljDD/sEx2uVVOFv7tW02iufyyWAK+/3QZ/Mv8/0FFv/ctuTRn5OXPmtGwAkj90Xd6vt8Gd1AABAgQIECBAgAABAgQIEFgPgXyH4I2+9M8x7kc/iuiTzj9a+o/H4pWjPh3LXnp5PVpeu480Zu/dn/KFL3Sa/Gtt4Y1sU8ylzz3XWvSbQNUKSACWeGh32GGHlh688MILsWLFii5780z2ctLWY/vtt2899ZsAAQIECBAgQIAAAQIECJS1wPBPHBqbTbogehWsgss73DBlSrzy6U/H4oce6vb+L3/11ZYE46K771lt2/kGJa/lOxe/teJutTe7SKCCBSQASzx4e2VbpedHvrz34Ycf7rI3d911V9u1Pffcs+3cCQECBAgQIECAAAECBAgQKHeBQe99b2xx5RXRZ5NxSVeb5s2LV4/7fMy76eakfkMKi/72QLxyxKdi+Ysvps1kGZBxu8+JEdusfL1W68XlL78c0779nZbNMVvr/CZQbQISgCUe0UMPPbStBxdffHHbeeFJU7ZD0mWXXdZSNXz48Nhvv/0KLzsnQIAAAQIECBAgQIAAAQJlL9Bvm21iy6uvjv4TJyZ9bW5oiNe/+tWYed75G5yEm3PNNfHq8cdHY5ZYLDx6jxgRmx+9aQzfakmM2Xle9B+RbsQ5P9usZO7V1xR+xDmBqhKQACzxcOY74e69994tvbjwwgvjr3/9a4cenXPOOfH000+31J966qlRX1/f4R4VBAgQIECAAAECBAgQIECg3AX6bLRRbP6ry2JwJxNbZvznf8Yb3/pW5AnBdT2as1dqvfHDH2af/3Zk79dKPp4nHre49toYOGFAS32v3hHj95yT7ULcN7lv+plnxpInn0zqFAhUi0D6Fs5qiaoH47j33nsjf39f6zFz5szW05b6Sy65pK2cnxx77LFJOS+ce+65kS/rXbJkSRx44IHxjW98o2WWX16+6qqr4vzzz2/5zLbbbhunn356h8+rIECAAAECBAgQIECAAAEClSLQa+DA2PS/fhHTf/ijmHPFFUm35157XTS8Pi3Gn/uz6D14cHKtq0K+2cdrp30lFmV/P29/5InGTc4+O2trUHKp7+DG2OTED8bUn69aepwnHl/78mmx5fXXRe+hQ5P7FQhUuoAE4AaO4KRJk+LSTrYTz5u97777Wn4KH9FZAvBd73pXXJ1Ngz7mmGNi/vz5LQnAws/k53ny7+abb44h7V6a2v4+ZQIECBAgQIAAAQIECBAgUO4Cdb17x9h/+2b03WxCTP+PsyJb+9vW5UXZ36UnH/2ZmHD+eVG/8cZt9Z2dLJ88Odvp94ux/KWXOlwedcLxsdFpp0X+rM6OIe9+W4w87riYXfA6rnxjkmnf/GaM//nPI9/F2EGgWgQsAS6TkTzkkEPisccei9Oyfznlyb6B2X8Ryd/3t+uuu8ZZZ50VjzzySGy99dZl0lvdIECAAAECBAgQIECAAAECGyaQJ9hGfu5zLbP96vr1Sxpb9txz8cqnjoylb70OK7n4VmHR//2t5Z72yb+67LVZ4370oxiTvVewq+Rfa3tjvnJaDNh559Ziy+8Ft/055rz1Hv7kggKBChaQANzAwcuX+DZn/6VibX9W97jNN988fvrTn8azzz7bsivwnDlz4sEHH4yvfe1rLQnB1X3WNQIECBAgQIAAAQIECBAgUIkCQ7NXYW1+2aXRe+TIpPsr3nwzJn/mmFh4991JfV6Yk23Y8eoJJ3Tc7CNrY7Nsld7wT6zacLPDhwsq8mTh+P/8afTOJuAUHtPP/kksefTRwirnBCpaQAKwoodP5wkQIECAAAECBAgQIECAQOULDNhpp9ji6qui75ZbJsE0LV7cssQ3T/jlR8tmHz84M974diebfbz97bHltdfEwHe/K2ljTYX6ceOy9wT+OL0t20hkavZewRXZxBwHgWoQkACshlEUAwECBAgQIECAAAECBAgQqHCBvhMmxBa/vjIGZq/CSo7GxpaE3/SzfhxTTj4l5lx+eXI5Lwzef//Y4soron78+A7X1qZi8N57x6hTTk5uXTFtWrz+9a9Hc1NTUq9AoBIFbAJSiaNWgX2eOHFi0uuG9djWPWlAgQABAgQIECBAgAABAgSqTiBfijvhogtj2je+GfNvuimJr3CzjsILo048Mdvs48tR12vD5jht9KUvxZK/PxKLH3igrflFd90dsyZdGKNPOrGtzgmBShTYsH86KjFifSZAgAABAgQIECBAgAABAgTKVqBX374tS3JHfeGU1fYxf3/fJmf9R4w5/SsbnPzLH1TXp09s8pOzo/fo0clzZ/zsZ7GoICmYXFQgUCECEoAVMlCV3s0nn3wyCn9uv/32Sg9J/wkQIECAAAECBAgQIECgSAL5DsFjTj01xv3g+xG9e3d4Su9Ro2KzbOOQYR//eIdrG1JRP2ZMjP/JTyIKZxNmS4BfP/2rsWLmzA1p2mcJlFRAArCk/B5OgAABAgQIECBAgAABAgQIdCUw/PDDY8J550WvQYPabum33Xax5TVXx8B3rdtmH20NrOFk0HvfExv9vy8ld62YMSNe++oZ0Zy9j9BBoBIFJAArcdT0mQABAgQIECBAgAABAgQI1IjA4L32jC1/d0OMOumkGPP1f92gzT7WlmzUySfHoL32Sm5f/H//FzP/+3+SOgUClSIgAVgpI6WfBAgQIECAAAECBAgQIECgRgX6brppjPnKaTHq2GOj18CBRVfINxTZ5OwfR5+xY5Nnzfzf/42F996X1CkQqAQBCcBKGCV9JECAAAECBAgQIECAAAECBHpUoM+IETH+P/8zItscpO1obo7XzzgjGqZPb6tyQqASBCQAK2GU9JEAAQIECBAgQIAAAQIECBDocYGB735XNvPwK8lzG+fMide+cno0NzQk9QoEyllAArCcR0ffCBAgQIAAAQIECBAgQIAAgZIKjDzu2Bh8wAFJH5Y8/HDMOPfcpE6BQDkLSACW8+joGwECBAgQIECAAAECBAgQIFBSgbq6utjkh2dG/fjxST9mTbowFtx+R1KnQKBcBSQAy3Vk9IsAAQIECBAgQIAAAQIECBAoC4Hew4bF+J/9LOrq61f1p3fvaHhj2qqyMwJlLCABWMaDo2sECBAgQIAAAQIECBAgQIBAeQgM2PEdMebr/9rSmT4bbxyb/+pXMfLoo8ujc3pBYA0CBVvZrOFOlwkQIECAAAECBAgQIECAAAECNSwwIkv4NS9dGsMOOyzyXYIdBCpFQAKwUkZKPwkQIECAAAECBAgQIECAAIGSCuTvAxx1/PEl7YOHE1gfAUuA10fNZwgQIECAAAECBAgQIECAAAECBAhUiIAEYIUMlG4SIECAAAECBAgQIECAAAECBAgQWB8BS4DXR81n1llg4sSJyWcaGhqSsgIBAgQIECBAgAABAgQIECBAgEBxBMwALI6rVgkQIECAAAECBAgQIECAAAECBAiUhYAZgGUxDNXfiSeffDIJcurUqTFhwoSkToEAAQIECBAgQIAAAQIECBAgQKD7BcwA7H5TLRIgQIAAAQIECBAgQIAAAQIECBAoGwEJwLIZCh0hQIAAAQIECBAgQIAAAQIECBAg0P0CEoDdb6pFAgQIECBAgAABAgQIECBAgAABAmUjIAFYNkOhIwQIECBAgAABAgQIECBAgAABAgS6X0ACsPtNtUiAAAECBAgQIECAAAECBAgQIECgbAQkAMtmKHSEAAECBAgQIECAAAECBAgQIECAQPcLSAB2v6kWCRAgQIAAAQIECBAgQIAAAQIECJSNgARg2QyFjhAgQIAAAQIECBAgQIAAAQIECBDofgEJwO431SIBAgQIECBAgAABAgQIECBAgACBshGQACybodARAgQIECBAgAABAgQIECBAgAABAt0vIAHY/aZaJECAAAECBAgQIECAAAECBAgQIFA2AhKAZTMUOkKAAAECBAgQIECAAAECBAgQIECg+wUkALvfVIsECBAgQIAAAQIECBAgQIAAAQIEykZAArBshkJHCBAgQIAAAQIECBAgQIAAAQIECHS/gARg95tqkQABAgQIECBAgAABAgQIECBAgEDZCEgAls1Q6AgBAgQIECBAgAABAgQIECBAgACB7hfo0/1NapFAR4GJEycmlQ0NDUlZgQABAgQIECBAgAABAgQIECBAoDgCZgAWx1WrBAgQIECAAAECBAgQIECAAAECBMpCwAzAshiG6u/Ek08+mQQ5derUmDBhQlKnQIAAAQIECBAgQIAAAQIECBAg0P0CZgB2v6kWCRAgQIAAAQIECBAgQIAAAQIECJSNgARg2QyFjhAgQIAAAQIECBAgQIAAAQIECBDofgEJwO431SIBAgQIECBAgAABAgQIECBAgACBshHwDsCyGYra6siKFSvaAp42bVrbeTmfLFmyJGbOnNnSxfwdhgMGDCjn7uobAQIECBAgQIAAgQ0W8B14gwk1UE4Cs5dEzG9a1aM350Rkf7er9qPw79yFfxev9rjFlwrUNWdHWqVEoPgCDz74YOy+++7Ff5AnECBAgAABAgQIECBAgAABAi0CDzzwQOy22240alDAEuAaHHQhEyBAgAABAgQIECBAgAABAgQI1I6AGYC1M9ZlFenSpUvj8ccfb+nTRhttFH36rN1q9P3337/lM7fffnuPx5NPm26dtZj/V5Nx48b1eB88kEA1CZTyn+dqcqykWIx5cUeLb+pb7R6VHF8l9L3c+ljK/vgOnP67RYlAdwj09D/T+bLfGTNmtHR9xx13jP79+3dHGNqoMIG1y7pUWFC6W/4C+b9w1mfacX19fUtwm266aUmDzJN/pe5DSQE8nEA3CJTLP8/dEIom1lLAmK8l1HrexjeFq3aPSo6vEvpebn0sl/74Dpz+e0aJwPoKlOKf6S222GJ9u+tzVSJgCXCVDKQwCBAgQIAAAQIECBAgQIAAAQIECHQmIAHYmYo6AgQIECBAgAABAgQIECBAgAABAlUiIAFYJQMpDAIECBAgQIAAAQIECBAgQIAAAQKdCdgEpDMVdQQ6EZg6dWpMmDCh5cqUKVO8A7ATI1UECBAgQIAAAQLVJeA7cHWNp2gIEKhdATMAa3fsRU6AAAECBAgQIECAAAECBAgQIFADAhKANTDIQiRAgAABAgQIECBAgAABAgQIEKhdAQnA2h17kRMgQIAAAQIECBAgQIAAAQIECNSAgHcA1sAgC5EAAQIECBAgQIAAAQIECBAgQKB2BcwArN2xFzkBAgQIECBAgAABAgQIECBAgEANCEgA1sAgC5EAAQIECBAgQIAAAQIECBAgQKB2BSQAa3fsRU6AAAECBAgQIECAAAECBAgQIFADAhKANTDIQiRAgAABAgQIECBAgAABAgQIEKhdAQnA2h17kRMgQIAAAQIECBAgQIAAAQIECNSAgARgDQyyEAkQIECAAAECBAgQIECAAAECBGpXQAKwdsde5AQIECBAgAABAgQIECBAgAABAjUgIAFYA4MsxPIRePDBB+MjH/lIDB8+PAYNGhTvfe9745prrimfDuoJAQIECBAgQIAAgW4UuPzyy+Pkk0+OXXfdNfr16xd1dXVxySWXdOMTNEWAAAECayPQZ21ucg8BAhsucMcdd8SHPvSh6N+/fxx11FExZMiQuP766+PII4+MKVOmxOmnn77hD9ECAQIECBAgQIAAgTIS+Ld/+7eYPHlyjB49OsaNG9dyXkbd0xUCBAjUjIAZgDUz1AItpcCKFSvixBNPjF69esXdd98d559/fpxzzjnxj3/8I7bddtv4xje+4ctQKQfIswkQIECAAAECBIoiMGnSpHjllVdixowZccoppxTlGRolQIAAgTULSACu2cgdBDZY4Pbbb48XX3wxjj766Nh5553b2hs2bFhL8m/58uVx6aWXttU7IUCAAAECBAgQIFANAh/4wAdi8803r4ZQxECAAIGKFpAArOjh0/m1EXjzzTfjpptuim9961tx0EEHtSw/yN89kv8ce+yxa9NE2z358oV8qe52223X8g6/kSNHxm677RZnn312LF68uO2+9id33nlnS9WBBx7Y/lLLsuC88q677upwTQUBAgQIECBAgACB9REoh+/A69NvnyFAgACB4gh4B2BxXLVaRgJjx47tlt7ceOONccwxx8T8+fPb2suTfg899FDLT7684eabb46tt9667XrryfPPP99yus0227RWtf3eeOONY/DgwdF6T9sFJwQIECBAgAABAgTWU6AcvgOvZ9d9jAABAgSKIGAGYBFQNVm+Aptttll0NgtvTT1+5JFHWjbryJN/ebLuzDPPjPvvvz/+8pe/tLzbL//8c889FwcffHAsWLCgQ3Pz5s1rqcuX/HZ2DB06NFrv6ey6OgIECBAgQIAAAQLrK1Cq78Dr21+fI0CAAIHuFzADsPtNtVhmAvnS33yZbv6T/5fQ/CXEW2655Tr18tRTT40lS5ZEnz594tZbb4099tij7fP7779/5DP7vva1r7UkAfPNPb7zne+0XXdCgAABAgQIECBAoKcFfAfuaXHPI0CAQHkLmAFY3uOjd90g8N3vfjc++tGPtiT/1qe5Bx54IO65556Wjx5//PFJ8q+1vfy9gNtvv31L8dxzz42GhobWSy2/W2f+dTXLL59Z2HpP8kEFAgQIECBAgAABAushUA7fgdej2z5CgAABAkUSkAAsEqxmq0fghhtuaAvmuOOOazsvPOnVq1d89rOfbamaO3du3HHHHYWXW2YI5hWdvefvjTfeiIULF7bdk3xQgQABAgQIECBAgEAJBLrjO3AJuu2RBAgQINCFgARgFzCqCbQK3HvvvS2ngwYNil122aW1usPvfffdt63uvvvuazvPT1qv5cuH2x9/+tOfWqpa72l/XZkAAQIECBAgQIBATwt0x3fgnu6z5xEgQIBA1wISgF3buEKgReDpp59u+Z3v7pu/A7CrY7vttmu71PqZ1ooDDjggttpqq7jyyivj0Ucfba1u2fjjhz/8YfTt27dtBmHbRScECBAgQIAAAQIESiTQ+n12Q74Dl6jrHkuAAAECnQh0nc3o5GZVBGpNYOnSpTFz5syWsDfddNPVhj9ixIjIZwkuWrQopkyZktybJw4nTZoUH/rQh2KfffaJo446KoYMGRLXX399TJ48OX7yk5/EFltskXxGgQABAgQIECBAgEApBLrrO3De9/w7cOtswscff7wlnLzuzjvvbDnfa6+94oQTTmg59z8ECBAgUDwBCcDi2Wq5CgQWLFjQFsXgwYPbzrs6aU0A5u/0a3/st99+LV9+vv3tb8fVV1/dslHIjjvuGGeddVYceeSR7W9XJkCAAAECBAgQIFASge78Dpwn/y699NIkjvx1OYWvzJEATHgUCBAgUBQBCcCisGq0WgTy//rZeuTLdNd09OvXr+WWJUuWdHrr7rvvHrfcckun11QSIECAAAECBAgQKAeB7vwOfMkll0T+4yBAgACB0gp4B2Bp/T29zAX69+/f1sPly5e3nXd1smzZspZLAwYM6OoW9QQIECBAgAABAgTKWsB34LIeHp0jQIDAeglIAK4Xmw/VikD+nr7Wo7Nlva3XWn/n7//Lj7VZLtz6Gb8JECBAgAABAgQIlJOA78DlNBr6QoAAge4RkADsHketVKlA/l8/R40a1RLd1KlTVxvlnDlzWjYAyW+aMGHCau91kQABAgQIECBAgEC5CvgOXK4jo18ECBBYfwEJwPW388kaEdhhhx1aIn3hhRdixYoVXUb9zDPPtF3bfvvt286dECBAgAABAgQIEKg0Ad+BK23E9JcAAQKrF5AAXL2PqwRir732alHIl/c+/PDDXYrcddddbdf23HPPtnMnBAgQIECAAAECBCpNwHfgShsx/SVAgMDqBSQAV+/jKoE49NBD2xQuvvjitvPCk6amprjssstaqoYPHx777bdf4WXnBAgQIECAAAECBCpKwHfgihounSVAgMAaBSQA10jkhloX2H333WPvvfduYbjwwgvjr3/9aweSc845J55++umW+lNPPTXq6+s73KOCAAECBAgQIECAQKUI+A5cKSOlnwQIEFg7gbrm7Fi7W91FoDIF7r333sjf39d6zJw5M84444yWYr5U94QTTmi91PL72GOPTcp54ZFHHon83iVLlrTs8PuNb3yjZZZfXr7qqqvi/PPPb/nMtttuGw899FAU7pzWoTEVBAgQIECAAAECBIos4DtwkYE1T4AAgQoTkACssAHT3XUXyBN6l1566Vp/sKuc+I033hjHHHNMzJ8/v9O28uTfzTffHFtvvXWn11USIECAAAECBAgQ6CkB34F7StpzCBAgUBkClgBXxjjpZRkIHHLIIfHYY4/FaaedFnmyb+DAgZG/72/XXXeNs846q2WWoORfGQyULhAgQIAAAQIECHSbgO/A3UapIQIECJRUwAzAkvJ7OAECBAgQIECAAAECBAgQIECAAIHiCpgBWFxfrRMgQIAAAQIECBAgQIAAAQIECBAoqYAEYEn5PZwAAQIECBAgQIAAAQIECBAgQIBAcQUkAIvrq3UCBAgQIECAAAECBAgQIECAAAECJRWQACwpv4cTIECAAIUrMcgAAAZHSURBVAECBAgQIECAAAECBAgQKK6ABGBxfbVOgAABAgQIECBAgAABAgQIECBAoKQCEoAl5fdwAgQIECBAgAABAgQIECBAgAABAsUVkAAsrq/WCRAgQIAAAQIECBAgQIAAAQIECJRUQAKwpPweToAAAQIECBAgQIAAAQIECBAgQKC4AhKAxfXVOgECBAgQIECAAAECBAgQIECAAIGSCkgAlpTfwwkQIECAAAECBAgQIECAAAECBAgUV0ACsLi+WidAgAABAgQIECBAgAABAgQIECBQUgEJwJLyezgBAgQIECBAgAABAgQIECBAgACB4gpIABbXV+sECBAgQIAAAQIECBAgQIAAAQIESiogAVhSfg8nQIAAAQIECBAgQIAAAQIECBAgUFwBCcDi+mqdAAECBAgQIECAAAECBAgQIECAQEkFJABLyu/hBAgQIECAAAECBAgQIECAAAECBIorIAFYXF+tEyBAgAABAgQIECBAgAABAgQIECipgARgSfk9nAABAgQIECBAgAABAgQIECBAgEBxBSQAi+urdQIECBAgQIAAAQIECBAgQIAAAQIlFZAALCm/hxMgQIAAAQIECBAgQIAAAQIECBAoroAEYHF9tU6AAAECBAgQIECAAAECBAgQIECgpAISgCXl93ACBAgQIECAAAECBAgQIECAAAECxRWQACyur9YJECBAgAABAgQIECBAgAABAgQIlFRAArCk/B5OgAABAgQIECBAgAABAgQIECBAoLgCEoDF9dU6AQIECBAgQIAAAQIECBAgQIAAgZIKSACWlN/DCRAgQIAAAQIECBAgQIAAAQIECBRXQAKwuL5aJ0CAAAECBAgQIECAAAECBAgQIFBSAQnAkvJ7OAECBAgQIECAAAECBAgQIECAAIHiCkgAFtdX6wQIECBAgAABAgQIECBAgAABAgRKKiABWFJ+DydAgAABAgQIECBAgAABAgQIECBQXAEJwOL6ap0AAQIECBAgQIAAAQIECBAgQIBASQUkAEvK7+EECBAgQIAAAQIECBAgQIAAAQIEiisgAVhcX60TIECAAAECBAgQIECAAAECBAgQKKmABGBJ+T2cAAECBAgQIECAAAECBAgQIECAQHEFJACL66t1AgQIECBAgAABAgQIECBAgAABAiUVkAAsKb+HEyBAgAABAgQIECBAgAABAgQIECiugARgcX21ToAAAQIECBAgQIAAAQIECBAgQKCkAhKAJeX3cAIECBAgQIAAAQIECBAgQIAAAQLFFZAALK6v1gkQIECAAAECBAgQIECAAAECBAiUVEACsKT8Hk6AAAECBAgQIECAAAECBAgQIECguAISgMX11ToBAgQIECBAgAABAgQIECBAgACBkgpIAJaU38MJECBAgAABAgQIECBAgAABAgQIFFdAArC4vlonQIAAAQIECBAgQIAAAQIECBAgUFIBCcCS8ns4AQIECBAgQIAAAQIECBAgQIAAgeIKSAAW11frBAgQIECAAAECBAgQIECAAAECBEoqIAFYUn4PJ0CAAAECBAgQIECAAAECBAgQIFBcAQnA4vpqnQABAgQIECBAgAABAgQIECBAgEBJBSQAS8rv4QQIECBAgAABAgQIECBAgAABAgSKKyABWFxfrRMgQIAAAQIECBAgQIAAAQIECBAoqYAEYEn5PZwAAQIECBAgQIAAAQIECBAgQIBAcQUkAIvrq3UCBAgQIECAAAECBAgQIECAAAECJRWQACwpv4cTIECAAAECBAgQIECAAAECBAgQKK6ABGBxfbVOgAABAgQIECBAgAABAgQIECBAoKQCEoAl5fdwAgQIECBAgAABAgQIECBAgAABAsUVkAAsrq/WCRAgQIAAAQIECBAgQIAAAQIECJRUQAKwpPweToAAAQIECBAgQIAAAQIECBAgQKC4AhKAxfXVOgECBAgQIECAAAECBAgQIECAAIGSCkgAlpTfwwkQIECAAAECBAgQIECAAAECBAgUV0ACsLi+WidAgAABAgQIECBAgAABAgQIECBQUgEJwJLyezgBAgQIECBAgAABAgQIECBAgACB4gpIABbXV+sECBAgQIAAAQIECBAgQIAAAQIESiogAVhSfg8nQIAAAQIECBAgQIAAAQIECBAgUFyB/x+Of6uWw49QbAAAAABJRU5ErkJggg==\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure()\\n\",\n    \"for s in symbols:\\n\",\n    \"    plot_acf(range(20),acfs[s])\\n\",\n    \"plt.grid(1)\\n\",\n    \"plt.legend(symbols)\\n\",\n    \"plt.show()    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/ChronoClock.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Partial Gaussianity recovery trought volume clock\\n\",\n    \"\\n\",\n    \"Reproducing Results of ch1 in High-Frequency trading EaslEy, de Prado and O’Hara.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import environment\\n\",\n    \"import utility\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import stats\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"import scipy.signal\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Trading hours:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tmin=9.5*1e9*60*60\\n\",\n    \"tmax=16.5*1e9*60*60\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Selecting data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"symbols = ([\\\"QQQ\\\"])\\n\",\n    \"symbols.sort()\\n\",\n    \"#dates = ['01302019','12282018','05302019','05302018','03272019']\\n\",\n    \"dates = ['01302019']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Extract returns in chrono time and in volume time\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_returns_chrono(symbol,date):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Resample execution price with linear interpolation in minute intervals\\n\",\n    \"    Returns pertental price change normalized by the sqrt of the time interval\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    a = environment.ts(date,'NASDAQ',symbol,PATH='/Volumes/LaCie/data')\\n\",\n    \"    mask = (a.messages.type=='E') & (a.messages.time<tmax) & (a.messages.time>tmin)\\n\",\n    \"    executions = a.messages.loc[mask,['price','time']].copy()\\n\",\n    \"    minutes_t = np.arange(tmin,tmax,60*1e9)\\n\",\n    \"    price = np.interp(minutes_t,executions.time,executions.price)\\n\",\n    \"    t = minutes_t*1e-9\\n\",\n    \"    change = np.diff(price) / price[1:]\\n\",\n    \"    res = change/np.sqrt(np.diff(t))\\n\",\n    \"    return res[~np.isnan(res) & ~np.isinf(res)]\\n\",\n    \"\\n\",\n    \"def get_returns_volume(symbol,date):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Get 50 data points in non constant time-intervals sampled every V/50 executed lots.\\n\",\n    \"    Where V is the total volume traded in that day.\\n\",\n    \"    Returns pertental price change normalized by the sqrt of the time interval\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    a = environment.ts(date,'NASDAQ',symbol,PATH='/Volumes/LaCie/data')\\n\",\n    \"    mask = (a.messages.type=='E') & (a.messages.time<tmax) & (a.messages.time>tmin)\\n\",\n    \"    executions = a.messages.loc[mask].copy()\\n\",\n    \"    total_vol = np.sum(executions['execSize'])\\n\",\n    \"    vol_bin_size = total_vol/50\\n\",\n    \"    x,_ = scipy.signal.find_peaks(np.cumsum(executions['execSize']) % int(vol_bin_size))\\n\",\n    \"    t = executions.iloc[x].time*1e-9\\n\",\n    \"    price = executions.iloc[x]['price']\\n\",\n    \"    change = np.diff(price) / price[1:]\\n\",\n    \"    res = change/np.sqrt(t.diff())\\n\",\n    \"    return res[~np.isnan(res) & ~np.isinf(res)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Building data of returns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"date:\\t01302019\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"chrono = np.array([])\\n\",\n    \"volume = np.array([])\\n\",\n    \"for d in dates:\\n\",\n    \"    print(\\\"date:\\\",d,sep='\\\\t')\\n\",\n    \"    chrono_time_ret = get_returns_chrono(symbols[0],d)\\n\",\n    \"    vol_time_ret = get_returns_volume(symbols[0],d)\\n\",\n    \"    chrono = np.append(chrono,chrono_time_ret)\\n\",\n    \"    volume = np.append(volume,vol_time_ret.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Outiler selection and normalization (if needed):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"chrono_n=chrono#[(chrono < 0.005) & (chrono > -0.005)]\\n\",\n    \"volume_n=volume#[(volume < 0.005) & (volume > -0.005)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### QQPlots of the returns in the two different clocks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure()\\n\",\n    \"plt.title('QQPlot')\\n\",\n    \"plt.subplot(1,2,1)\\n\",\n    \"scipy.stats.probplot(chrono, dist=\\\"norm\\\",plot=plt)\\n\",\n    \"plt.title('chrono clock')\\n\",\n    \"plt.grid(1)\\n\",\n    \"plt.subplot(1,2,2)\\n\",\n    \"scipy.stats.probplot(volume, dist=\\\"norm\\\",plot=plt)\\n\",\n    \"plt.title('volume clock')\\n\",\n    \"plt.grid(1)\\n\",\n    \"plt.ylabel(' ')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/ExecutionsMessage.py",
    "content": "#!/usr/bin/env python2.7\nimport sys\nimport pandas as pd\nfrom datetime import datetime as dt\nfrom matplotlib import pyplot as plt\nimport utility\n\nif __name__ == \"__main__\":\n\n    fileName = str(sys.argv[1])\n    outFile = \".\".join(fileName.split('.')[:-1])+'_Executions.csv'\n    try:\n        print \"Reading file \" + fileName\n        df = pd.read_csv(fileName)\n    except:\n        print \"Error in reading the file.\"\n    try:\n        print \"Converting time...\"\n        df.time = df.time.map(lambda t: utility.parseNanosecondsToDateTime(fileName,t))\n    except:\n        print \"Error in date conversion.\"\n\n    print \"Writing messages...\"\n    df_Executions = df[(df.type == \"P\") | (df.type == \"E\")]\n    df_Executions.to_csv(outFile,index=False)\n    print outFile+\" closed.\"\n\n    print \"Done.\"\n"
  },
  {
    "path": "examples/LFTimePatters.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Evidence of chrono time by LF taders\\n\",\n    \"\\n\",\n    \"Reproducing Figure 1.4 in High-Frequency trading EaslEy, de Prado and O’Hara.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#%%javascript\\n\",\n    \"#IPython.OutputArea.auto_scroll_threshold = 9999;\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import environment\\n\",\n    \"import utility\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import stats\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"from mpl_toolkits.mplot3d import Axes3D\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#plt.rcParams['figure.figsize'] = [18,12]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### List of principal securities\\n\",\n    \"\\n\",\n    \"Choose semi-random liquid-securities in NASDAQ.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"symbols = ([\\\"AAPL\\\",\\\"ARQL\\\",\\\"CSCO\\\",\\\"QQQ\\\",\\\"FB\\\",\\\"INTC\\\",\\\"QCOM\\\",\\\"NVDA\\\",\\\"CMCSA\\\",\\\"AMD\\\",\\\"EEM\\\",\\\"SPY\\\",\\\"GDX\\\",\\\"MSFT\\\",\\\"AMZN\\\"])\\n\",\n    \"symbols.sort()\\n\",\n    \"dates = ['01302019','12282018','05302019','05302018','03272019']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Function to obtain the series of total Volumes for each hour,second pair\\n\",\n    \"avg over minutes (only execution volume)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_total_size(symbol,date):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    read message data, extract executions, group by hour and second bukets by mean.\\n\",\n    \"    So for each pair security/data return a ps.Series of mean volume happend in that Hour/Second combination.\\n\",\n    \"    \\n\",\n    \"    e.g. (15,38) then is the avg volume executed in all times were the hour was 15 and the seconds were 38.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    a = environment.ts(date,'NASDAQ',symbol,PATH='/Volumes/LaCie/data')\\n\",\n    \"    executions = a.messages.loc[a.messages.type=='E'].copy()\\n\",\n    \"    new_time = executions.time.apply(lambda x: utility.parseNanosecondsToDateTime(x))\\n\",\n    \"    executions.loc[:,'time'] = new_time\\n\",\n    \"    executions.loc[:,'hs'] = executions.time.apply(lambda x: (x.hour,x.second))\\n\",\n    \"    ts_total_size_by_sec = executions.fillna(0).groupby(['hs']).mean()\\n\",\n    \"    volumes = ts_total_size_by_sec.loc[ts_total_size_by_sec.index.map(lambda x: x[0]>=9 and x[0]<=16),'size']\\n\",\n    \"    return volumes/volumes.sum()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Joining all the series in a dtaframe\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"AAPL\\t01302019\\n\",\n      \"AAPL\\t12282018\\n\",\n      \"AAPL\\t05302019\\n\",\n      \"AAPL\\t05302018\\n\",\n      \"AAPL\\t03272019\\n\",\n      \"AMD\\t01302019\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.7/site-packages/numpy/core/_methods.py:32: RuntimeWarning: invalid value encountered in reduce\\n\",\n      \"  return umr_minimum(a, axis, None, out, keepdims, initial)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"AMD\\t12282018\\n\",\n      \"AMD\\t05302019\\n\",\n      \"AMD\\t05302018\\n\",\n      \"AMD\\t03272019\\n\",\n      \"AMZN\\t01302019\\n\",\n      \"AMZN\\t12282018\\n\",\n      \"AMZN\\t05302019\\n\",\n      \"AMZN\\t05302018\\n\",\n      \"AMZN\\t03272019\\n\",\n      \"ARQL\\t01302019\\n\",\n      \"ARQL\\t12282018\\n\",\n      \"ARQL\\t05302019\\n\",\n      \"ARQL\\t05302018\\n\",\n      \"ARQL\\t03272019\\n\",\n      \"CMCSA\\t01302019\\n\",\n      \"CMCSA\\t12282018\\n\",\n      \"CMCSA\\t05302019\\n\",\n      \"CMCSA\\t05302018\\n\",\n      \"CMCSA\\t03272019\\n\",\n      \"CSCO\\t01302019\\n\",\n      \"CSCO\\t12282018\\n\",\n      \"CSCO\\t05302019\\n\",\n      \"CSCO\\t05302018\\n\",\n      \"CSCO\\t03272019\\n\",\n      \"EEM\\t01302019\\n\",\n      \"EEM\\t12282018\\n\",\n      \"EEM\\t05302019\\n\",\n      \"EEM\\t05302018\\n\",\n      \"EEM\\t03272019\\n\",\n      \"FB\\t01302019\\n\",\n      \"FB\\t12282018\\n\",\n      \"FB\\t05302019\\n\",\n      \"FB\\t05302018\\n\",\n      \"FB\\t03272019\\n\",\n      \"GDX\\t01302019\\n\",\n      \"GDX\\t12282018\\n\",\n      \"GDX\\t05302019\\n\",\n      \"GDX\\t05302018\\n\",\n      \"GDX\\t03272019\\n\",\n      \"INTC\\t01302019\\n\",\n      \"INTC\\t12282018\\n\",\n      \"INTC\\t05302019\\n\",\n      \"INTC\\t05302018\\n\",\n      \"INTC\\t03272019\\n\",\n      \"MSFT\\t01302019\\n\",\n      \"MSFT\\t12282018\\n\",\n      \"MSFT\\t05302019\\n\",\n      \"MSFT\\t05302018\\n\",\n      \"MSFT\\t03272019\\n\",\n      \"NVDA\\t01302019\\n\",\n      \"NVDA\\t12282018\\n\",\n      \"NVDA\\t05302019\\n\",\n      \"NVDA\\t05302018\\n\",\n      \"NVDA\\t03272019\\n\",\n      \"QCOM\\t01302019\\n\",\n      \"QCOM\\t12282018\\n\",\n      \"QCOM\\t05302019\\n\",\n      \"QCOM\\t05302018\\n\",\n      \"QCOM\\t03272019\\n\",\n      \"QQQ\\t01302019\\n\",\n      \"QQQ\\t12282018\\n\",\n      \"QQQ\\t05302019\\n\",\n      \"QQQ\\t05302018\\n\",\n      \"QQQ\\t03272019\\n\",\n      \"SPY\\t01302019\\n\",\n      \"SPY\\t12282018\\n\",\n      \"SPY\\t05302019\\n\",\n      \"SPY\\t05302018\\n\",\n      \"SPY\\t03272019\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame()\\n\",\n    \"for s in symbols:\\n\",\n    \"    for d in dates:\\n\",\n    \"        print(s,d,sep='\\\\t')\\n\",\n    \"        try:\\n\",\n    \"            serie = get_total_size(s,d)\\n\",\n    \"            serie.name = s+'_'+d\\n\",\n    \"            df=df.join(serie, how='outer')\\n\",\n    \"        except:\\n\",\n    \"            pass\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Normalizing the data since we want to give same weight to a stock very traded (large number size) and one that is not very traded\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"normalized_df = (df-df.min())/(df.max()-df.min())\\n\",\n    \"orig_cols = normalized_df.columns\\n\",\n    \"normalized_df.loc[:,'hour'] = normalized_df.index.map(lambda x: x[0])\\n\",\n    \"normalized_df.loc[:,'sec'] = normalized_df.index.map(lambda x: x[1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Building the matrix with n=0,...,59 (seconds) rows and m=9,...,16 columns (hours). \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n=len(set(normalized_df.sec.values))\\n\",\n    \"m=len(set(normalized_df.hour.values))\\n\",\n    \"B = np.zeros([n,m])\\n\",\n    \"for i in range(n):\\n\",\n    \"    for j in range(m):\\n\",\n    \"        ans = normalized_df.loc[(normalized_df.hour==j+9) & (normalized_df.sec==i)].loc[:,orig_cols].fillna(0).mean(axis=1).values\\n\",\n    \"        if ans.size>0:    \\n\",\n    \"            B[i,j] = ans\\n\",\n    \"\\n\",\n    \"A = B/np.sum(B,axis=0)/m\\n\",\n    \"            \\n\",\n    \"vol_df = pd.DataFrame(data=A*100,index = range(n), columns = range(9,9+m))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAQABJREFUeAHs3Qm8neOdOPDn5mZfCEkYlUiREUsRI4khtjBoYy9mqkWK0hatrajW39oqbXWY6UxTy0R0pZS2qLHFUkuFWksQbUiKSiSRiMh27z/POz239yb33pxz7lne857v+/kc5z3v+6zf57jc332e92loXnUEBwECBAgQIECAAAECBAgQIECAAAECmRTolsle6RQBAgQIECBAgAABAgQIECBAgAABAomAAKAvAgECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKDvAAECBAgQIECAAAECBAgQIECAAIEMCwgAZnhwdY0AAQIECBAgQIAAAQIECBAgQIBAdwTZFFi5cmWYOnVqeOSRR8JLL70UZs2aFd5///2wZMmS0KdPn9C/f/8wbNiwsNVWW4Vx48aF8ePHh8bGxmxi6BUBAgQIECBAgAABAgQIECBAoI4FGppXHXXc/8x1ffHixeGKK64IV111VViwYMEa/YvD3dDQsMb1gQMHhtNOOy2cccYZoV+/fmvcL9eFGzf6TLmKVm6BAj/vsbDAHJKXS2CTbpX7d7BcfchKuc+teDcrXan5fry74v2a70OWOnBEr82y1J2a7stzwb8baRnAKadukJamaMcqgb5nXFPzDsvn/il1fegx2M//1A2KBhHIU8AMwDyhaiHZa6+9Fg444IDwyiuvhNXjujGoF1+9evUKS5cuDTFQGF+5Y/78+eHCCy8MP/vZz8Idd9wRNt1009wt7wQIECBAgAABAgQIECBAgAABAjUsIABYw4PXuulxae/+++8fXn311ST4F5f2HnPMMWGPPfYIW265ZYgz/FY/4gzB6dOnhwcffDDccMMNyVLh+DmW84c//CH07t179Sw+EyBAgAABAgQIECBAgAABAgQI1JiATUBqbMA6au5//dd/JTP/4v3vfOc74YUXXgjnnHNO+Od//ud2g38xXQwKxvsxXUx/+eWXx8vh5ZdfDrE8BwECBAgQIECAAAECBAgQIECAQO0LmAFY+2OY9OCmm25Knu13wgknhDPPPLPgXsXnAp511llhxowZ4ZprrgmxvGLKyVU8e/bs3Kl3AgQIECBAgAABAgQIEChUoGlloTmkJ0CAQIcCAoAd0tTWjfj8v3h86lOf6lLDjzzyyCQAGAOBXTniDsP5HD//h0/nk0waAgQIECBAgAABAgQIECBAgACBIgUEAIuES1u2uLFHPPr06dOlpuXyL1u2rEvlyEyAAAECBAgQIECAAAECXRBobupCZlkJECDQVkAAsK1HzX6KM+7i7r8PPPBA2GmnnYrux9SpU5O8Q4cOLbqMmHHWrFl55X9kzDl5pZOIAAECBAgQIECAAAECBAgQIECgOAEBwOLcUpdrwoQJyeYdl156aRg/fnwYO3ZswW38/e9/H2L++DzAuBNwV46uBhC7Ure8BAgQIECAAAECBAgQIECAAAECfxewC/DfLWr67NRTTw3rrLNOeP/998Nuu+0WTj755DBt2rTQ1NT5tPF4P6Y76aSTwu67757kj+XE8hwECBAgQIAAAQIECBAgUCWB+Ltc2l5VolAtAQJdFzADsOuGqShhk002CT/60Y+STUCWLFkSJk2alLz69esXRowYEeKMvP79+4eePXuG+Hy/GCiMO/XGzT4WL16c9KG5uTl5huCPf/zjkO8mHqnovEYQIECAAAECBAgQIECAAAECBAh0KCAA2CFN7d048MADw+9+97tk9l58j0cM9D377LPJq70exaBf7ogzB6+66qowatSo3CXvBAgQIECAAAECBAgQIECAAAECNS7QsCoA9PcIUI13RvP/LhCX9d52221JQPCll14Kc+fO/fvNv50NGjQobL311mHXXXcNhxxySBgzZswaacp94YaNjyp3FcrPU+C+HkvyTClZuQUWNS8vdxXKz1NgeEPfPFNKVm6BWc0flLsK5Rcg8JGGPgWklrScAs+teLecxSu7AIER3QcWkFrScgtcO/PmcldR9vKXvfnHstdRaAU9P7JNoVmkJ0AgJQJmAKZkIErdjBjMax3QW7p0aVi0aFH48MMPQ+/evcOAAQNCr169Sl2t8ggQIECAAAECBAgQIECAAAECBFImIACYsgEpV3NisE/Ar1y6yiVAgAABAgQIECBAgAABAgQIpFdAADC9Y6NlBAgQIECAAAECBAgQIFCvAnEHYAcBAgRKJNCtROUohgABAgQIECBAgAABAgQIECBAgACBFAoIAKZwUDSJAAECBAgQIECAAAECBAgQIECAQKkELAEulaRyCBAgQIAAAQIECBAgQIBAqQSaLQEuFaVyCBAIwQxA3wICBAgQIECAAAECBAgQIECAAAECGRYQAMzw4OoaAQIECBAgQIAAAQIECBAgQIAAAUuAfQcIECBAgAABAgQIECBAgEDaBJpWpq1F2kOAQA0LmAFYw4On6QQIECBAgAABAgQIECBAgAABAgTWJiAAuDYh9wkQIECAAAECBAgQIECAAAECBAjUsIAlwDU8eFlo+uvdm7PQjUz0Yf3QIxP9yEInlgc7vqVlHOeF5WlpSt2347Wl79a9QZoAPtbro2lqTl23pU+D/36n5Qtw3sD30tIU7ciKgF2AszKS+kEgFQJmAKZiGDSCAAECBAgQIECAAAECBAgQIECAQHkEBADL46pUAgQIECBAgAABAgQIECBAgAABAqkQsAQ4FcOgEQQIECBAgAABAgQIECBAoJVAk8fCtNJwSoBAFwXMAOwioOwECBAgQIAAAQIECBAgQIAAAQIE0iwgAJjm0dE2AgQIECBAgAABAgQIECBAgAABAl0UsAS4i4CyEyBAgAABAgQIECBAgACBUgs02wW41KTKI1DXAmYA1vXw6zwBAgQIECBAgAABAgQIECBAgEDWBcwAzPoI6x8BAgQIECBAgAABAgQI1J6ATUBqb8y0mECKBcwATPHgaBoBAgQIECBAgAABAgQIECBAgACBrgoIAHZVUH4CBAgQIECAAAECBAgQIECAAAECKRawBDjFg6NpBAgQIECAAAECBAgQIFCnAjYBqdOB120C5REwA7A8rkolQIAAAQIECBAgQIAAAQIECBAgkAoBMwBTMQzVacSCBQvCXXfdFV5//fXQv3//sO2224bddtstNDQ0VKxBvULl6qpYp2q0otebP6jRlmev2bs1r5O9TtVojx4Ni2q05dlr9vrd+2WvUzXco7caltVw67PV9MHd+mSrQzXcm5/N71vDrc9e08/NXpf0iAABAl0SEADsEl86Mz///PPhoosuCg8//HD48MMPw5ZbbhlOP/308KlPfaqlwTfccEM45ZRTwuLFi1uuxZPNNtssXH311WH8+PFtrvtAgAABAgQIECBAgAABAhUUaFpZwcpURYBA1gUsAc7YCN99991h7Nix4dZbbw1z5swJixYtCk8++WT4zGc+E84777ykt3fccUc4/vjjw/vvvx+am5vbvF577bUwYcKEcP/992dMRncIECBAgAABAgQIECBAgAABAvUpIACYoXGfN29eOOqoo8LSpUuToN7IkSPD6NGjQ2NjY/L5W9/6VnjqqafCaaedFpqamsK//uu/hp/85CchBg0nT56cBP4iR8x/3HHHJbMHM8SjKwQIECBAgAABAgQIECBAgACBuhSwBDhDw37dddeFuXPnhu7du4df/OIX4eCDD0569/LLL4c999wzvPPOO+HEE08Mf/rTn0JcAhxnBbY+Jk6cGP793/89nHnmmWHWrFnhpptuCsccc0zrJHmfz549O++0EhIgQIAAAQIECBAgQIDAagJ2AV4NxEcCBLoiYAZgV/RSlvfOO+9MNvD45Cc/2RL8i02MMwHPOuusZBbgM888Ew499NA1gn+5rsRnBY4bNy75ePvtt+cuF/w+bNiwkM+r4IJlIECAAAECBAgQIECAAAECBAgQKEhAALAgrnQnfvHFF5MGHn744Ws09KCDDmq5Fpf+dnbEmYHx2YAxWOggQIAAAQIECBAgQIAAAQIECBCobQFLgGt7/Nq0fsGCBcnnoUOHtrkeP2y88cYt1zbffPOW8/ZOtt122+RyXDJc7BGXEOdz/HTcV/NJJg0BAgQIECBAgAABAgTqS2DVc9sdBAgQKJWAAGCpJFNQTp8+fZJdf7t1W3NiZ7yXO1qf5661fh8wYEDyccmSJa0vF3TeXhCyoAIkJkCAAAECBAgQIECAAAECBAgQKInAmpGikhSrkGoIDBkyJKn2rbfe6lL177//fpJ/0KBBXSpHZgIECBAgQIAAAQIECBAgQIAAgeoLmAFY/TEoWQviMt+4w29Hy28nT56c1LW22XmvvfZakm7DDTcsWdsURIAAAQIECBAgQIAAAQIFCNgFuAAsSQkQWJuAAODahGro/ujRo8PDDz8c/vCHP7Tb6okTJ7Z7ffWLv/vd75JLI0aMWP2WzwQIECBAgAABAgQIECBAgAABAjUmIABYYwPWWXPHjBmT7N57//33d5as03tLly4Nt9xyS2hoaAjjxo3rNG0pbq67shSlKKMUAr0aPRGgFI6lKGN5QylKUUYpBLoHg1EKx1KUsUFj31IUo4wSCRy6pLFEJSmmqwJnN8zuahHyl0hgy16blagkxRAgQIAAgdILCACW3rRqJU6YMCHcc889Sf3Nzc1JEK/Qxvz85z8PcROQ+Np///0LzS49AQIECBAgQIAAAQIECJRCwC7ApVBUBgECfxMQAMzQVyEG7fbee+8u9SguE853qXCXKpKZAAECBAgQIECAAAECBAgQIECgIgLW/FWEWSUECBAgQIAAAQIECBAgQIAAAQIEqiNgBmB13NVKgAABAgQIECBAgAABAgQ6FGhu9sD0DnHcIECgYAEzAAsmk4EAAQIECBAgQIAAAQIECBAgQIBA7QgIANbOWGkpAQIECBAgQIAAAQIECBAgQIAAgYIFLAEumEwGAgQIECBAgAABAgQIECBQZoHmpjJXoHgCBOpJwAzAehptfSVAgAABAgQIECBAgAABAgQIEKg7ATMA627IdZgAAQIECBAgQIAAAQIEUi/QZAZg6sdIAwnUkIAZgDU0WJpKgAABAgQIECBAgAABAgQIECBAoFABAcBCxaQnQIAAAQIECBAgQIAAAQIECBAgUEMClgDX0GBlsan9zWpPzbD27N6YmrbUe0Oean6/3glS0/8Pm1ekpi313pANuvWpd4JU9f/GPstT1Z56bsyA5f7dSMv4X/jWA2lpinasEvh6FhRsApKFUdQHAqkRMAMwNUOhIQQIECBAgAABAgQIECBAgAABAgRKLyAAWHpTJRIgQIAAAQIECBAgQIAAAQIECBBIjYAlwKkZCg0hQIAAAQIECBAgQIAAAQJ/E2haiYIAAQIlEzADsGSUCiJAgAABAgQIECBAgAABAgQIECCQPgEBwPSNiRYRIECAAAECBAgQIECAAAECBAgQKJmAJcAlo1QQAQIECBAgQIAAAQIECBAokYBdgEsEqRgCBKKAGYC+BwQIECBAgAABAgQIECBAgAABAgQyLCAAmOHB1TUCBAgQIECAAAECBAgQIECAAAEClgD7DhAgQIAAAQIECBAgQIAAgbQJNDWlrUXaQ4BADQuYAVjDg6fpBAgQIECAAAECBAgQIECAAAECBNYmIAC4NiH3CRAgQIAAAQIECBAgQIAAAQIECNSwgCXANTx4xTR92rRp4cYbbwzxfc6cOaF79+5h4403Drvttls49thjw0YbbVRMsfIQIECAAAECBAgQIECAQCkF7AJcSk1lEah7gYbmVUfdK2QA4I9//GO47rrrQrdu3cJll12WBPZad2vhwoXhuOOOC7feemvry23Oe/bsGb7xjW+EM888s831cn64cPhnylm8sgsQeC4sKiC1pOUUMDW7nLqFle3JO4V5lTP1m8sXlrN4ZRcoMKLH+gXmkLxcAv/Q0KtcRSuXQE0LfHfmz2q6/bHxHz6Wvj703vnImnfVAQL1KmAGYEZG/qabbgpXXnllGDdu3BrBv2XLloUJEyaExx57LOTivXHm3+DBg8PKlSvD3Llzk+tLly4NZ599dli0aFG48MILMyKjGwQIECBAgAABAgQIECBAgACB+hYw0SQj4z916tTQ0NAQDj300DV69J//+Z/h0UcfTa5vv/324fbbb0+CfG+++Wb461//GhYsWBAmT54chg4dmgQC4yzAZ599do1yXCBAgAABAgQIECBAgACBCgnEXYDT9qpQ11VDgEDpBQQAS29alRJnzpyZ1LvDDjusUf8111yTBAf/+Z//OZkFGGcD9ur19+UiAwYMCBMnTgxPPPFESxDw+9///hrluECAAAECBAgQIECAAAECBAgQIFB7ApYA196YtdviuKFHPAYNGtTm/vz588Mrr7ySBAAvv/zy0Lt37zb3W3/4h3/4h/DVr341nHLKKSHOKOzKMXv27K5kl5cAAQIECBAgQIAAAQIECBAgQKBEAgKAJYKsdjFxFt+7774b3nvvvTZNiddyx5gxY3KnHb7vtNNOyb233nqrwzT53Bg2bFg+ycIFm3w6r3QSESBAgAABAgQIECBAoK4E4vJfBwECBEokYAlwiSCrXczw4cOTJkybNq1NU1rPCPzwww/b3Gvvw5IlS5LLcUdgBwECBAgQIECAAAECBAgQIECAQO0LCADW/hgmPYjP9Ys7/Mbn/cWdfXPHeuutF7bZZpvk429/+9vc5Q7f77zzzuTepptu2mGafG7MmjUr5PPKpyxpCBAgQIAAAQIECBAgQIAAAQIEihcQACzeLlU5P/e5z4V+/folz/s74YQTVm0W9ffp4ueee24SHPzKV74SZsyY0WG743P/rrzyyuR5gYccckiH6fK5EXcUzueVT1nSECBAgAABAgQIECBAoN4EmptXrvo9Ll2vehsD/SWQJQEBwIyMZnzm3qWXXpoE+qZMmRJGjx4dbrrpprB48eLw6U9/Onz5y18Ob775Zoi7BJ9xxhkhzgZ84YUXwrPPPhtuvfXWcMwxx4R99903xGXCG264YfjSl76UERndIECAAAECBAgQIECAAAECBAjUt4BNQDI0/jFoFzcBOf/885PA3pFHHpnM5vvoRz8aBg8enOwAHAOCV111VfJavetxCfHAgQPDzTffHOLSYQcBAgQIECBAgAABAgQIVEmg1aquKrVAtQQIZEjADMAMDWbsynnnnRfuueeesPnmmyezAeNS4D/96U8hbg6ydOnSJCCY63IM+LV+feITnwi///3vwy677JJL4p0AAQIECBAgQIAAAQIECBAgQKDGBcwArPEBbK/5e++9d3j55ZfD3XffHX7xi1+Exx9/PEyfPr3NcwFjviFDhiQbhOy+++7hsMMOC9tuu217xZX12kthcVnLV3j+AoMaeuWfWMqyCnQPDWUtX+H5C7ze9H7+iaUsq8D/9OlX1vIVXpjApJU9CssgddkEnl7xbtnKVnBhAmcsH1RYBqkJECBAgEAFBQQAK4hdyaoaGhrCfvvtl7xivXGmX1weHJcA9+7dOwwYMCD07Nmzkk1SFwECBAgQIECAAAECBAjkK9D8940d880iHQECBDoSEADsSCZj12NAMD7fL74cBAgQIECAAAECBAgQIECAAAEC9SPgGYD1M9Z6SoAAAQIECBAgQIAAAQIECBAgUIcCZgDW4aDrMgECBAgQIECAAAECBAikXMAuwCkfIM0jUFsCZgDW1nhpLQECBAgQIECAAAECBAgQIECAAIGCBAQAC+KSmAABAgQIECBAgAABAgQIECBAgEBtCVgCXFvjpbUECBAgQIAAAQIECBAgUA8CdgGuh1HWRwIVEzADsGLUKiJAgAABAgQIECBAgAABAgQIECBQeQEBwMqbq5EAAQIECBAgQIAAAQIECBAgQIBAxQQsAa4YtYoIECBAgAABAgQIECBAgECeAnYBzhNKMgIE8hEwAzAfJWkIECBAgAABAgQIECBAgAABAgQI1KiAGYA1OnBZafaABl/BtIxlc2hOS1Pqvh0fhqa6N0gLwIhuA9LSlLpvx/dWrqx7gzQBbL+yR5qaU9dtmdWtV133P02d36D7h2lqjrYQIECAAIE2AqIvbTh8IECAAAECBAgQIECAAAECKRCwC3AKBkETCGRHwBLg7IylnhAgQIAAAQIECBAgQIAAAQIECBBYQ0AAcA0SFwgQIECAAAECBAgQIECAAAECBAhkR8AS4OyMpZ4QIECAAAECBAgQIECAQFYE7AKclZHUDwKpEDADMBXDoBEECBAgQIAAAQIECBAgQIAAAQIEyiMgAFgeV6USIECAAAECBAgQIECAAAECBAgQSIWAJcCpGAaNIECAAAECBAgQIECAAAECrQQsAW6F4ZQAga4KmAHYVUH5CRAgQIAAAQIECBAgQIAAAQIECKRYQAAwxYOjaQQIECBAgAABAgQIECBAgAABAgS6KmAJcFcF5SdAgAABAgQIECBAgAABAqUWaG4qdYnKI0CgjgXMAKzjwdd1AgQIECBAgAABAgQIECBAgACB7AuYAZjhMX799dfDww8/HF588cXwxhtvhEWLFoUlS5aEAQMGhPXWWy9ss802YezYsWGXXXYJDQ0NGZbQNQIECBAgQIAAAQIECNSYgE1AamzANJdAugUEANM9PkW17pe//GW47LLLwlNPPZVX/mHDhoUvfOEL4Ywzzgg9e/bMK0+pEv15xcJSFaWcLgr071bZse9iczOdfVzDwEz3r5Y690TwMyot4/X68gVpaYp2rBJY3nMIh5QILFyxNCUt0YwLGldCSJHAnSlqi6YUJhAnsvzHf/xHuOOOO8KsWbNCr169wuabbx7+9V//NZx88smhb9++hRXYSep77703/PjHPw6/+93vwltvvRW6d+8eNtxww7DddtuFvffeOxx99NGhf//+HZbwwQcfhO9///vhF7/4RXjttdfC0qVLQ/z9ev/99w9f/vKXw/DhwzvM6waBSgs0NK86Kl2p+sojEH/4fOpTn0p+UMYaVh/aOMtv9Wu5lsR7I0eODDfddFP42Mc+lrtc9ve9h+5b9jpUkJ+AAGB+TpVIJQBYCeX86hAAzM+pEqkEACuhnH8dWwsA5o9V5pSzVywqcw2Kz1egVzdzK/K1qkS6O9+o/RDgkl9/txJUBdXR56CvFJS+0MS/+c1vwlFHHRUWLmz/j7BbbLFF8vvuiBEjCi26Tfr58+eHY489NvzqV79qc331D08//XQYNWrU6peTzzNmzAgTJkwIr776arv311lnnfCTn/wkHHDAAe3ed5FApQX8V6rS4mWs75Of/GS45557kiDfJptsEvbZZ5/krxfvvfdeeOihh8Lzzz+f/PXke9/7Xthxxx2TH1RxlmCcMRiXCE+fPj2MHz8+TJ06taJBwDKSKJoAAQIECBAgQIAAAQK1KVBnm4DEYNu//du/JY+tirPuzj333OT30/gYq5///OfhmmuuCa+88koyu+7JJ59MHm1VzMDG34/j78q5FXOHHnpoOPzww5NZho2NjcmswwcffDDccsstHRYfH68VZ/nlgn8nnHBCMhmnT58+ye/T3/rWt5IgZuzPI4880mEQscMK3CBQBgEzAMuAWo0ib7zxxnDkkUcmz/I7//zzw//7f/8vdOvWdo+Xn/3sZ+Gzn/1sssw3/rCLfz2JR9OqZ0tce+214fTTT09+2G677bbhD3/4Q4g//Mp9mAFYbuH8yzcDMH+rcqc0A7DcwvmXbwZg/lblTmkGYLmFCyvfDMDCvMqZ2gzAcuoWVrYZgIV5lTt1JmYA/urb5WYquPw+B59dcJ58M+y+++7JM+zjMtw4gWXnnXduk/U73/lOOPvs/6v/ggsuCBdeeGGb+/l+OOaYY8KPfvSjZHJMXAF30EEHtZs1rp5buXJlsix49QTxd+5LLrkkufztb387nHXWWW2SPProo2GPPfYIK1asSN4feOCBNvd9IFANgbYRomq0QJ0lEbjhhhuSco444ogQfxiuHvyLN2OAMAYGFy9eHL761a+21BvTnnjiiSFOt47nL7zwQjJVuSVBESezZ88O+byKKFoWAgQIECBAgAABAgQIEMiQwBNPPJEE/2KXjj/++DWCf/H6mWeeGbbaaqt4Gq666qqwfPny5LyQf8Rn/cXgXzy+8Y1vdBj8i/fjY7JiMHL1I9Ybn1EYj9ie2K7Vj7jRZuxHPOJswmnTpq2exGcCFRcQAKw4eXkqfOaZZ5IfUBMnTuy0gtz9+EDVZcuWtUm71157hU9/+tPJEuL4l5CuHPHBp/m8ulKHvAQIECBAgAABAgQIEMisQNwFOG2vMmHfdtttLSXHZ/O1d8TJKnH2XjwWLFiQLLVtL11n1+KGHfFYd911wymnnNJZ0g7vxUdmxWXE8Yi/X7c3+Sbei6vvcsett96aO/VOoGoCAoBVoy9txXPnzk0K/MhHPtJpwRtttFFyP05F/stf/rJG2vgcwXg899xza9xzgQABAgQIECBAgAABAgQIlFogzsyLR79+/ZLn1XdUflxWmzvis/UKOeIEmNymH/EZgL17906yx2W+cbfhmTNnhg8//HCtRebaGhO2bs/qGUePHt2yY3GhbV29LJ8JlEJAALAUiikoI/4FIx5vvvlmp62JW5vnjp49e+ZOW97j5iHxmDNnTsu1Yk7iD9B8XsWULQ8BAgQIECBAgAABAgQIZEfgpZdeSjoTd/dtb9ltrqdbbrll7jTk8rRcWMvJs88+2xLgi8+9jzsNn3baaWHw4MEh/h686aabJjMDY3Cws2f2vfjiiy01tW5Py8W/ncR+5HYrLrStq5flM4FSCKy5oL0UpSqj4gLbbbddMgU6PgvwE5/4RIf1554VGP+ykpsN2Drx0qVLk49x16WuHEOHDu1KdnkJECBAgAABAgQIECBQ3wIp3AU4Puc9n6OQ3wfjrLvcira15VtvvfWSWYLxufZxwkkhR+vAXdwIM87Qy+3imysnzhK89957w3333RfiTr7nnHNO7lbLe84g/k49cODAluvtncTHYsXVdXGCTfxdu1evXu0lc41ARQTMAKwIc/kriUt34y5F8dl9cTeieL76EXcKvvjii5NnBe67777tPqvgj3/8Y5KtveDg6uX5TIAAAQIECBAgQIAAAQL1I5DPc95jmkKORYsWtSTPZyJKDLzF4/3332/Jl8/JvHnzWpJdfvnlSfDv4x//eIgbkMQg5DvvvBN+8IMfJLMA4+/TcePM3JLhloyrTnLtLaStMX+h7W1dp3MCpRAwA7AUiikoI+4wFHcieuWVV5Lt0CdPnhzi1OUNN9wweUBp3EY9/uUh/iDr0aNHOO+889ptddwcJO52NHbs2Hbvu0iAAAECBAgQIECAAAECBEol0Pq5e+09pmr1enKz6JYsWbL6rU4/x1mDuSPWGX9fvv3220NjY2NyeciQIeELX/hC+NjHPpY82y/OEjz33HOTnYLj78i5I9feQtoa8xba3lx93gmUSkAAsFSSVS4n/hCMf52IP8TilOTXX389XHvttW1aFYN/8TkEkyZNCqNGjWpzL36Izw+Mzzro27dviDMEHQQIECBAgAABAgQIECBQJYG4A3DKjkKX3ebT/NxmHDFtXIK7tiP32Ko+ffqsLWmb+63riTfiLMBc8K91wl133TXEFXY333xz8pzB559/PsRHbuWOXDmFtDXmLbS9ufq8EyiVgABgqSRTUM7IkSPDU089FS666KLwk5/8pGVr8ti0OOtvr732SmYH7rTTTu22Nu4gPH/+/Hbvlevi+o2F/dAuVzuUG8K6DT0wpETg7pVd24QnJd3IRDN2ahyUiX5koRMf7fl/y32y0Jcs9OGIZf/3zOAs9KXW+/ClYCzSMoZnLV8/LU3RDgJlE1jbM/qKqXjAgAEt2fJZJpubyZfPEtyWgledtK4nzvbbYYcdWt9uc77ffvslAcB4cdq0aW0CgLlyCmlrLKfQ9sY8DgKlFPAMwFJqpqCs+IPs+9//fnj33XeTv1bELcqfeeaZJLD329/+NnQU/EtB0zWBAAECBAgQIECAAAECBOpMIM6oGzTo//7omttgoyOCOGElFwAs9FmDrdOvLZDZOm3cwKP1kcsb27FgwYLWt9Y4z82YjL+n55Yur5HIBQIVEhAArBB0pavp1q1biDMCd9lll+SvFXFZr4MAAQIECBAgQIAAAQIEakQgLgFO26tMdFtvvXVS8owZM8KKFSs6rGX69Okt97baaquW83xOttlmm5ZkK1eubDlv76T1/fgYrdZHrq3xWuv2tE4Tz2M/XnvtteRyoW1dvSyfCZRCQACwFIrKIECAAAECBAgQIECAAAECBIoSiM/di0ecVRcfa9XR8eCDD7bcGjduXMt5PifDhw8Pm2yySZJ05syZyQaZHeXLBe7i/Y033rhNslxb48XW7WmTaNWHJ598smW2YqFtXb0snwmUQkAAsBSKyiBAgAABAgQIECBAgAABAgSKEjjkkENa8k2ePLnlvPVJ3JX3hhtuSC4NHDgwjB8/vvXtvM4PO+ywJN3ChQvDfffd12GeX/7yly33Wgf84sU999wzrLvuusn9KVOmdBhIvP7665M08R+HHnpoy7kTAtUSEACslrx6CRAgQIAAAQIECBAgQIBARwLNzWFVdCldr47a2sXrY8eODbvttltSynXXXRcee+yxNUq84oorkufcxxunnnpqstFl60QPPPBAaGhoSF6f/exnW99qOT/ttNNCbhffM844I8RA4OrHj3/84xDLisf+++8fWj8PMF7r2bNn+PKXvxxPk/Z897vfTc5b/yO2P/YjHnvssUcYM2ZM69vOCVRFQACwKuwqJUCAAAECBAgQIECAAAECBHICV111VejTp0/y7Lx99903fOtb3wqPP/54mDp1avj85z8fzj777CTpFltsEc4888xctoLe4xLgiy++OMnz/PPPhxh4jDMO47LjWM+XvvSlkAserrPOOuHf//3f2y3/rLPOCrEd8Yjtiu2L+WN7Y7tj++MzAGN/rrzyynbLcJFApQXaPs2y0rWrjwABAgQIECBAgAABAgQIEKh7gR122CHceOON4aijjkpm5n3ta19bwyQG3e64444wYMCANe7leyEG7+bNmxcuv/zy8PLLL4fjjjtujawbbLBBuO2228I//uM/rnEvXoj1x3ZMmDAhvPrqq+Hqq69OXq0TxwDiT37ykzBq1KjWl50TqJqAGYBVo1cxAQIECBAgQIAAAQIECBDoQCBtOwDH9pT5OPDAA8Nzzz0XTj/99GSGXd++fUN83t/o0aOTgN3TTz8dRowY0eVWxFl6jzzySDj66KPDRz/60dCrV6/kuX5xqe4ll1wSXnnllbDzzjt3Wk9sR2xPDCTG9sV2xvaOHDkyaX/sxwEHHNBpGW4SqKRAQ/Oqo5IVqotAa4Ejhh/c+qPzKgqs29CjirWrurXAzJWLWn90XkWBnRoHVbF2VbcWWBr870prj2qfH7FsabWboP6/CXwpvMsiJQIXr9woJS3RjCjw8b/+vOYhlvzsgtT1oc+RF6WuTRpEgEB+AmYA5uckFQECBAgQIECAAAECBAgQIECAAIGaFPAMwJocNo0mQIAAAQIECBAgQIAAgUwLVGDJbab9dI4AgTYCAoBtOHyotMDAhp6VrlJ9HQi8btlpBzKVv9wjmJxdefX2a/xz85L2b7hacYFeDf69qDh6JxVe0t0S4E54KnprQFOvitanso4FftWz/M9H67h2d1YX+PjqF3wmQIBAnQsIANb5F0D3CRAgQIAAAQIECBAgQCCFAs2CyikcFU0iULMC/pxes0On4QQIECBAgAABAgQIECBAgAABAgTWLiAAuHYjKQgQIECAAAECBAgQIECAAAECBAjUrIAlwDU7dBpOgAABAgQIECBAgAABApkVsAlIZodWxwhUQ8AMwGqoq5MAAQIECBAgQIAAAQIECBAgQIBAhQQEACsErRoCBAgQIECAAAECBAgQIECAAAEC1RCwBLga6uokQIAAAQIECBAgQIAAAQKdCTQ3d3bXPQIECBQkYAZgQVwSEyBAgAABAgQIECBAgAABAgQIEKgtAQHA2hovrSVAgAABAgQIECBAgAABAgQIECBQkIAlwAVxSUyAAAECBAgQIECAAAECBCogYBfgCiCrgkD9CJgBWD9jracECBAgQIAAAQIECBAgQIAAAQJ1KCAAWIeDrssECBAgQIAAAQIECBAgQIAAAQL1I2AJcP2MdSp7+sbK91PZrnps1Abd+tZjt1PZ5yENPVPZrnpsVI/QUI/dTmWfn1k5P5XtqtdGjWpcr167nrp+v96wJHVtqtcG3bloer12Xb/LJWAJcLlklUugLgUEADM+7M8880x47bXXQmNjY9hqq63CyJEj8+rxnDlzwg9+8IMk7fnnn59XHokIECBAgAABAgQIECBAgAABAgTSJyAAmL4xKUmL/vd//zd86UtfSoJ/rQvcbrvtwje/+c0wYcKE1pfXOH/nnXfChRdeGBoaGoIA4Bo8LhAgQIAAAQIECBAgQIAAAQIEakbAMwBrZqjyb+hNN90UDjzwwCT419zcHFq/nnvuueTe8ccfH5YssWQkf1UpCRAgQIAAAQIECBAgUEGB5qYQ0vaqYPdVRYBAaQXMACytZ9VL++tf/xpOPPHEsGLFimT23mGHHRbGjx8fli5dGh588MFwxx13hJUrV4brr78+vPjii8nn9ddfv+Ttnj17dsnLVCABAgQIECBAgAABAgQIECBAgEDhAgKAhZulOsekSZPCwoULk2f+3XLLLeGggw5qae/pp58enn322fC5z30uPPXUU+GJJ54Iu+++e7jnnnvCRhtt1JKuFCfDhg3Lq5h9h348r3QSESBAgAABAgQIECBAgAABAgQIFCdgCXBxbqnNFZ/9F5/bd8IJJ7QJ/uUavP3224dHHnkkxCXAcWnwSy+9FHbdddcwc+bMXBLvBAgQIECAAAECBAgQIFBlgeamVY9zStmryiSqJ0CgCwJmAHYBL41ZX3nllaRZn/zkJztsXs+ePcM111wTNttss/D1r389Cf7ttttuyUzALbfcssN8hdyYNWtWXsmP3/mEvNJJRIAAAQIECBAgQIAAAQIECBAgUJyAAGBxbqnNFZf/xmPIkCFrbeO5554b4vP/Tj755PDmm28my4HvvvvuMGrUqLXmXVuCoUOHri2J+wQIECBAgAABAgQIECBAgAABAhUQsAS4AsiVrKJ///5Jde+++25e1X7+858PP/rRj5JnBs6dOzfstdde4fHHH88rr0QECBAgQIAAAQIECBAgUCaBplW7AKftVaauKpYAgfILCACW37iiNYwYMSKp78knn8y73iOPPDLcfPPNoVevXmHBggVh3333Dffdd1/e+SUkQIAAAQIECBAgQIAAAQIECBBIr4AAYHrHpqiWjR49Otnc4/bbby8of9wtOObp169fWLx4cYg7BjsIECBAgAABAgQIECBAgAABAgRqX8AzAGt/DNv0YJ999gmTJk1Kdvp9+eWXw8iRI9vc7+zD3nvvHeIuwgcccEB47733OktasnvbNg4sWVkK6prArOYlXStA7pIJvN38YcnKUlDXBIY09OpaAXKXTOB7PfwvS8kwS1DQhSsXl6AURZRCYGBDz1IUo4wSCJzXZ7sSlKIIAq0EmlctAXYQIECgRAJmAJYIMi3FfPzjH09m8TU3N4cLL7yw4Gbtsssu4f77789rE5GCC5eBAAECBAgQIECAAAECBAgQIECg4gL+nF5x8vJW2KdPn/Doo4+GuBtwt27FxXfjLsCxjIcffri8jVU6AQIECBAgQIAAAQIECLQv0NTc/nVXCRAgUISAAGARaGnPsu2223a5iZtttlmILwcBAgQIECBAgAABAgQIECBAgEBtCxQ3Ray2+6z1BAgQIECAAAECBAgQIECAAAECBOpGwAzAuhlqHSVAgAABAgQIECBAgACBmhFosglIzYyVhhKoAQEzAGtgkDSRAAECBAgQIECAAAECBAgQIECAQLECAoDFymU835QpU0JjY2Po3t0k0YwPte4RIECAAAECBAgQIECAAAECGRcQ3cn4AHele83Ndp3qip+8BAgQIECAAAECBAgQKFrAEuCi6WQkQGBNATMA1zRxhQABAgQIECBAgAABAgQIECBAgEBmBAQAMzOUOkKAAAECBAgQIECAAAECBAgQIEBgTQFLgNc0qekrxx13XEnaP2PGjJKUoxACBAgQIECAAAECBAgQKELAI5mKQJOFAIGOBAQAO5Kp0evXX399aGhoqJnWb9zUWDNtzXpD3+1mLNIyxvOalqalKXXfjj83r6h7g7QA/Hfom5amaMcqgY0bLCJJyxfh98veTktT6r4dj6/8sO4N0gRwQpoaoy0ECBBIgYAAYAoGoRxNsIFHOVSVSYAAAQIECBAgQIAAAQIECBCoPQEBwNobs05bPGjQoDBv3ryw3377hUmTJnWatrObN998czjrrLM6S+IeAQIECBAgQIAAAQIECJRLwC7A5ZJVLoG6FBAAzNiwjxkzJtx1111h+vTpYfjw4UX3bvDgwUXnlZEAAQIECBAgQIAAAQIECBAgQCA9Ah7gkp6xKElLYgAwHm+88UaYM2dOScpUCAECBAgQIECAAAECBAgQIECAQO0KCADW7ti12/KxY8e2XJ82bVrLuRMCBAgQIECAAAECBAgQqCGBpuYQ0vaqIT5NJUCgrYAlwG09av5TLgAYNwGJAcAJEyYU1acRI0aEiRMnFpVXJgIECBAgQIAAAQIECBAgQIAAgfQICACmZyxK0pIhQ4aEphI8LHbcuHEhvhwECBAgQIAAAQIECBAgQIAAAQK1LSAAWNvjp/UECBAgQIAAAQIECBAgkEWB5qYs9kqfCBCokoBnAFYJXrUECBAgQIAAAQIECBAgQIAAAQIEKiEgAFgJZXUQIECAAAECBAgQIECAAAECBAgQqJKAJcBVglctAQIECBAgQIAAAQIECBDoUCDuAOwgQIBAiQTMACwRpGIIECBAgAABAgQIECBAgAABAgQIpFHADMA0jkodtenVbsvqqLfp7mrv0JjuBtZR6zbo1qeOepvurm4YeqS7gXXUui2W+5tlmob78R7++52W8RjcvV9amlL37XhvxQd1bwCgtALNTTYBKa2o0gjUt4AAYJ2O/zvvvBOuu+668OCDD4a//OUvicJHPvKRsMcee4Rjjz02bLTRRnUqo9sECBAgQIAAAQIECBAgQIAAgWwJCABmazzDDTfckPTokEMOCeuss067vbv22mvD6aefHj74oO1fKV988cVw7733hksvvTRcccUV4fOf/3y7+V0kQIAAAQIECBAgQIAAAQIECBCoHQEBwNoZq7xa+tnPfjY0NDSE0aNHh6233nqNPFdffXX44he/mFxvbm5O0g4aNCh5nzt3bojXYmDwpJNOCt27dw/HH3/8GmW4QIAAAQIECBAgQIAAAQJlFrAJSJmBFU+gvgQEAOtovN98881w2mmnJUG+Hj16hPPPPz+ceOKJYciQIYlCDAD+8Ic/DJdccklYtmxZkvbggw8OgwcPLlhp9uzZBeeRgQABAgQIECBAgAABAgQIECBAoPQCAoClN01tiT/4wQ/Chx9+GLp16xZuvfXWMGHChDZtjYG+r3/962H77bcPMfAXZwLG5cJf/epX26TL58OwYcPySRa+OPyIvNJJRIAAAQIECBAgQIAAAQIECBAgUJyALfWKc6vJXPH5fnF58OGHH75G8K91hw444IBw2GGHJTMF77vvvta3nBMgQIAAAQIECBAgQIBAJQSaV+0CnLZXJfqtDgIEyiJgBmBZWNNZ6IwZM5KGxeDe2o4jjjgi3HzzzeGPf/zj2pK2e3/WrFntXl/94qW7nrH6JZ8JECBAgAABAgQIECBAgAABAgRKKCAAWELMtBe1cOHCpImbbbbZWpuaSzN//vy1pm0vwdChQ9u77BoBAgQIECBAgAABAgQIECBAgECFBQQAKwxezeriM/7efvvtgpoQdwJ2ECBAgAABAgQIECBAgECFBewCXGFw1RHItoDoTkbH96233gr9+/dv07uRI0cmAcC4PHfHHXdsc2/1D++8805yqZgdgFcvy2cCBAgQIECAAAECBAgQIECAAIHqCQgAVs++rDXvu+++HZb/+OOPh0MOOaTD+/HGs88+m9zfaKONOk3nJgECBAgQIFAAyp8AAEAASURBVECAAAECBAgQIECAQLoFBADTPT5Fta65ubnTfLfccku47LLLOk3zm9/8JtkxePTo0Z2m6+rNZWHVzlaOVAhs3tw7Fe3QiBAWNvj3Ii3fg4FN3dLSlLpvx02NxT2Ttu7hygTwmWUDy1SyYgsVuKzJvxuFmpUr/Y59PAO7XLZ1W26T/yes27HXcQJlEBAALANqNYucPHlyXtUvWrQoDBgwoN20zzzzTHjssceSAODOO+/cbhoXCRAgQIAAAQIECBAgQIAAAQIEakNAALA2xinvVk6cODHvtB0lXG+99cLUqVOT22t7VmBHZbhOgAABAgQIECBAgAABAgQIECCQDgEBwHSMQ6paMXz48BBfDgIECBAgQIAAAQIECBCokoBdgKsEr1oC2RTwcKNsjqteESBAgAABAgQIECBAgAABAgQIEEgEBAB9EQgQIECAAAECBAgQIECAAAECBAhkWMAS4AwPrq4RIECAAAECBAgQIECAQI0KNNsFuEZHTrMJpFLADMBUDkv1GzVlypTQ2NgYuncXI67+aGgBAQIECBAgQIAAAQIECBAgQKB4AdGd4u0yn7O5uTnzfdRBAgQIECBAgAABAgQIECBAgEDWBQQAsz7C+keAAAECBAgQIECAAAECtSdgF+DaGzMtJpBiAUuAUzw4mkaAAAECBAgQIECAAAECBAgQIECgqwJmAHZVMGX5jzvuuJK0aMaMGSUpRyEECBAgQIAAAQIECBAgQIAAAQLVFRAArK5/yWu//vrrQ0NDQ8nLLVeB2zb1LlfRyi1Q4MVuSwvMIXm5BAYGP5rLZVtoufeHeYVmkb5MAt1C7fy3rUwEqSr2+kb/bqRlQAZ365+WptR9O6Yvm1P3BgBKK9DcZBfg0ooqjUB9C/gtM6PjbwOPjA6sbhEgQIAAAQIECBAgQIAAAQIEChQQACwQLO3JBw0aFObNmxf222+/MGnSpKKbe/PNN4ezzjqr6PwyEiBAgAABAgQIECBAgEAXBGwC0gU8WQkQWF1AAHB1kRr/PGbMmHDXXXeF6dOnh+HDhxfdm8GDBxedV0YCBAgQIECAAAECBAgQIECAAIH0CNgFOD1jUZKWxABgPN54440wZ47nkJQEVSEECBAgQIAAAQIECBAgQIAAgRoWEACs4cFrr+ljx45tuTxt2rSWcycECBAgQIAAAQIECBAgUEMCcQlw2l41xKepBAi0FbAEuK1HzX/KBQDjJiAxADhhwoSi+jRixIgwceLEovLKRIAAAQIECBAgQIAAAQIECBAgkB4BAcD0jEVJWjJkyJDQVILt4seNGxfiy0GAAAECBAgQIECAAAECBAgQIFDbAgKAtT1+Wk+AAAECBAgQIECAAAECWRRobspir/SJAIEqCXgGYJXgVUuAAAECBAgQIECAAAECBAgQIECgEgICgJVQVgcBAgQIECBAgAABAgQIECBAgACBKglYAlwleNUSIECAAAECBAgQIECAAIEOBeIOwA4CBAiUSEAAsESQiilO4K7md4vLKFfJBfZfOajkZSqwOIH53fzPXnFypc/VKzSWvlAlEsiAwPaN62agF9nowi8/mJGNjmSgFwN79MtAL3SBAAECBLIqIACY1ZEtoF/PP/98mD59eujevXvYdNNNw6hRowrILSkBAgQIECBAgAABAgQIECBAgECaBQQA0zw6XWjbzJkzw/e+973w2GOPhWXLloWRI0eGL37xi2H8+PEtpb700kth4sSJ4amnnmq5Fk823njjcPnll4cjjzyyzXUfCBAgQIAAAQIECBAgQKAyAs2WAFcGWi0E6kRAADCDA33vvfeGQw45JCxZsqSldy+88EK45ZZbwnnnnRcuuuii8Oabb4Z99tknvPXWW6G5ue1yw9mzZ4ejjjoqvP322+H0009vKcMJAQIECBAgQIAAAQIECBAgQIBA7QkIANbemHXa4jlz5oRPfepT4YMPPmhJN3jw4DB37tzk8ze+8Y2wxx57hGuvvTYJAvbu3TsJ9u24445h+fLl4cEHHwy33npraGpqCueee2448MADw4gRI1rKckKAAAECBAgQIECAAAECBAgQIFBbAgKAtTVea23tpEmTwrx580JDQ0Mye+/8888P66yzTli0aFG4+OKLwxVXXBEuvfTS8Oijj4ZBgwYlAb+tt966pdxTTjkl/Pa3vw0HH3xwEhD84Q9/GL7zne+03M/3JM4idBAgQIAAAQIECBAgQIBAkQKWABcJJxsBAu0JCAC2p1LD1+66664k+Bdn7n33u99t6cmAAQOSQN5rr70WbrvttiRNfEZg6+BfLvEnPvGJ8LnPfS7EYGKcEVjMMWzYsLyyfXzox/NKJxEBAgQIECBAgAABAgQIECBAgEBxAt2KyyZXWgXibr7xiJt7tHccffTRLZcPOuiglvPVT+IzBOMxY8aM1W/5TIAAAQIECBAgQIAAAQIECBAgUEMCZgDW0GDl09S41Dcem2yySbvJW1/fcMMN200TL+buvf/++x2m6ezGrFmzOrvdcu+EnU9oOXdCgAABAgQIECBAgAABAn8TWPVcdgcBAgRKJSAAWCrJlJTTr1+/sHDhwmSDj7ixx+pH3PU3d8RnBQ4ZMiT3sc37/Pnzk899+/Ztcz3fD0OHDs03qXQECBAgQIAAAQIECBAgQIAAAQJlFLAEuIy41Sh68803T6r99a9/3W71ra/fe++97aaJF++5557k3vDhwztM4wYBAgQIECBAgAABAgQIECBAgED6BQQA0z9GBbXwX/7lX0Jzc3O4/vrrQ9zBt/URP0+ePDnssMMOobGxMXz9618Pc+bMaZ0kOX/hhRfCf/7nfyYbhey6665r3HeBAAECBAgQIECAAAECBMosEHcBTturzF1WPAEC5RMQACyfbVVK/uIXvxj69OkTmlY9L+Kkk04KG2ywQdhpp52S9/g5Xv/a174W4k6/M2fODKNGjQqXXXZZuPvuu8Odd94Zzj333BCDfvHZfw0NDcluwFXpiEoJECBAgAABAgQIECBAgAABAgRKIuAZgCVhTE8hccnupEmTwrHHHpsE++bOnRvefffdZFZgbGXcHfiwww4LW2+9dXjooYfC22+/ncwEbN2DOIMwHqeeemoyW7D1vVKf9+nWo9RFKq9IgQfCwiJzylZqgfUaepW6SOUVKdD0t5+HRWaXrYQCBzQNLGFpiuqqwK/C/z0ruKvlyN91gWG91u96IUooicCYRmNREkiFECBAgEBZBMwALAtrdQs9+uijw4MPPhj233//sN5664W4kcc//dM/hauvvjpcd911SeO22mqrEJ8HuNFGGyXBwRj0y71i+osvvjhcccUV1e2I2gkQIECAAAECBAgQIFCvAmlb/hvb4yBAoGYFzACs2aHrvOHjxo0Lv/nNbzpNtNtuu4U//elP4a677govvfRS6N69e9h0003D3nvvHdZdd91O87pJgAABAgQIECBAgAABAgQIECBQGwICgLUxTmVrZc+ePcNBBx2UvMpWiYIJECBAgAABAgQIECBAoCCB3KOZCsokMQECBDoQsAS4AxiXCRAgQIAAAQIECBAgQIAAAQIECGRBQAAwC6OoDwQIECBAgAABAgQIECBAgAABAgQ6ELAEuAMYlwkQIECAAAECBAgQIECAQNUEbLpRNXoVE8iigBmAWRzVEvRpypQpobGxMdkYpATFKYIAAQIECBAgQIAAAQIECBAgQKBKAmYAVgm+Fqr10NlaGCVtJECAAAECBAgQIECAAAECBAh0LiAA2LmPuwQIECBAgAABAgQIECBAoPIClgBX3lyNBDIsYAlwhgdX1wgQIECAAAECBAgQIECAAAECBAiYAZix78Bxxx1Xkh7NmDGjJOUohAABAgQIECBAgAABAgQIECBAoLoCAoDV9S957ddff31oaGgoebnlKnBlc1O5ilZugQKDuvUpMIfk5RLoGxrLVbRyCxQY3q1fgTkkL5fA693896JctsWU26+5RzHZ5CmDwBlL/ZwqA2tRRZ7U/GpR+WQqj8A3y1NsRUtttgS4ot4qI5B1AQHAjI6wDTwyOrC6RYAAAQIECBAgQIAAAQIECBAoUEAAsECwtCcfNGhQmDdvXthvv/3CpEmTim7uzTffHM4666yi88tIgAABAgQIECBAgAABAgQIECCQDgEBwHSMQ8laMWbMmHDXXXeF6dOnh+HDhxdd7uDBg4vOKyMBAgQIECBAgAABAgQIdFHAEuAuAspOgEBrAbsAt9bIwHkMAMbjjTfeCHPmzMlAj3SBAAECBAgQIECAAAECBAgQIECgKwICgF3RS2HesWPHtrRq2rRpLedOCBAgQIAAAQIECBAgQIAAAQIE6lPAEuCMjXsuABg3AYkBwAkTJhTVwxEjRoSJEycWlVcmAgQIECBAgAABAgQIEOiiQFMX88tOgACBVgICgK0wsnA6ZMiQ0NTU9f9SjBs3LsSXgwABAgQIECBAgAABAgQIECBAoLYFLAGu7fHTegIECBAgQIAAAQIECBAgQIAAAQKdCpgB2CmPmwQIECBAgAABAgQIECBAoPICzXYBrjy6GglkWMAMwAwPrq4RIECAAAECBAgQIECAAAECBAgQEAD0HSBAgAABAgQIECBAgAABAgQIECCQYQFLgDM8uLXQtZ4NjbXQzLpo459WvFcX/ayFTm7Xff1aaGZdtPG5FfProp+10MmJzRvUQjPrpo2LevSum76mvaOX9VyQ9ibWTft2bRxeN33V0QoJWAJcIWjVEKgPATMA62OcC+rlI488Eo477rhw/PHHF5RPYgIECBAgQIAAAQIECBAgQIAAgfQJCACmb0yq3qIZM2aE66+/PnlVvTEaQIAAAQIECBAgQIAAAQIECBAg0CUBS4C7xCczAQIECBAgQIAAAQIECBAog0BTGcpUJAECdStgBmDdDr2OEyBAgAABAgQIECBAgAABAgQI1IOAGYAZGuWHHnqoJL2ZPn16l8uZPXt2l8tQAAECBAgQIECAAAECBOpVoNkmIPU69PpNoCwCAoBlYa1OoXvuuWdoaGioTuWr1Tps2LDVrrT/8fBNDmr/hqsECBAgQIAAAQIECBAgQIAAAQIlERAALAljugppbm5OV4O0hgABAgQIECBAgAABAgQIECBAoGoCAoBVoy99xT179gzLly8P2223XTj00EOLruCZZ54Jv/rVr4rOHzPOmjUrr/ynjzs5r3QSESBAgAABAgQIECBAoK4EbAJSV8OtswTKLSAAWG7hCpYfA39PPvlk6NGjR7jggguKrnnKlCldDgAOHTq06PplJECAAAECBAgQIECAAAECBAgQKJ2AXYBLZ1n1ksaMGZO04fnnnw/Lli2rens0gAABAgQIECBAgAABAgQIECBAoPoCAoDVH4OStWDs2LFJWXEZcFzG6yBAgAABAgQIECBAgACB2hSIuwCn7VWbklpNgEAUEADM0PcgFwCMXZo2bVqGeqYrBAgQIECAAAECBAgQIECAAAECxQp4BmCxcinMt+WWW4brr78+xF2At95666JbOHHixBBflTiGN/StRDXqyEOgV2NjHqkkqYTAus3GohLO+dRxQLcN8kkmTQUEXurmSegVYM67ijnNS/NOK2F5BXbtNqi8FSg9b4FZwSN48saSkAABAgQqLmAGYMXJy1dhQ0NDOOaYY5LgXe55gOWrTckECBAgQIAAAQIECBAgUDaB+LevtL3K1tm/F/z666+HM888M8QJLv369Qvrr79+iL/ffuc73wkffPDB3xOW8CyWu9lmm4X4O3V8ffSjH+209D333LMlbS5PR++dFuQmgQoKmAFYQWxVESBAgAABAgQIECBAgAABAu0L/OY3vwlHHXVUWLhwYUuCGJx78sknk9e1114b7rjjjjBixIiW+6U4Of/888Of//znUhSlDAKpFRAATO3QaBgBAgQIECBAgAABAgQIEKgPgaeffjr827/9W1iyZEno379/OPfcc8P48eOTzz//+c/DNddcE1555ZWw//77J8HAAQMGlAQm1nvllVeG3r17hx49eoRFixblXe7o0aPD5MmT804vIYFqCggAVlNf3QQIECBAgAABAgQIECBAoB2B5jp7/O2pp56aBPu6d+8e7r777rDzzju3qOy1117hH//xH8PZZ5+dBAGvuOKKcOGFF7bcL/Zk5cqV4YQTTgjx/YILLgjXXXddQQHAuET5Yx/7WLHVy0egogKeAVhRbpURIECAAAECBAgQIECAAAECrQWeeOKJ8PDDDyeXjj/++DbBv1y6+FzArbbaKvl41VVXheXLl+duFf0ey3nqqafCyJEjwznnnFN0OTISqAUBAcBaGKUi2vi73/0unHbaaeHAAw8MhxxySDJ9Ok5tzud49dVXkwegbr755vkkl4YAAQIECBAgQIAAAQIECBQtcNttt7XkPfbYY1vOW59069Yt2fQyXluwYEGYOnVq69sFn8fNRuKz/+IxadKk0LNnz4LLkIFALQlYAlxLo5VHW+NfQeIPzJ/97GdtUseHqX77298Ohx12WPjv//7vMHjw4Db3W39YtmxZmDlzZrKrUevrzgkQIECAAAECBAgQIECgQgJ1tAQ4TmCJR1xSu+OOO3YIvMcee7Tce+SRR8K+++7b8rnQk5NOOiksXrw4HH300SHu6usgkHUBMwAzNsLx+QU//elPQ3Nzc7uvW265JWy33XYh/rB0ECBAgAABAgQIECBAgACBagu89NJLSRPi7r7xGYAdHVtuuWXLrVyelgsFnMRNRe68886w3nrrhfg8wWKP6dOnh5122ikMHDgw2URk6NCh4eCDDw433HBDSZYoF9su+Qi0JyAA2J5KjV6LQb34g6ahoSFsscUWIc76e//998O7774bYuBv7NixSVDw7bffTv5Scvvtt9doTzWbAAECBAgQIECAAAECBCotMHv27JDPq5B2ffjhh2Hu3LlJlhhA6+yIAbs4SzAes2bN6ixph/fmz5+fPC4rJrjsssvCkCFDOky7tht//etfQ3x+4XvvvReWLl0a/vKXv4Rf//rXYeLEiWHUqFGhK0HKtdXtPoFCBToOrRdakvRVF4g7FsVj4403Do8++mhYf/31k899+/YNhx56aPIswO9973vJ8wDj1uqf/OQnky3LP/OZzyTp/IMAAQIECBAgQIAAAQIE0iGQxl2Ahw0blhdOXJGW77Fo0aKWpP3792857+gkBgDj0t042aWY46yzzgoxcBd3GY4r6Io54vMI99577zBhwoSw/fbbh0GDBiW7B//hD38IP/zhD5PA34svvhjGjx+fBAg32WSTYqqRh0BJBQQAS8pZ3cLiDMA4+y/ujpQL/rVuUe7e6NGjk+Bf/MtH/MtE/IH7hS98oXVS5wQIECBAgAABAgQIECBAoOwCcQZg7shnI45evXolyeOklkKPhx56KPzP//xPssw4bvwRf0cu5vjlL3+ZLPtdPe9uu+0W4rMFY2BxypQpSaAxbs4Z0zsIVFtAALDaI1DC+t98882ktPiXjM6O+ODU+INvv/32CzHPySefnPz15Ctf+Upn2cpyb71mq9DLAltEoR9t6ltELlnKIfDHxmXlKFaZRQj8uWFlEblkKYdA39BYjmKVWaTAPiv+b/lVkdllK6HAIz3+/otzCYtVVBECuy/vXUQuWQjUlkCxy24762Xv3n//dyduSLm2Iy61jUefPn3WlrTN/ZjvxBNPTB6LdeqppybPxm+ToIAP8Zl/HR09evQI1157bXj88cfDyy+/HG699dZkaXBcqecgUE0B0Zdq6pe47rgDcDwaG9f+S9I222wTHn744bDpppsmPwDPOeeccMEFF5S4RYojQIAAAQIECBAgQIAAgaIE4i7AKXvFZ/Tl8yqkvwMGDGhJns+y3rj8Nx75LBduKXjVyTe/+c0kIBeXMV900UWtb5X8PG5kcvzxx7eU++CDD7acOyFQLQEzAKslX4Z648NL44y+N954o9Ot03NVx+BfDALus88+yTMKvvGNbyQzAY877rhcEu8ECBAgQIAAAQIECBAgQKBsAnEGYHyGXty8Mm4w0tkRH2OVCwDm+zzCXHmXX355cvov//IvyYaZueut33Nlx/e4U3A8Nthgg7DXXnu1TpbX+dZbb92SLm4O4iBQbQEBwGqPQAnrjz9gYgAwBvXiph/5HB/5yEeS5cD77rtvePrpp8OVV14ZnnvuuXyySkOAAAECBAgQIECAAAECBLosEH+Xjb/HzpgxI6xYsSJ5Rl97hU6fPr3l8lZbbdVyns9Jbnnx5MmTk80wO8sTdyU+8sgjkyTxEVrFBACLfb5gZ+1yj0BXBCwB7opeyvLuuuuuyXLeX/ziF8l7vs2Lf22ZOnVq2GWXXZJ8999/f75ZpSNAgAABAgQIECBAgACBMgjEXYDT9ipDN5Mi4++y8Ygz75566qnkvL1/tF5KO27cuPaSpOZa3AU4d8SJNw4C1RYQAKz2CJSw/rgFeTziLMBCdxlaZ511wj333JMsBy5ky/YSNl9RBAgQIECAAAECBAgQIFCHAoccckhLr+MMvfaOpqamcMMNNyS34iYc48ePby9Zh9fi77lrew0fPjzJH99zaR944IEOy+zoRpzFGHcbzh2777577tQ7gaoJCABWjb70Fe+4444h/uVks802S7YcL7SGuIvS7bffnvfy4ULLl54AAQIECBAgQIAAAQIE8hNI2+y/2J5yHWPHjg277bZbUvx1110XHnvssTWquuKKK5Jn18cbcRffuNtu6yMG6uKy2/j67Gc/2/pWSc/j6rkFCxZ0WGbcnPNzn/tcS1sPPPDAUOjzCjss3A0CXRDwDMAu4KUx60MPPdSlZsUforfcckuXypCZAAECBAgQIECAAAECBAgUInDVVVeFuKx3yZIlIT6j/mtf+1oyyy9+jhtyXH311UlxW2yxRTjzzDMLKbqkaadMmRIOOuig5LXnnnuGkSNHhriiLu5gHJcvx3bmlv/GDURivxwE0iAgAJiGUdAGAgQIECBAgAABAgQIECBQxwI77LBDuPHGG8NRRx0VFi5cmAQAV+eIwb877rgjDBgwYPVbFf0cg30//elPk1dHFW+77bZJ4HLTTTftKInrBCoqIABYUW6VESBAgAABAgQIECBAgACBtQuUc8nt2muvToq4XPa5555LZs3FQN/s2bNDz549w4gRI8IRRxwRTjnllNC3b9/qNO5vtZ5zzjlh1KhRyTLlONNvzpw5Yd68eaFXr15hww03DKNHjw6HH3548mitxsbGqrZV5QRaCzSserBlc+sLzglUUuCbwz9TyerU1YnAuk0Nndx1q5ICf2xcVsnq1NWJwAdhZSd33aqkQN/gf6Ar6b22usYu77m2JO5XSOCRHh9WqCbVrE1g9+W915bE/QoKHPOXH1ewtvJU9dfxe5Sn4C6UuuHUB7uQW1YCBKopYBOQauqrmwABAgQIECBAgAABAgQIECBAgECZBSwBLjNw2ouPOxhdcsklyU5J9913X8WbO7VpbsXrVGH7Ahs39m//hqsVF9h5ZZ+K16nC9gUOHTGr/RuuVlzg0j9vVPE6VdixwJSGtzu+6U5FBT6z4h8qWp/KOhaYb6JyxzjuFCfQbIVOcXByESDQnoAAYHsqdXTtnXfeCbnt0uuo27pKgAABAgQIECBAgAABAgQIEKgbAUuA62aodZQAAQIECBAgQIAAAQIECBAgQKAeBcwAzNCob7bZZgX3ZvHixS15Vs/f0NAQXnvttZb7TggQIECAAAECBAgQIECgMgL1uAtwZWTVQqA+BQQAMzTuM2fOTJ7lV8zGzjFPzN/6iAFABwECBAgQIECAAAECBAgQIECAQG0LCADW9vi12/p11lknrLfeeu3eW/1inAE4d+7cJHC4ySabrH676M+zZ88uOq+MBAgQIECAAAECBAgQIECAAIH/z96dwEdV3Qsc/08WIBBQZIlKohKQRYuCBBQhAiLUBgVBpVDRyF7QVhBxpQrWVlEpYtVSFEFaNIIRCwQRFIWwVARFdpcg+w4GEgiBZOblHDvzJslkMjOZ5d6Z332feblz7lm/Byn8OeceBPwnQADQf5Yhr+mKK67Qq/jOnz8vQ4cOlccee0xiYtxP8fvvvy8DBgzQff/pp5/8NoakpCSP6uqW2MOjfGRCAAEEEEAAAQQQQAABBCJJwGZlR1YkzTdjRSDQAhwCEmjhINa/detWefjhh+XcuXPy9NNPy7XXXivZ2dlB7AFNIYAAAggggAACCCCAAAIIIIAAAggYTYAAoNFmpAr9iYuLk5dfflnWrVsnbdq0ke3bt0uXLl1k0KBBcvz48SrU7H3RvXv3iicf72umBAIIIIAAAggggAACCCCAAAIIIICANwLu94d6UxN5DSOggn8qCPjKK6/IM888I7Nnz5aFCxfKpEmTZMiQIUHpZ2JiYlDaoREEEEAAAQQQQAABBBBAIBwFOAU4HGeVMSEQOgFWAIbOPqAtR0VF6e3AalvwrbfeKidOnJDhw4dLamqqqDQuBBBAAAEEEEAAAQQQQAABBBBAAIHIECAAGObzrE72zcrKknfffVcaNmwoq1evluuuu04fEFJQUBDmo2d4CCCAAAIIIIAAAggggAACCCCAAAIEACPk10D//v31OwEHDx4sRUVF+l2BLVu2lGXLlkWIAMNEAAEEEEAAAQQQQAABBMwjYLNZxGgf8+jRUwQQKCtgsZVcZRP5Ht4CK1eu1NuBv//+e7FY1P+o2PTP4uLioA/810m/CXqbNOha4KKo6q4fkBp0AWvQW6TBigRSrfEVPSI9yAKLLSeC3CLNuRMo5o+P7niC+iy65M9yXMYQSI6qbYyO0Ast8Nqu900vsb/DzYYbQ6O1yw3XJzqEAAKeCbAC0DOnsMp10003yaZNm2T8+PESGxsbVmNjMAgggAACCCCAAAIIIIAAAggggAACpQUIAJb2iJhv1apVk2effVbOnj0rVqtVQrH6L2KwGSgCCCCAAAIIIIAAAggg4KWAOgXYaB8vh0B2BBAwkAABQANNBl1BAAEEEEAAAQQQQAABBBBAAAEEEEDA3wIEAP0tSn0IIIAAAggggAACCCCAAAIIIIAAAggYSCDGQH2hKwgggAACCCCAAAIIIIAAAgggUCJgs3LID78QEEDAfwIEAP1naaiaVq1aJR988IHk5ORIdHS0tGzZUvr16ydt2rSptJ8//PCD/PrXv9YnA6vyXAgggAACCCCAAAIIIIAAAggggAAC5hUgAGjeuXPZ8/Pnz8ugQYPkvffeK/V84cKF8uKLL8qdd94pb7zxhtSvX7/Uc+cv586dk127dukAoHM69wgggAACCCCAAAIIIIAAAsERsNmC0w6tIIBAZAjwDsAwm+dhw4bJu+++K7aS/7Vw9cnMzJRrrrlGVq9eHWYjZzgIIIAAAggggAACCCCAAAIIIIAAAq4ECAC6UjFpmgrqzZ49W6/ca9asmahVf/n5+XL8+HFRgb/27dvroOChQ4ekR48esmjRIpOOlG4jgAACCCCAAAIIIIAAAggggAACCHgqQADQUykT5JsxY4buZaNGjWTNmjXSs2dPqVmzptStW1f69Okja9eulZdeekliYmKkoKBA+vbtK3PmzDHByOgiAggggAACCCCAAAIIIBBZAuoQEKN9ImsGGC0C4SVAADCM5lOtALRYLDJ27Fi56KKLyo3M/mzZsmU6KFhUVCTp6ekybdq0cnlJQAABBBBAAAEEEEAAAQQQQAABBBAIDwEOAQmPedSjOHDggP7ZoUMHt6Pq3LmzrFy5Up/0q8o88MADeqvwI4884rZcIB5eG31hIKqlTh8E8qTYh1IUCYTAruK8QFRLnT4I/BBdw4dSFAmEwJVSJxDVUqePAjut+T6WpJi/BRKi+H3K36a+1jf/5BZfi1IuAAKvBaBOqkQAAQTMLMAKQDPPXpm+qxOA1RUdHV3mSfmvV199tWRnZ0vjxo31ewEfe+wxeeaZZ8pnJAUBBBBAAAEEEEAAAQQQQCDoAkbb/qv6w4UAAuYVIABo3rkr1/MGDRrotD179pR75ipBBf9UELBly5Y6CPjcc8/p7cOu8pKGAAIIIIAAAggggAACCCCAAAIIIGBOAQKA5pw3l72+6qqrdLoK6nl6XXrppXo7cJs2bXQQ8JVXXpHRo0d7Wpx8CCCAAAIIIIAAAggggAACCCCAAAIGFyAAaPAJ8qZ7nTp10kG8efPm6Z+elq1Xr558/vnncuONN+pyy5cv97Qo+RBAAAEEEEAAAQQQQAABBAIgYLNJyd/PjPUJwDCpEgEEgiRAADBI0MFoJi0tTTejDvb48MMPvWqyTp06ok4H7t69u1fBQ68aITMCCCCAAAIIIIAAAggggAACCCCAQNAFCAAGnTxwDbZt21bUKsDk5GR55513vG4oLi5OFi1aJH369PG6LAUQQAABBBBAAAEEEEAAAQQQQAABBIwpEGPMbtErXwVWrlzpa1FdLjY2VjIzM6tUB4URQAABBBBAAAEEEEAAAQSqJsCpu1XzozQCCJQWYAVgaQ++IYAAAggggAACCCCAAAIIIIAAAgggEFYCBADDajoZDAIIIIAAAggggAACCCCAAAIIIIAAAqUF2AJc2oNvCCCAAAIIIIAAAggggAACCIRcwGazhLwPdAABBMJHgBWA4TOXjAQBBBBAAAEEEEAAAQQQQAABBBBAAIFyAqwALEdCQjAF9tnOBrM52nIjcLGlupunPEIgMgW6nuXfyYwy80djmAujzIXqxzrbaSN1J6L7UmA7H9HjN9LgH49vY6Tu0BcEEEAAAQRKCfCn6VIc5v6SnJwsaWlp8v7770thYaG5B0PvEUAAAQQQQAABBBBAAIEIFrBZRYz2ieDpYOgImF6AAKDpp/D/B7Br1y755JNP5He/+50kJCTIsGHDJDs7+/8zcIcAAggggAACCCCAAAIIIIAAAgggEHECBADDcMptNpucOnVK3n77benSpYs0adJEJk6cKDt37gzD0TIkBBBAAAEEEEAAAQQQQAABBBBAAAF3AgQA3emY9JlaAXjppZeKCgSqj1oZ+Oyzz8qVV14pqamp8tZbb+kAoUmHR7cRQAABBBBAAAEEEEAAgbAXsJacAmy0T9ijM0AEwliAAGAYTu6TTz4pe/bskSVLlujtwHFxcY5g4Jo1a2TEiBFy8cUXS//+/WXx4sVitZa8XMLP1759+8STj5+bpToEEEAAAQQQQAABBBBAAAEEEEAAgTICnAJcBiRcvkZFRUmPHj30Jz8/Xz744AOZPXu2rFixQgcDz549K/PmzdOfhg0byj333CP33XefXHPNNX4hSEpK8qieAZfd4VE+MiGAAAIIIIAAAggggAACCCCAAAII+CbACkDf3ExVKj4+Xu6//35Zvny53g783HPPSYsWLRyrAg8fPixTpkyRNm3aSOvWreWVV16RI0eOmGqMdBYBBBBAAAEEEEAAAQQQCCcBW8kWYKN9wsmXsSAQaQIEACNsxtXKPLVFeNu2bfLll1/KqFGjpF69eo5g4ObNm2Xs2LGSmJgot912m886e/fuFU8+PjdAQQQQQAABBBBAAAEEEEAAAQQQQAABjwTYAuwRU3hmateunaiPWvGXlZWltwirn+fOnZOioiL5+OOPfR64CiByIYAAAggggAACCCCAAAII+CZgs1p8K0gpBBBAwIUAKwBdoERaUkxMjPTu3VsyMzPl4MGD8vrrr0v79u0jjYHxIoAAAggggAACCCCAAAIIIIAAAmEpQAAwLKfV90HVrVtXRo4cKf/973/lu+++870iSiKAAAIIIIAAAggggAACCCCAAAIIGEKALcCGmAZjdqJp06bG7Bi9QgABBBBAAAEEEEAAAQTCXMBmC/MBMjwEEAiqAAHAoHLTWFmB2hZ+CZY1CdX3GOEdI6GyL9vu1dEXlk3ie4gE1kbzJ+8Q0ZdrNqm4XBIJIRSoZakWwtZp2lngRNFp56/ch1Agt1r9ELZO0wgggAACCLgXIPri3sdUT3/66Sfd30aNGpmq33QWAQQQQAABBBBAAAEEEEAAAQQQQCBwAgQAA2cb9Jovv/zyoLdJgwgggAACCCCAAAIIIIAAAv4X4BRg/5tSIwKRLMAhIJE8+4wdAQQQQAABBBBAAAEEEEAAAQQQQCDsBQgAhv0UM0AEEEAAAQQQQAABBBBAAAEEEEAAgUgWYAtwJM8+Y0cAAQQQQAABBBBAAAEEEDCkgNXGIX2GnBg6hYBJBQgAmnTiKuv2qlWr5IMPPpCcnByJjo6Wli1bSr9+/aRNmzaVFZUffvhBfv3rX4vFYtHlKy1ABgQQQAABBBBAAAEEEEAAAQQQQAABwwoQADTs1PjWsfPnz8ugQYPkvffeK1XBwoUL5cUXX5Q777xT3njjDalfv36p585fzp07J7t27dIBQOd07hFAAAEEEEAAAQQQQAABBBBAAAEEzCfAOwDNN2duezxs2DB59913xWazufxkZmbKNddcI6tXr3ZbDw8RQAABBBBAAAEEEEAAAQRCJ2Ar2QJstE/oNGgZAQSqKkAAsKqCBiqvgnqzZ8/WK/eaNWsmatVffn6+HD9+XFTgr3379jooeOjQIenRo4csWrTIQL2nKwgggAACCCCAAAIIIIAAAggggAACgRAgABgI1RDVOWPGDN1yo0aNZM2aNdKzZ0+pWbOm1K1bV/r06SNr166Vl156SWJiYqSgoED69u0rc+bMCVFvaRYBBBBAAAEEEEAAAQQQQAABBBBAIBgCBACDoRykNtQKQHVwx9ixY+Wiiy4q16r92bJly3RQsKioSNLT02XatGnl8pKAAAIIIIAAAggggAACCCAQOoGStzqV7OAy1id0GrSMAAJVFeAQkKoKGqj8gQMHdG86dOjgtledO3eWlStX6pN+VZkHHnhAbxV+5JFH3JYLxMPTtuJAVEudPggkWKr5UIoigRDYZssLRLXU6YPALdY6PpSiSCAEtkWfD0S11OmjQJLE+1iSYv4WuDCqur+rpD4fBZYXH/GxJMUCIfCnQFRKnQgggICJBVgBaOLJK9t1dQKwuqKjo8s+Kvf96quvluzsbGncuLF+L+Bjjz0mzzzzTLl8JCCAAAIIIIAAAggggAACCCCAAAIImFuAAKC5569U7xs0aKC/79mzp1R6RV9U8E8FAVu2bKmDgM8995zePlxRftIRQAABBBBAAAEEEEAAAQSCI2AtOQXYaJ/gjJxWEEAgEAIEAAOhGqI6r7rqKt2yCup5el166aV6O3CbNm10EPCVV16R0aNHe1qcfAgggAACCCCAAAIIIIAAAggggAACBhcgAGjwCfKme506ddJBvHnz5umfnpatV6+efP7553LjjTfqcsuXL/e0KPkQQAABBBBAAAEEEEAAAQQQQAABBAwuQADQ4BPkTffS0tJ0dnWwx4cffuhNUalTp46o04G7d+/uVfDQq0bIjAACCCCAAAIIIIAAAggg4JGArWQLsNE+HnWcTAggYEgBAoCGnBbfOtW2bVtRqwCTk5PlnXfe8bqSuLg4WbRokfTp08frshRAAAEEEEAAAQQQQAABBBBAAAEEEDCmQIwxu0WvfBVYuXKlr0V1udjYWMnMzKxSHRRGAAEEEEAAAQQQQAABBBBAAAEEEDCOAAFA48wFPUEAAQQQQAABBBBAAAEEEEBAC9hsQCCAAAL+E2ALsP8sqQkBBBBAAAEEEEAAAQQQQAABBBBAAAHDCbAC0HBTQocQQAABBBBAAAEEEEAAAQQiXcBacggIFwIIIOAvAVYA+kuSehBAAAEEEEAAAQQQQAABBBBAAAEEEDCgACsADTgpkdSl82KNpOEaeqznhZeMGGWC6liqGaUrEd+PebYjEW9gFIDfWBsapSv0o0SgrjUWB4MIrI5hhZBBpkKqRUcbpSv0AwEEEEAAgXICBADLkYRXwunTp2X9+vWyfft22bt3r+Tn50tBQYHExcVJfHy8JCUlScuWLSUlJUVq1aoVXoNnNAgggAACCCCAAAIIIICASQVsbAE26czRbQSMKUAA0JjzUuVerVy5Ul566SX57LPPpLCwsNL6qlevLrfccouMGzdOUlNTK81PBgQQQAABBBBAAAEEEEAAAQQQQAABcwjwDkBzzJPHvTx37pwMHDhQunbtKosXL5azZ8+KreT8+Mo+Kl9WVpZ06dJF7r33XlH1cCGAAAIIIIAAAggggAACCCCAAAIImF+AFYDmn8NSI7jzzjt14E8F/KJL3kOiVvV17txZWrRoobf7qm2+arWfWhWotgerbcE7duyQFStWyKeffirFxcXy7rvvysmTJ2XBggWl6uYLAggggAACCCCAAAIIIIBAcAQ4BTg4zrSCQKQIEAAMo5nOyMjQq/gsFovcfvvt8sYbb0ijRo3cjrBt27b6+eOPPy779u2TUaNGyaJFi3Q9c+fOlX79+rktX9FDVRcXAggggAACCCCAAAIIIIAAAggggEDoBQgAhn4O/NaDWbNm6brU9t+PPvpIVCDQmysxMVGXU6sGv/jiC3n77bd9DgCqw0U8ufpd1tuTbORBAAEEEEAAAQQQQAABBBBAAAEEEPBRgHcA+ghnxGLffvutDvo99NBDXgf/7OOJioqSMWPG6K+qPi4EEEAAAQQQQAABBBBAAIHgC9hKmjTaJ/gKtIgAAv4SYAWgvyQNUE9ubq7uxaWXXlql3tjL2+vzpTL1bkFPrrEdH/QkG3kQQAABBBBAAAEEEEAAAQQQQAABBHwUIADoI5wRiyUkJOhDPbZs2SL2d/v50s/NmzfrYqo+Xy+1nZgLAQQQQAABBBBAAAEEEEAAAQQQQCD0AmwBDv0c+K0Hqampok7//ctf/iI///yzT/Wqcqq8en+gqo8LAQQQQAABBBBAAAEEEEAg+ALqFGCjfYKvQIsIIOAvAQKA/pI0QD3qBF/1Dr+cnBy57rrrZN68eVJUVORRz1Q+deqvWjmoyqsA4AMPPOBRWTIhgAACCCCAAAIIIIAAAggggAACCBhXgC3Axp0br3vWoUMHGT9+vDz77LOyZ88e6d+/v9SpU0dUeosWLURty42Pj5dq1arJuXPnJD8/X/bt2yc7duyQtWvXyqlTp/QKQtXwn/70J7nhhhu87gMFEEAAAQQQQAABBBBAAAEEEEAAAQSMJUAA0FjzUeXeTJgwQS655BJ5/PHH5eTJk/rzySefiPq4u9TWYXVdcMEFMmnSJBk+fLi77H571tRS0291UVHVBH6yFVStAkr7TeBw8Wm/1UVFVROoE1W9ahVQ2m8CZyy//O+U3yqkoioJfB19pkrlKew/gdrCH+f9p1m1mq4vrlW1CiiNQBkBW8kWYC4EEEDAXwJsAfaXpIHqGTFihOzcuVMH8tTqP7UtWAX4Kvqo5yqfCvypcsEK/hmIjK4ggAACCCCAAAIIIIAAAggggAACYSvAPxmG6dTWrVtXxo0bpz/nz5+X77//Xm/3zcvLk7Nnz0qNGjWkdu3aeltws2bNJDY2NkwlGBYCCCCAAAIIIIAAAggggAACCCAQ2QIEACNg/lVw7+qrr9afCBguQ0QAAQQQQAABBBBAAAEETC9gNf0IGAACCBhJgC3ARpoN+oIAAggggAACCCCAAAIIIIAAAggggICfBVgB6GdQM1WXm5srS5Yskd27d+vTgVu1aiWpqalisfCyWTPNI31FAAEEEEAAAQQQQAABBBBAAAEE3AkQAHSnY9JnmzdvlokTJ0p2drZ+31+LFi1kzJgx0r9/f8eIZs+eLQ8++KCcPl36tNHk5GSZPn26dO3a1ZGXGwQQQAABBBBAAAEEEEAAgeAK2ISFGcEVpzUEwluALcBhNr9Lly6V9u3by/z58+Xo0aOiDv1Yv3693HPPPTJ+/Hg92qysLBkyZIjk5+eXOxk4JydH0tLSZPny5WEmw3AQQAABBBBAAAEEEEAAAQQQQACByBQgABhG837ixAkZOHCgFBYW6sBe8+bNJSUlRaKjo/X3559/XjZs2CCjR48Wq9Uq/fr1kzlz5ogKGs6cOVMH/hSHKj948GC9ejCMeBgKAggggAACCCCAAAIIIIAAAgggEJECbAEOo2mfMWOGHDt2TGJiYmTevHnSu3dvPbrvvvtOunTpIkeOHJHhw4fLzp07RW0BVqsCna/09HSZMmWKjB07Vvbu3Stz586V++67zzkL9wgggAACCCCAAAIIIIAAAkEQsNqC0AhNIIBAxAiwAjCMpnrx4sX6AI++ffs6gn9qeGol4Lhx4/QqwI0bN0qfPn3KBf/sDOpdgR07dtRfFy1aZE/mJwIIIIAAAggggAACCCCAAAIIIICASQUIAJp04lx1e9u2bTr5rrvuKve4V69ejjS19dfdpVYG2mw2UcFCLgQQQAABBBBAAAEEEEAAgeALWEsOATHaJ/gKtIgAAv4SYAuwvyQNUE9ubq7uRWJiYrneNGrUyJHWpEkTx72rm1atWulktWU40FfrQk62CrSxp/WfqV7d06zkC7CALZr9HgEm9rj6M7Yij/OSMbACuyyFgW2A2r0T4Lcp77wCmPun4rwA1k7V3ggMLOI/DG+8yIsAAgggEFwBVgAG1zugrcXFxen6o6LKT6v9mcrgfO+qQ7Vr19bJBQUFrh6ThgACCCCAAAIIIIAAAggggAACCCBgIgFWAJposirraoMGDSQvL08OHjxYWVa3z/Pz8/XzevXquc3HQwQQQAABBBBAAAEEEEAAgcAI2Eq2AHMhgAAC/hIgAOgvSQPUo7b5qhN+1Qm+rq6ZM2fqZFdbhJ3z5+Tk6K8JCQnOydwjgAACCCCAAAIIIIAAAggggAACCJhQgACgCSetoi6npKRIdna2fP311y6zpKenu0wvm7hq1Sqd1LRp07KP+I4AAggggAACCCCAAAIIIIAAAgggYDIBAoAmmzB33W3Xrp0+vXf58uXusrl9VlhYKJmZmWKxWKRjx45u8/IQAQQQQAABBBBAAAEEEEAgMALWwFRLrQggEKECBADDaOLT0tJk2bJlekQ2W8kbI0qCeN5eGRkZog4BUZ+ePXt6W5z8CCCAAAIIIIAAAggggAACCCCAAAIGEyAAaLAJqUp3VNCuW7duValC1DZhT7cKV6khCiOAAAIIIIAAAggggAACCCCAAAIIBEWAAGBQmGkEAQQQQAABBBBAAAEEEEAAAc8FOAXYcytyIoBA5QJRlWchBwIIIIAAAggggAACCCCAAAIIIIAAAgiYVYAAoFlnjn4jgAACCCCAAAIIIIAAAggggAACCCDggQBbgD1AIgsCCCCAAAIIIIAAAggggAACwRTgFOBgatMWAuEvQAAw/OfY0CNcVP2sofsXSZ0rLjk5mssYAm1t8cboCL2QRkX8d2GUXwZZsQVG6Qr9KBGoaYnGwSACcRb+OG+QqZDp1QnXGGUuVD9SjdQZ+oIAAggYQIA/MRhgEkLZhYMHD8qePXskPj5emjZtKtWrVw9ld2gbAQQQQAABBBBAAAEEEEAAAQQQQMDPAgQA/QxqhOoKCgpkxowZkp2dLWfPnpUWLVrIyJEj5YorrnB0b/PmzTpt7dq1jrS4uDi5++675YUXXpCEhARHOjcIIIAAAggggAACCCCAAALBFWBNaXC9aQ2BcBcgABhmM7xr1y7p3r277Ny50zGyRYsWyauvvioZGRnSu3dv+fHHH6Vr167y888/i81p2+eZM2dk9uzZsnz5clmxYkWpgKGjMm4QQAABBBBAAAEEEEAAAQQQQAABBEwlQADQVNPlvrNFRUU6wJeTk1MuY2Fhodx7772yfft2GTx4sJw4cUKSkpLk1ltvlQYNGsj+/fvl448/liNHjsjevXt1XrWCkAsBBBBAAAEEEEAAAQQQQAABBBBAwNwCBADNPX+leq9W+KmtvRaLRf74xz/K448/LrVr15b33ntPRo0aJadPn5ZHH31UVq1aJb///e9l6tSpEhsb66hDrQAcNGiQzJs3T9asWSNLly6VHj16OJ57c7Nv3z5vspMXAQQQQAABBBBAAAEEEEDAScAmFqdv3CKAAAJVEyAAWDU/Q5VWgTt13XjjjTJlyhRH34YMGSJbt26VV155RW8Dbtu2rbz++us6UOjIVHJTs2ZNvQV4w4YN8tNPP+lAoK8BQLW60JPrvsv7epKNPAgggAACCCCAAAIIIIAAAggggAACPgpE+ViOYgYU+Oabb3RQT63iK3vdd999jqRhw4aVC/7ZH6pTgFXAUL0bcP369fZkfiKAAAIIIIAAAggggAACCCCAAAIImFSAFYAmnThX3T527JhObtKkSbnHzicAX3PNNeWeOye0b99ef92zZ49zslf36j2CnlxPdXrIk2zkQQABBBBAAAEEEEAAAQQiSsDKDuCImm8Gi0CgBQgABlo4iPWrd/+pS733r+x14YUXOpLq1KnjuHd1U79+fZ2cn5/v6rFHaYmJiR7lIxMCCCCAAAIIIIAAAggggAACCCCAQGAFCAAG1jeotavTfNXKu8OHD1epXXVisLpcBRKrVDGFEUAAAQQQQAABBBBAAAEEPBKwcgiIR05kQgABzwQIAHrmZIpcCQkJOgB48OBBl/1V7wFUqwTr1q3r8rk9cf/+/fpW1ceFAAIIIIAAAggggAACCCCAAAIIIGBuAQKA5p6/Ur2/9tpr9cEdGzduLJVu/zJr1iz7rduf69at088vv/xyt/l4iAACCCCAAAIIIIAAAggggAACCCBgfAECgMafI497qA7veOutt2TVqlUel3GVMTMzU68UtB8G4iqPv9IOFZ/xV1XUU0WBa6P//z2RVayK4lUU+Ep8f/9mFZumeBmBH6rFlknha6gEotkGFSp6l+1WkyiX6SQGX+AYf5YKPnoFLU6M4b+LCmhI9lHA5mM5iiGAAAKuBAgAulIxaVqPHj3kqaeekqioKLFarfqnt0P58MMP5ccff9QBQFUfFwIIIIAAAggggAACCCCAAAIIIICAuQUIAJp7/kr1/rLLLpM///nPpdK8/dK5c2f56aefdDFVHxcCCCCAAAIIIIAAAggggAACCCCAgLkFCACae/783vt69eqJ+nAhgAACCCCAAAIIIIAAAgiETsAauqZpGQEEwlCAF1WE4aQyJAQQQAABBBBAAAEEEEAAAQQQQAABBOwCrAC0S0Tgz9zcXFmyZIns3r1b4uPjpVWrVpKamqrf/xeBHAwZAQQQQAABBBBAAAEEEEAAAQQQCEsBAoBhOK2bN2+WiRMnSnZ2tpw9e1ZatGghY8aMkf79+ztGO3v2bHnwwQfl9OnTjjR1k5ycLNOnT5euXbuWSucLAggggAACCCCAAAIIIIBA8ASsFkvwGqMlBBAIewG2AIfZFC9dulTat28v8+fPl6NHj0peXp6sX79e7rnnHhk/frwebVZWlgwZMkTy8/PFZrOV+uTk5EhaWposX748zGQYDgIIIIAAAggggAACCCCAAAIIIBCZAgQAw2jeT5w4IQMHDpTCwkId1GvevLmkpKRIdHS0/v7888/Lhg0bZPTo0WK1WqVfv34yZ84cUUHDmTNn6sCf4lDlBw8erFcPhhEPQ0EAAQQQQAABBBBAAAEEEEAAAQQiUoAAYBhN+4wZM+TYsWMSExOjVwBu375d1q1bJ2pLcEJCgh7p8OHDZefOnaK2AGdkZMiAAQPklltukfT0dFm0aJFMnjxZ59u7d6/MnTs3jHQYCgIIIIAAAggggAACCCBgHgFbSVeN9jGPHj1FAIGyAgQAy4qY+PvixYv1AR59+/aV3r17O0aiVgKOGzdOrwLcuHGj9OnTR28JdmRwulHvCuzYsaNOUQFBLgQQQAABBBBAAAEEEEAAAQQQQAABcwsQADT3/JXq/bZt2/T3u+66q1S6+tKrVy9Hmtr66+5S7wtU7wZUwUIuBBBAAAEEEEAAAQRlWkhKAABAAElEQVQQQAABBBBAAAFzC3AKsLnnr1Tvc3Nz9ffExMRS6epLo0aNHGlNmjRx3Lu6adWqlU4+cuSIq8d+TWsUXcuv9VGZ7wI/2kqfCO17TZSsqkCxzVrVKijvJ4FGwu9RfqKscjVFHIRYZUN/VpAnxf6sjrqqINCQP0tVQc+/RYed/+XP4v6tldp8FfjK14IGKsefCA00GXQFgTAQYAVgGEyifQhxcXH6Niqq/LTan6kMzvf2ss4/a9eurb8WFBQ4J3OPAAIIIIAAAggggAACCCCAAAIIIGBCgfKRIhMOgi7/ItCgQQN9c/DgwSqR5Ofn6/L16tWrUj0URgABBBBAAAEEEEAAAQQQQAABBBAIvQABwNDPgd96YN/mq07wdXXNnDlT3n77bXG1Rdg5f05Ojv5qPznY+Rn3CCCAAAIIIIAAAggggAACgRewlrz+wmifwI9aZPfu3TJ27Fhp0aKF1KpVSy666CJp166dvPTSS3LmzJkqdWH79u3y2muvSXp6ulx33XX678Y1atTQ7SQnJ8tvf/tb+c9//qPfie9JQ0VFRTJt2jRJTU0VtSBH7bZTr9waMWKEbN261ZMqyINA0AR4B2DQqAPfUEpKimRnZ8vXX3/tsjH1m5wn16pVq3S2pk2bepKdPAgggAACCCCAAAIIIIAAAghUWWDhwoUycOBAOXXqlKMuFfRbv369/rz11luSlZUlvv5d9S9/+YvMmTPHUbfzzU8//STqM3fuXOncubNkZmaKu11xx44dk7S0NPnqq9JvnNy5c6dMnz5d3nnnHR1sHDp0qHMz3CMQMgFWAIaM3v8Nq38VUaf3Ll++3OfKCwsL9W90FotFOnbs6HM9FEQAAQQQQAABBBBAAAEEEEDAU4FvvvlGr8BTwb/4+HhRwbo1a9bIZ599JsOGDdPVfP/999KzZ0/Jy8vztNpS+WJiYuT666+Xhx9+WNQOuY8//lgHFpctWyZ///vf5Ve/+pXOv2LFCrn99tvFanV9FEtxcbH06dPHEfzr27evruvLL7+UV199VRo2bCjq79ZqJaBqgwsBIwhYSgJGNiN0hD5UXUD9Jrhu3Tpd0c033ywqiOftpf6VYsKECbrY0qVL5corr/S2Cq/yD77iLq/ykzlwArm2c4GrnJq9EuAUYK+4Apq5reWCgNZP5Z4LcAqw51bByMkpwMFQ9qyNXdbTnmUkV8AF9hWdDHgbNOC5wFcHVnqe2aA551w60HA9u+fAvwPWp5tuuknvaFNBupUrV0qHDh1KtaW2AD/66KM67ZlnnnH8vbVUpkq+qC27qv6KLhXY69evn3z44Yc6i9oO3KtXr3LZ1au1hgwZotNHjRolr7/+eqk8P/74o7Rt21avZFSrFdXWY3ftlirMFwQCJMAKwADBhqJadXpvt27d9MeX4J/qs9ombF/6HOjgXyiMaBMBBBBAAAEEEEAAAQQQQMBYAmohi3qdlbpUYK1s8E+lq/cCtmzZUt3K1KlT5fz58/rem/9XWRAuOjpaxo0b56jS3idHwv9uXn75ZX2n3k+oApNlLxX0e+KJJ3SyCgbOnz+/bBa+IxB0AQKAQSenQQQQQAABBBBAAAEEEEAAAQQQsAt89NFH9lsZNGiQ4975JioqSu677z6dlJubK59//rnzY7/dq4U19uvs2bP2W8dPtQ1ZrehTl1otWLNmTccz55v777/f8ZUAoIOCmxAKEAAMIT5NI4AAAggggAACCCCAAAIIIOBKQL2ry2gfV/30R5r9IEp16q/aOlvRpQ7nsF+rV6+23/r1Z0ZGhqM+dRJx2cveV5Xu3J+y+S6++GJp1qyZTg5UX8u2yXcE3AkQAHSnwzMEEEAAAQQQQAABBBBAAAEEEAiogH1Fndo6626brnNAzl7GHx1TJ/quXbtWbz9Wh4+oq379+nLPPfeUq37btm2ONOf+OBKdbuzP9+7dK6dP885WJxpuQyBQ8dsvQ9AZmkQAAQQQQAABBBBAAAEEEEAAARGr92c6Bpxt3759HrWRmJjoUT6VSW2zVQE4dVVWrm7duqJWCapgmgqqVeXq0qWLqNN+XV0q+Ke27V544YXlHjsbVNbfpKQkXV6dvarKNW/evFx9JCAQLAECgMGSph2XAkesBS7TSQy+wIWW6sFvlBZdCnxX9MsfgFw+JDGoAv2FU4CDCu6msd2xBvxbkJv+hvuj1cUnwn2IphlfQnQt0/Q13DuaWu3ScB8i40NA7AGtyihUwMvTKy8vz5E1Pj7ecV/RjT0AmJ+fX1GWKqX/8Y9/lD/96U96BaCrirzpr+qr/QpUf+318xOBygQIAFYmFIbP1b+wfPrpp/LVV1/J0aNH9RLrRo0aSWpqqtx4441hOGKGhAACCCCAAAIIIIAAAgggYEQB54M2qlWrVmkXq1f/ZeFCQUHVFpPMnDlTryRUwUp1qMj69evlH//4h7z22muyc+dOeeuttyQhIaFcf7zpr72vqpKq9rdcR0hAwEsBAoBeghk5+/Hjx8X+ctGePXuKOsK87KV+M5s4caKcOOH6X+5btWol06ZNkxtuuKFsUb4jgAACCCCAAAIIIIAAAggEScAapHa8aaaq225dtVWjRg1H8rlz5xz3Fd0UFhbqR3FxcRVl8Si9cePGpfKpBTEjR46Uu+++WxYtWiTt2rWTNWvWlNuWXLa/zt9LVVjyxd5XlV7V/patm+8IeCtAANBbMQPnnz17tjzyyCNy5ZVXSq9evcr19MEHH9T/oqEeVLQke9OmTfoko8zMTLntttvK1UECAggggAACCCCAAAIIIIBAZApU9s47X1Rq167tKObJNln7YRqebBd2VOzhjQrmqZWBl19+uX7H4KOPPirvvvtuqdJl++suAGjvq6ogEP0t1TG+IFCJAKcAVwJkpsefffaZ7u5dd91VrtvqBaZvvPGGDvzVrFlTBwqXLVsmO3bskK1bt8qCBQskPT1drxo8f/68Pu3o8OHD5eohAQEEEEAAAQQQQAABBBBAAAF/CagAWr169XR1zgdsuKr/559/dpym6+n7CF3V4y5NHQDSsWNHneU///mPqL8fO1/OQdDK+mtfMWmxWMqtJHSuk3sEgiHACsBgKAepDRXMU5er9/i9/PLL+pl619/nn38u6nh156tly5Z6xd/9998vaWlpov7lZerUqfLXv/7VOZvH95X9RuhxRWREAAEEEEAAAQQQQAABBCJQwPNjNMyPc9VVV0l2drb8+OOPUlRUpN9T72pU9r/zqmfq77CBuho0aKCrPnPmjD6h+JJLLnE0pfpqv1R/Wrdubf9a7qe9vypY6XwgSLmMJCAQBAFWAAYBOVhN2FfsXXpp6RPI1G9aX375pah/dVCBwLLBP+f+de7cWcaMGaNXCn788cfOj7y6V7/BefLxqlIyI4AAAggggAACCCCAAAIIhJ1Ap06d9JjUltkNGzZUOL4VK1Y4ntlX6TkS/Hizf/9+R21lt+7a+6oyOPfHUeB/N4cOHZLvv/9efwtkX8u2y3cEKhIgAFiRjAnTo6J+mU7nU4nUMFRg0Gr95RWyv/nNbyodmT2POvmICwEEEEAAAQQQQAABBBBAAIFACtxxxx2O6tU7+Fxd6u+06r336rrwwgula9eurrJVOU3tZlu7dq2uR70L0PmdfyqxWbNmjtWHc+fOFbXgxtU1a9YsR3KfPn0c99wgECoBAoChkg9Au2p7r7rsy4ztTTifNuR8DLn9edmf9jzFxcVlH3n8Xb3rwJOPxxWSEQEEEEAAAQQQQAABBBCIIAGrRcRon0Dxt2/fXtQpvOqaMWOGIwDn3N7kyZNl+/btOumhhx6S2NhY58fyxRdf6F1vaueberVV2Uutxlu+fHnZ5FLfT548Kb/73e/EfhrxfffdV+q5/Ys6fFNdJ06cEHVQSNkrJydHnn/+eZ2sduARACwrxPdQCPAOwFCoB6hN9S8gKvinTily/g3v4osvFvVRKwHVVuCbbrrJbQ9UHnXZA4puM1fw0PnFqBVkIRkBBBBAAAEEEEAAAQQQQAABLaDeQa+2yhYUFEiPHj3kySef1Kv81PeMjAyZPn26zqdW4I0dO9ZrtQMHDki3bt3k2muvFbXisG3btvrvyTExMaK2665evVoHH9W9un71q1/J448/7rIddYDm22+/rcu8/vrruvywYcOkbt26sm7dOvnzn/8sp06dErVL79VXX63wnYYuKycRgQAJEAAMEGwoqlVBv2nTpok6DfjNN98U9RuQ/RoxYoRMnDhR/wam/tWjoqPKDx48qP+lQv2rSffu3e3F+YkAAggggAACCCCAAAIIIIBAwATatGkj77//vgwcOFAHz1QAsOylgn9ZWVnltuWWzefu+7fffivq4+7q2bOnqK3INWvWdJktOjpaPvroI32A5ldffSWZmZn645xZ7ax77bXXxP6KLedn3CMQCgG2AIdCPUBttmvXTgYPHqwP8Bg5cqSoZdEqoKcu9ZvnDTfcoFcAqp8LFixwHJ+unquly+pfMNTSa/UvI9WqVZMHHnhAPeJCAAEEEEAAAQQQQAABBBAIsoC1pD2jfQJNcPvtt8umTZv0wZQq2KcCcOp9fykpKTJp0iT55ptv3B5q6a5/anXhJ598IuPGjdMrC6+88kqpU6eOXp130UUX6RWB6u/Aq1atkkWLFon9JOCK6qxfv76sWbNG3njjDVEHg9SrV08vtElOTtaLcdRhJkOHDq2oOOkIBF3AYiu5gt4qDQZMQL2rQC1nXrJkiX7/gVpyfN1110mrVq1EvQtQvU+hsLBQt69W+anfTNW7/tTyZHWpXw4qXf1LhQoiBvq67bKegW6C+j0UuNBS3cOcZAu0wHfnjwW6Cer3UOBhSfIwJ9kCLbA7tuRFSFyGEVhWfMQwfYn0jiRE14p0AsOM/2L+LGWYuVAd+duuDEP1x5fOvJk40JdiAS0zbN+/A1o/lSOAQOAE2AIcONuQ1KxW7v3nP//R7xxQ/0Jy/vx5Wb9+vf44d0gF+tRHrfxzvho2bKjfUdCvXz/n5IDdXxEVH7C6qdg7gZO2894VIHfABFrE1g9Y3VTsncAcW653BcgdMIFoGwHAgOH6UHGdKP7RyAe2gBRpKa63pwWkMSp1K3BMfD9Az23FPEQAAQQQQMAPAmwB9gOi0apQpyE9++yz+kAQdSJR48aNdbDPHvRTP50vlV8dDKJeuqpOKwpW8M+5D9wjgAACCCCAAAIIIIAAAgj8v4DRtv+q/nAhgIB5BVgBaN65q7TnKvD3wgsv6M+xY8dk27ZtesXf6dOn9bsJateuLUlJSaLefaBOPuJCAAEEEEAAAQQQQAABBBBAAAEEEAg/AaI+4TenLkekXlCqVvlxIYAAAggggAACCCCAAAIIIIAAAghElgABwMiab0aLAAIIIIAAAggggAACCCBgAgFef2uCSaKLCJhIgACgiSbL313Nzc3VpwXv3r1b4uPj9UnBqamp+hRgf7dFfQgggAACCCCAAAIIIIAAAggggAACoREgABga94C2unnzZpk4caJkZ2fL2bNnpUWLFjJmzBjp37+/o93Zs2fLgw8+KOp9gM5XcnKyTJ8+Xbp27eqczD0CCCCAAAIIIIAAAggggAACCCCAgEkFCACadOIq6vbSpUuld+/ecu7cOX3yr8q3fv16ueeee2TLli3y3HPPSVZWlgwZMkSKi4vLVaNOAU5LS9N5br755nLPSUAAAQQQQAABBBBAAAEEEAi8AKfuBt6YFhCIJIGoSBpsuI/1xIkTMnDgQCksLNTBv+bNm0tKSopER0fr788//7xs2LBBRo8eLVarVfr16ydz5swRFTScOXOmDvwpI1V+8ODBevVguJsxPgQQQAABBBBAAAEEEEAAAQQQQCDcBQgAhtEMz5gxQ44dOyYxMTEyf/582b59u6xbt07UluCEhAQ90uHDh8vOnTtFbQHOyMiQAQMGyC233CLp6emyaNEimTx5ss63d+9emTt3bhjpMBQEEEAAAQQQQAABBBBAAAEEEEAgMgUIAIbRvC9evFgf4NG3b1+9Ddg+NLUScNy4cXoV4MaNG6VPnz56S7D9ufNP9a7Ajh076iQVEORCAAEEEEAAAQQQQAABBBAIvoDaAmy0T/AVaBEBBPwlQADQX5IGqGfbtm26F3fddVe53vTq1cuRprb+urvU+wJtNpuoYCEXAggggAACCCCAAAIIIIAAAggggIC5BTgExNzzV6r3ubm5+ntiYmKpdPWlUaNGjrQmTZo47l3dtGrVSicfOXLE1WO/prUqivVrfVTmu0C8lbnwXc+/JddWO+/fCqnNZ4GzUv6wJJ8ro2CVBIpsvAq9SoB+Lnyb9QI/10h1vgq8Zznqa1HK+VmgmN+n/CxKdTYIEEAAAT8KsALQj5ihriouLk53ISqq/LTan6kMzveu+ly7dm2dXFBQ4OoxaQgggAACCCCAAAIIIIAAAggggAACJhIoHykyUefpammBBg0a6ISDBw+WfuDlt/z8fF2iXr16XpYkOwIIIIAAAggggAACCCCAAAIIIICA0QTYAmy0GalCf9Q2X3XCrzrB19U1c+ZMnexqi7Bz/pycHP3VfnKw8zPuEUAAAQQQQAABBBBAAAEEAi9gtQS+DVpAAIHIESAAGEZznZKSItnZ2fL111+7HFV6errL9LKJq1at0klNmzYt+4jvCCCAAAIIIIAAAggggAACCCCAAAImE2ALsMkmzF1327Vrp0/vXb58ubtsbp8VFhZKZmamWCwW6dixo9u8PEQAAQQQQAABBBBAAAEEEEAAAQQQML4AKwCNP0ce9zAtLU2WLVum89tsNh3E87jw/zJmZGSIOgREfXr27OltcfIjgAACCCCAAAIIIIAAAgj4QcDqhzqoAgEEELALEAC0S4TBTxW069atW5VGorYJe7pVuEoNURgBBBBAAAEEEEAAAQQQQAABBBBAICgCbAEOCjONIIAAAggggAACCCCAAAIIIIAAAgggEBoBVgCGxp1WEUAAAQQQQAABBBBAAAEEEKhQgC3AFdLwAAEEfBBgBaAPaBRBAAEEEEAAAQQQQAABBBBAAAEEEEDALAIEAM0yU/QTAQQQQAABBBBAAAEEEEAAAQQQQAABHwTYAuwDGkX8J7As+pT/KqOmKgm0ia5dpfIU9p9Aq6JY/1VGTVUSSC3mfyarBOjHwrElp9tzGUfg/er5xulMhPekt7VBhAsYZ/hfxxQYpzP0JCwE+F++sJhGBoGAYQT4m41hpiJwHdm3b59s27ZN9uzZI3l5eVJQUCDqxOC6devK1VdfLddcc41ER0cHrgPUjAACCCCAAAIIIIAAAggggAACCCAQMgECgCGjD2zD+/fvl6lTp0pmZqbs2rXLbWM1atSQvn37yqhRo6RDhw5u8/IQAQQQQAABBBBAAAEEEEAAAQQQQMBcArwD0Fzz5VFvJ0+eLM2aNRP186effhJbybYp9VGX/d75p1oR+O6770qnTp1k8ODBepWgRw2RCQEEEEAAAQQQQAABBBBAICACVouI0T4BGSiVIoBAUARYARgU5uA18uSTT8qkSZMcAb8mTZpIQkKCnDx5Unbs2CHFxcVSvXp1efTRR6VWrVryww8/yIYNG2Tjxo26k++8845s3bpVli5dKhdccEHwOk5LCCCAAAIIIIAAAggggAACCCCAAAIBESAAGBDW0FT63//+Vwf/VOvdu3eX119/XZo2berozNGjR2X8+PHy5ptv6hV/KvBXp04d/TwnJ0f+9Kc/SUZGhqxfv17uv/9+mT9/vqOstzfqvYNcCCCAAAIIIIAAAggggAACCCCAAAKhFyAAGPo58FsPpk2bplf+paSkSFZWlsTElJ7eBg0ayD//+U85ffq0vPfeezrgp94TqC61UlBtA7722mvliSeekAULFsinn34qt9xyi0/9S0pK8qjcnZfd7lE+MiGAAAIIIIAAAggggAACkSRgjaTBMlYEEAi4AO8ADDhx8BpYuXKlWCwWGTNmTLngn3MvHnvsMR0o/Ne//uXYKmx/rp6lpqbqr//+97/tyfxEAAEEEEAAAQQQQAABBBBAAAEEEDCpQOklYiYdBN3+ReDQoUP6pkWLFm5J7M/VewHVacGJiYml8quDQLKzs2X16tWl0r35snfvXo+yj+44yqN8ZEIAAQQQQAABBBBAAAEEEEAAAQQQ8E2AAKBvboYspQ73KCwsrPQU37y8PEf/i4qKHPf2G3uA0B5QtKd787NsUNGbsuRFAAEEEEAAAQQQQAABBCJdwBbpAIwfAQT8KsAWYL9yhrYy9R4/dS1evNhtR+zPo6Oj5eKLLy6XV6Wry/6zXAYSEEAAAQQQQAABBBBAAAEEEEAAAQRMI0AA0DRTVXlHb731Vv1Ov1dffVW++OILlwXUab/qPX/qXYHXX3+91KhRo1y+nTt36rSEhIRyz0hAAAEEEEAAAQQQQAABBBBAAAEEEDCXAFuAzTVfbnv7xz/+UV577TW9BbhHjx4yYMAAUUFBFchT7/tbsWKFzJw5Uz+3HxbiqkJ1+q963qpVK1ePSUMAAQQQQAABBBBAAAEEEAiwgFXYBBxgYqpHIKIECACG0XQ3bNhQZs+eLXfffbecP39e1Cm+ZU/ytdl++R+RoUOHSt++fcuN/vTp0/LBBx/olYTdunUr99zfCTWEX4L+NvW1vv2W874WpZyfBY7FsDjbz6Q+V3cihv8ufMbzc8EztvLvrPVzE1TnhcCJ4gIvcpM1kAI/R9cKZPXU7YXAivxfdtF4UYSsCCCAAAIIBE2A6EvQqIPTUK9evUSt4Bs1apRs3bq1XKN169aVp556Sh5++OFyz1SCChAuW7ZMP2vevLnLPCQigAACCCCAAAIIIIAAAggEVsAa2OqpHQEEIkyAAGAYTnhqaqps3rxZNmzYIOvXr5fjx49LfHy8tGzZUjp16iRxcXEVjlrla9u2bYXPeYAAAggggAACCCCAAAIIIIAAAgggYC4BAoDmmi+veqsCeQTzvCIjMwIIIIAAAggggAACCCCAAAIIIBB2AgQAw25KGRACCCCAAAIIIIAAAggggIDZBTgCxOwzSP8RMJYAAUBjzUdQe5ObmytLliyR3bt36y3C6tRftX1YnQDMhQACCCCAAAIIIIAAAggggAACCCAQHgIEAMNjHkuNQr3/b+LEiZKdnS1nz56VFi1ayJgxY6R///6OfOq04AcffFDUqb/OV3JyskyfPl26du3qnMw9AggggAACCCCAAAIIIIAAAggggIBJBQgAmnTiKur20qVLpXfv3nLu3Dl9oq/Kpw4Cueeee2TLli3y3HPPSVZWlgwZMkSKi4vLVZOTkyNpaWk6z80331zuOQkIIIAAAggggAACCCCAAAKBF+AU4MAb0wICkSQQFUmDDfexnjhxQgYOHCiFhYU6+Ne8eXNJSUmR6Oho/f3555/XJwOPHj1arFar9OvXT+bMmSMqaDhz5kwd+FNGqvzgwYP16sFwN2N8CCCAAAIIIIAAAggggAACCCCAQLgLEAAMoxmeMWOGHDt2TGJiYmT+/Pmyfft2WbdunagtwQkJCXqkw4cPl507d4raApyRkSEDBgyQW265RdLT02XRokUyefJknW/v3r0yd+7cMNJhKAgggAACCCCAAAIIIIAAAggggEBkChAADKN5X7x4sT7Ao2/fvnobsH1oaiXguHHj9CrAjRs3Sp8+ffSWYPtz55/qXYEdO3bUSSogyIUAAggggAACCCCAAAIIIBB8AWvJ2YxG+wRfgRYRQMBfAgQA/SVpgHq2bdume3HXXXeV602vXr0caWrrr7tLvS/QZrOJChZyIYAAAggggAACCCCAAAIIIIAAAgiYW4BDQMw9f6V6n5ubq78nJiaWSldfGjVq5Ehr0qSJ497VTatWrXTykSNHXD32a9r+4jy/1kdlvgu0i/plm7jvNVDSXwI7o4r8VRX1VFEgVkr+6Z3LEAJNLbUM0Q868YvAj9FIGEVgu5w2Slcivh/RFtZWRPwvAgAQQAABAwsQADTw5Hjbtbi4OMnLy5OoqPJ/+FDP7JfzvT3N+Wft2rX114KCAudk7hFAAAEEEEAAAQQQQAABBIIkYBVbkFqiGQQQiASB8pGiSBh1mI6xQYMGemQHDx6s0gjz8/N1+Xr16lWpHgojgAACCCCAAAIIIIAAAggggAACCIRegBWAoZ8Dv/VAbfNVJ/yqE3xdXTNnztTJrrYIO+fPycnRX+0nBzs/4x4BBBBAAAEEEEAAAQQQQAABBBBAwFwCBADNNV9ue5uSkiLZ2dny9ddfu8yXnp7uMr1s4qpVq3RS06ZNyz7iOwIIIIAAAggggAACCCCAQBAE2AAcBGSaQCCCBNgCHEaT3a5dO3167/Lly30eVWFhoWRmZorFYpGOHTv6XA8FEUAAAQQQQAABBBBAAAEEEEAAAQSMIcAKQGPMg196kZaWJsuWLdN12Ww2HcTztuKMjAxRh4CoT8+ePb0tTn4EEEAAAQQQQAABBBBAAAEEEEAAAYMJEAA02IRUpTsqaNetW7eqVCFqm7CnW4Wr1BCFEUAAAQQQQAABBBBAAAEEKhSwVviEBwgggID3AmwB9t6MEggggAACCCCAAAIIIIAAAggggAACCJhGgACgaaaKjiKAAAIIIIAAAggggAACCCCAAAIIIOC9AFuAvTejBAIIIIAAAggggAACCCCAAAIBFbAK5wAHFJjKEYgwAVYARtiEM1wEEEAAAQQQQAABBBBAAAEEEEAAgcgSYAVgZM234UZbzcIvQaNMypEoXjNslLmIF/5txihzcdooHaEfclTOoWAggbO2YgP1JrK7EiOWyAYw0OjvqtXcQL2hKwgggAACCJQWIPpS2iNsvm3cuFFycnIkOjpaWrZsKc2be/YHkqNHj8o//vEP7fD000+HjQcDQQABBBBAAAEEEEAAAQTMJMAGYDPNFn1FwPgCBACNP0de9fCTTz6RP/zhDzr451zwmmuukb/85S+SlpbmnFzu/siRIzJhwgSxWCxCALAcDwkIIIAAAggggAACCCCAAAIIIICA6QTYZ2a6Kau4w3PnzpXbb79dB/9sNps4fzZt2qSfDRkyRAoKCiquhCcIIIAAAggggAACCCCAAAIhF1Av6DHaJ+QodAABBHwWYAWgz3TGKnj48GEZPny4FBUV6dV7d955p3Tt2lUKCwtlxYoVkpWVJcXFxTJr1izZtm2b/n7RRRcZaxD0BgEEEEAAAQQQQAABBBBAAAEEEEDA7wIEAP1OGpoKp02bJqdOndLv/MvMzJRevXo5OjJmzBj59ttvZejQobJhwwZZt26d3HTTTbJs2TK55JJLHPn8ebNv3z5/VkddCCCAAAIIIIAAAggggAACCCCAAAI+ChAA9BHOaMXUu//Ue/uGDRtWKvhn7+e1114rq1evlgceeEBmzJgh27dvl06dOslnn30mV1xxhT2b334mJSV5VFePxFs9ykcmBBBAAAEEEEAAAQQQQCCSBKzCMSCRNN+MFYFAC/AOwEALB6n+77//XrfUt2/fClusVq2avPnmm/owEPV+wF27dklqaqrs2LGjwjI8QAABBBBAAAEEEEAAAQQQQAABBBAwtwArAM09f47eq+2/6mrQoIEjraKbJ554QtT7/9RqwAMHDujtwEuXLpXWrVtXVMTr9L1793pUZkiHYR7lIxMCCCCAAAIIIIAAAggggAACCCCAgG8CBAB9czNcqfj4eDl58qQcP37co76NGDFC6tSpI+np6XLs2DG5+eabZfHixVK7dm2PyleWKTExsbIsPEcAAQQQQAABBBBAAAEEEKhAgA3AFcCQjAACPgmwBdgnNuMVatq0qe7U+vXrPe7cgAED5IMPPpDq1atLbm6u9OjRQ78T0OMKyIgAAggggAACCCCAAAIIIIAAAgggYHgBAoCGnyLPOpiSkiLqvX6LFi3yrMD/cqnTglWZWrVqyenTp0WdGMyFAAIIIIAAAggggAACCCCAAAIIIBA+AmwBDpO57N69u0ybNk2f9Pvdd99J8+bNPR5Zt27dRJ0ifNttt+ltxB4X9EPGVtEX+qEWqvCHQLRY/FENdfhBIE+K/VALVfhD4KbzNfxRDXX4QaB+UZEfaqEKfwn8u0asv6qinioKVLfw7/lVJPRb8XVFx/xWFxUhoASsMCCAAAJ+FOBPDH7EDGVVt956q17Fp1YBTpgwweuu3HjjjbJ8+XKPDhHxunIKIIAAAggggAACCCCAAAIIIIAAAgiETIAVgCGj92/DcXFxsmbNGlGnAUdF+RbXVacAqzqys7P92zlqQwABBBBAAAEEEEAAAQQQQAABBBAImQABwJDR+7/hVq1aVbnS5ORkUR8uBBBAAAEEEEAAAQQQQACB0AnYhHOAQ6dPywiEnwABwPCbU49HVFBQIEePHtX5L7vsMo/LkREBBBBAAAEEEEAAAQQQQAABBBBAwDwCvu0VNc/46KkbgQULFkjjxo1Z8efGiEcIIIAAAggggAACCCCAAAIIIICA2QVYAWj2Gaxi/9WhIVwIIIAAAggggAACCCCAAALGEuAUYGPNB71BwOwCBADNPoP/6//KlSu9Hsm2bdscZdTBH2WDgTfddJPjOTcIIIAAAggggAACCCCAAAIIIIAAAuYUIABoznkr1+suXbqIxWIpl+5Jggr8qfLOl6qrqKjIOYl7BBBAAAEEEEAAAQQQQAABBBBAAAETChAANOGkuety2VV87vI6P/O1nHMd3COAAAIIIIAAAggggAACCPhHwMopwP6BpBYEENACBADD7BdCQkKCDBkyRGJjYysd2ZYtWyQzM1OvHHz66acrzU8GBBBAAAEEEEAAAQQQQAABBBBAAAHzCRAANN+cuezxuHHjZMqUKXLkyBGZP3++TJs2TVJTU13mtSe+//77OgCovj/zzDP2ZH4igAACCCCAAAIIIIAAAggggAACCISRQFQYjSWihzJp0iRZt26dtG3bVrZv367f6adWAp44cSKiXRg8AggggAACCCCAAAIIIGBGAVtJp432MaMjfUYAgV8EWAEYRr8SWrduLV9++aVMnTpV1JbeWbNmyYIFC+TFF1+UQYMGGXKk31nzDNmvSOxUTQu/HRhl3g8U8d+FUeZid0wto3Ql4vsRG8O/WRrpF8Ht5+ON1J2I7suMqMMRPX4jDb5xzAVG6g59QQABBBBAoJQAf5ouxWH+L+r03tGjR8vWrVslLS1Njh8/LkOHDpXOnTvrlYHmHyEjQAABBBBAAAEEEEAAAQQQQAABBBDwRoAAoDdaJsqblJQkCxculIyMDGnYsKFkZ2eLWiH4xBNPSEFBgYlGQlcRQAABBBBAAAEEEEAAgcgTUKcAG+0TebPAiBEIHwECgOEzly5H0q9fP9mxY4deBVhUVKS3A1999dWSlZXlMj+JCCCAAAIIIIAAAggggAACCCCAAALhJcBLv8JrPl2O5oILLpDp06fLvffeK8OHD5fvvvtOevXqJcnJyS7zk4gAAggggAACCCCAAAIIIBBaAWtom6d1BBAIMwFWAIbZhLobTmpqqmzatEkfEBIbGys5OTnusvMMAQQQQAABBBBAAAEEEEAAAQQQQCAMBFgBGAaT6M0QVOBvwoQJMmDAAJkyZYoUFhZ6U5y8CCCAAAIIIIAAAggggAACCCCAAAImEyAAaLIJ81d3mzdvLtOmTfNXddSDAAIIIIAAAggggAACCCDgRwFbySEgXAgggIC/BNgC7C9J6kEAAQQQQAABBBBAAAEEEEAAAQQQQMCAAgQADTgpdAkBBBBAAAEEEEAAAQQQQAABBBBAAAF/CbAF2F+S1IMAAggggAACCCCAAAIIIICAnwQ4BdhPkFSDAAJagBWA/EJAAAEEEEAAAQQQQAABBBBAAAEEEEAgjAVYARjGk2uGodWwRJuhmxHRR+bCONM8RC4xTmcivCdvFx2McAHjDP/ymAuM0xl6IoX8E7JhfhUkRtc2TF8ivSPVhf8wIv3XAONHAAEEjCxAANDIsxOAvh05ckRmzJghK1askP379+sWLr30UuncubMMGjRILrmEwEMA2KkSAQQQQAABBBBAAAEEEPBKgFOAveIiMwIIVCJAALASILM8nj17tu7qHXfcIXXq1HHZ7bfeekvGjBkjZ86cKfV827Zt8umnn8pf//pXmTx5sowYMaLUc74ggAACCCCAAAIIIIAAAggggAACCJhXgACgeeeuVM/vv/9+sVgskpKSIldddVWpZ+rL9OnTZeTIkTrdZrPpvPXq1dM/jx07JipNBQZHjRolMTExMmTIkHJ1kIAAAggggAACCCCAAAIIIIAAAgggYD4BXlRhvjnzuscHDhyQ0aNH6yCfCu79+c9/lkOHDonaDnz48GH9UWnVqlXTeVReFRTkQgABBBBAAAEEEEAAAQQQCI2AOgXYaJ/QSNAqAgj4Q4AVgP5QNHgd//jHP+Ts2bMSFRUl8+fPl7S0tFI9rl+/vjz11FNy7bXXSu/evfVKQLVd+PHHHy+Vz5sv+/bt8yY7eRFAAAEEEEAAAQQQQAABBBBAAAEEAiTACsAAwRqpWvV+P7U9+K677ioX/HPu52233SZ33nmnXgX42WefOT/y+j4pKUk8+XhdMQUQQAABBBBAAAEEEEAAAQQQQAABBLwSIADoFZc5M//444+64yq4V9l199136yxbt26tLCvPEUAAAQQQQAABBBBAAAEEAiRgLXlPu9E+ARoq1SKAQBAE2AIcBORQN3Hq1CndheTk5Eq7Ys/z888/V5rXXYa9e/e6e+x4NqbjA457bhBAAAEEEEAAAQQQQAABBBBAAAEE/C9AAND/poarUb3jTx364c2lDgupypWYmFiV4pRFAAEEEEAAAQQQQAABBBBAAAEEEPCTQNWiPH7qBNX4T+DgwYMSHx9fqsLmzZvrAKBalde2bdtSz8p+UScDq0sFDbkQQAABBBBAAAEEEEAAAQRCI2ALTbO0igACYSpAADDMJrZHjx4Vjui///2v3HHHHRU+Vw++/fZb/fySSy5xm4+HCCCAAAIIIIAAAggggAACCCCAAALmECAAaI558qiXtpKXxLq7MjMz5YUXXnCXRRYuXKhPDE5JSXGbz18Pi2xWf1VFPVUVsFS1Asr7S2BvjPv/lv3VDvVULnC+iN+jKlcKTo6D1tPBaYhWPBLYFFPNo3xkCrxA++KagW+EFjwS+Dzql/due5SZTAgggAACCARZgABgkMED1dzMmTM9qjovL09q167tMu/GjRtl7dq1OgDYoUMHl3lIRAABBBBAAAEEEEAAAQQQCLyAVfhH4cAr0wICkSNAADBM5jo9Pb3KI6lbt658/vnnup7K3hVY5caoAAEEEEAAAQQQQAABBBBAAAEEEEAgKAIEAIPCbI5GLr/8clEfLgQQQAABBBBAAAEEEEAAAQQQQACB8BEgABg+c+n1SAoKCuTo0aO63GWXXeZ1eQoggAACCCCAAAIIIIAAAggERsDGFuDAwFIrAhEqEBWh42bYJQILFiyQxo0bS3JyMh4IIIAAAggggAACCCCAAAIIIIAAAmEqwArAMJ1YT4dV2cnBntZDPgQQQAABBBBAAAEEEEAAAf8JWP1XFTUhgAACQgAwTH4RrFy50uuRbNu2zVEmOztbygYDb7rpJsdzbhBAAAEEEEAAAQQQQAABBBBAAAEEzClAANCc81au1126dBGLxVIu3ZMEFfhT5Z0vVVdRUZFzEvcIIIAAAggggAACCCCAAAIIIIAAAiYUIABowklz1+Wyq/jc5XV+5ms55zq4RwABBBBAAAEEEEAAAQQQ8I+AlUNA/ANJLQggoAUIAIbZL4SEhAQZMmSIxMbGVjqyLVu2SGZmpl45+PTTT1eanwwIIIAAAggggAACCCCAAAIIIIAAAuYTIABovjlz2eNx48bJlClT5MiRIzJ//nyZNm2apKamusxrT3z//fd1AFB9f+aZZ+zJ/EQAAQQQQAABBBBAAAEEEEAAAQQQCCOBqDAaS0QPZdKkSbJu3Tpp27atbN++Xb/TT60EPHHiRES7MHgEEEAAAQQQQAABBBBAwIwCtpItwEb7PzM60mcEEPhFgBWAYfQroXXr1vLll1/K1KlTRW3pnTVrlixYsEBefPFFGTRokCFHWieqmiH7FYmdShDmwijzvt520ihdifh+NI2pG/EGRgGoa6n81RZG6Wsk9KNPQXEkDNMUY9wVwx/njTJRCw5vMEpX6AcCCCCAAALlBFgBWI7E3Anq9N7Ro0fL1q1bJS0tTY4fPy5Dhw6Vzp0765WB5h4dvUcAAQQQQAABBBBAAAEEEEAAAQQQ8FaAAKC3YibJn5SUJAsXLpSMjAxp2LChZGdni1oh+MQTT0hBQYFJRkE3EUAAAQQQQAABBBBAAIHIFLCWDNton8icCUaNQHgIEAAMj3mscBT9+vWTHTt26FWARUVFejvw1VdfLVlZWRWW4QECCCCAAAIIIIAAAggggAACCCCAQPgIEAAMn7mscCQXXHCBTJ8+Xb744gtp1qyZ7Nq1S3r16iXjx4+vsAwPEEAAAQQQQAABBBBAAAEEEEAAAQTCQ4AAYHjMo0ejSE1NlU2bNukDQmJjYyUnJ8ejcmRCAAEEEEAAAQQQQAABBBAIroDNVnIGsME+wRWgNQQQ8KcAx4b5U9MEdanA34QJE2TAgAEyZcoUKSwsNEGv6SICCCCAAAIIIIAAAggggAACCCCAgK8CBAB9lTN5uebNm8u0adNMPgq6jwACCCCAAAIIIIAAAggggAACCCBQmQABwMqEeI4AAggggAACCCCAAAIIIIBAkAWsYgtyizSHAALhLMA7AMN5dhkbAggggAACCCCAAAIIIIAAAggggEDECxAAjPhfAgAggAACCCCAAAIIIIAAAggggAACCISzAFuAw3l2GRsCCCCAAAIIIIAAAggggIApBaym7DWdRgABowoQADTqzAShX5s3b5YdO3ZITEyMNG7cWFq3bh2EVks3UU9iSyfwLWQCR+VcyNqm4dICtS3VSifwLWQCp2z8dxEy/DINR1ksZVL4GkqBd+JC2TptOwtEy1nnr9yHUGDFRR1C2DpNI4AAAggg4F6AAKB7H9M93bVrl/ztb3+TtWvXyrlz50Sd9jty5Ejp2rWrYyzbt2+X9PR02bBhgyNN3TRq1EgmTZokAwYMKJXOFwQQQAABBBBAAAEEEEAAAQQQQAAB8woQADTv3JXr+aeffip33HGHFBQUOJ5t2bJFMjMzZfz48TJx4kQ5cOCAdO/eXQ4ePCg2W+lTpfbt2ycDBw6UQ4cOyZgxYxx1cIMAAggggAACCCCAAAIIIBBcARunAAcXnNYQCHMBAoBhMsFHjx6V/v37y5kzZxwjql+/vhw7dkx/f+6556Rz587y1ltv6SBgjRo1dLCvbdu2cv78eVmxYoXMnz9frFarPPHEE3L77bdL06ZNHXVxgwACCCCAAAIIIIAAAggggAACCCBgTgFOATbnvJXr9bRp0+TEiRNiKXlH0sMPPyy5ubly5MgROXnypIwdO1av9vvrX/8qH330kdSrV0/Wr18v06dPlxEjRsiDDz4o8+bNk4ULF+r3AaqA4D//+c9ybZCAAAIIIIAAAggggAACCCCAAAIIIGA+AVYAmm/OXPZ4yZIlOvinVu69/PLLjjy1a9eWl156SXJycnTwTwUI1TsCr7rqKkce+81vfvMbGTp0qKhgoloRWJVLbSfmQgABBBBAAAEEEEAAAQQQ8E3AGqFbgHfv3i2vvvqqZGVlyd69e6V69erSpEkT6devnzzwwANSs2ZN30BLSqkdb+ogzHXr1unPV199JZs2bdLvz1eVfv7559KlSxd16/ZSeTz9O3PZV2+5rZiHCARQgABgAHGDWbX6TUxd6nAPV9e9996rA4DqWa9evVxl0WnqHYIqAPjjjz9WmMeTB0lJSZ5kkzGX/9ajfGRCAAEEEEAAAQQQQAABBBAIbwG1K029l/7UqVOOgarXXKkdbOqjXmmlAoO+vq7qX//6l9x///2OurlBIJIECACGyWzn5eXpkVx22WUuR+ScnpCQ4DKPSrQ/y8/PrzAPDxBAAAEEEEAAAQQQQAABBAIrEGkrx7755hv57W9/qw+1jI+P1++m79q1q/6ekZEhb775pnz//ffSs2dPHQxUu928vZxNY2NjpVWrVvqd+Js3b/a2Kp0/JSVFZs6c6VNZCiEQbAECgMEWD1B7tWrV0v9Kok75VQd7lL3Uqb/2S70rsEGDBvavpX7+/PPP+ntVllWrCtRSbU+uv3V6xJNs5EEAAQQQQAABBBBAAAEEEAhjgYceekgH+2JiYmTp0qXSoUMHx2hvvvlmufLKK+XRRx/VQcDJkyfLhAkTHM89vVGvwlLbi9u1ayetW7cWdTimqsfXAKD6e/ivfvUrT5snHwIhFeAQkJDy+69x9U4EdS1YsMBlpc7pn376qcs8KnHZsmX62eWXX15hHk8eJCYmiicfT+oiDwIIIIAAAggggAACCCCAQPgKqHfyZWdn6wEOGTKkVPDPPmp1uGXLli3116lTp+qVe/Znnv5s3769/OEPf5AbbrhBB/88LUc+BMJBgABgOMxiyRhuueUWfdLvrFmzyp3gq070VcuS27RpI9HR0fLUU0/J0aNHy418y5Yt8ve//10fJtKpU6dyz0lAAAEEEEAAAQQQQAABBBAIjoC1pBmjfQI18o8++shR9aBBgxz3zjdRUVFy33336aTc3Fx9YIfzc+4RQMC9AAFA9z6meTpy5EiJi4vTpxqNGjVKGjZsKNdff73+qb6r046efPJJUSf97tq1Sy93fuGFF/TS6sWLF+v3K6ign3r3nzopWJ0GzIUAAggggAACCCCAAAIIIIBAoAVWrVqlm1Bbal290srefufOne23snr1asc9NwggULlATOVZyGEGAbVlV53eq/61RAX7jh07JsePH9erAlX/1enAd955p6h3HqxcuVIOHTqkVwI6j83+QlT17gW1WjAYV11bdDCaoQ0PBPIsxR7kIkswBJKlWjCaoQ0PBNbYCjzIRZZgCBy3ng1GM7ThocCxotMe5iRboAXuiE0MdBPU76HAa9U5RM9DqqBk6xiUVmjEXwLbt2/XVanTfdU7ACu6WrRo4XhkL+NICMHNjh079MKb7777Ts6ePSv169fXAUz1d+8BAwaIOmiECwGjCFT8X5ZRekg/PBa49957JTk5WdTKvjVr1si5c+ekefPm8vvf/14GDx6s61HvTFDvA1S/GakDQ5wvdfDH448/LuPHj3dO5h4BBBBAAAEEEEAAAQQQQCDIAjaxBbnFypvbt29f5ZlKcqj3wXt6qcCZWsCirsrK1a1bV9QqwdOnT3t88KSn/fAl3+HDh0V97Nf+/ftFfdTfuSdNmiQffPCB472F9jz8RCBUAgQAQyUfoHY7duwoCxcudFt7amqq7Ny5U5YsWSLqX03Uv7A0btxYunXrJhdccIHbsjxEAAEEEEAAAQQQQAABBBCITIGkpCSPBm7fXeZJ5ry8PEe2+Pj4/2vvXuCtquoEAK+LXFBL1HiogAhooJTPEgMzRbIxRzHzUVojiELmNKNZajXm61f2HscZlXR8gNmMlKikhqajJCm+IzWkAkFBJS0tBFHgcueubed2uN7nueex9znf9ne8+5y99tr/9a3Lvff8z9prNe+3tZNLAMbpqyq1xfkI4/vnww47LOy5556hb9++IbbjiSeeSObkj++zFy5cGMaNGxfiAidDhgypVKiuS6BZQAKwmaK2dnr16hUmTJiQPGqr5VpLgAABAgQIECBAgAABAmkRiCMAc1t8n9rR1rt376TI2rWVm6rl5ptvDttss807Qo2DbeIc/FOmTAkzZsxIRgeeccYZIZa3Eai0gARgpXuggtePPzBzqwH7RKKCHeHSBAgQIECAAAECBAgQaCGwMYW3AC9fvrxFlN1/uvnmmzdXEqex6mh76623kiJxEcxKba0l/3KxxHn/rr766vDQQw+FODfgLbfcktwWPGjQoFwRXwlUREACsCLs6bhobi7AOHx5w4YN6QhKFAQIECBAgAABAgQIECCQSoGO5ugrJOitttqq+bTO3NYb5/+LW2duF26uuMw7cZqtk08+OZx99tnJlX/5y1+GE044ocxRuByBTQV6bPrUs1oU6Mr8DLXoo80ECBAgQIAAAQIECBAgUBqBOAIwzqEXt44WGXnttdeSBUBi2c7ORxjLVmIbNWpU82XjwiA2ApUWMAKw0j1QpOvff//9Xa4pTkqa2+bNmxdaJgI/8pGP5A77SoAAAQIECBAgQIAAAQJlFGj5/qyMly77pWKyLL4nXbx4cXJ3WhxB19q2aNGi5pd322235v007tTV1aUxLDHVsEDr/6pqGCSrTT/ooINCoT9g4i+WeH7+FutyW3C+iH0CBAgQIECAAAECBAgQKIXAhz/84SQBGG/vffzxx8N+++3X6mXirbS5bf/998/tpvJr/oCbgQMHpjJGQdWWgFuAq6y/YzKvK49c81s7J3fMVwIECBAgQIAAAQIECBAgUCqBT3ziE81VX3fddc37+TsbN24M119/ffJSXIRj3Lhx+YdTtR8H01x77bXNMbm7rpnCTgUFjACsIH4pLr3ddtslk43GlYc62p5++ukwa9asZOTgeeed11FxxwkQIECAAAECBAgQIECgTAJpXAW4VE0fPXp0OOCAA5JRgNdcc02YOHFiGDNmzCaX+8EPfhCeeeaZ5LXTTz89tHzPO3fu3OakYDx/+vTpm5xfrCf33Xdf2HvvvUNbKwGvX78+TJkypTnWI444IvXzFRbLRj3pFpAATHf/dDq6s846K1xyySXh5ZdfTpYZ/+EPf5j8AG2vgpkzZyYJwFjm/PPPb6+oYwQIECBAgAABAgQIECBAoGQCl156aYi39a5duzZ87GMfC1/72teShF58fuONN4arrroqufaIESPCl770pYLjaJkYXLBgQXNdd955Z1i2bFnz81122SXE25PztxkzZoQJEyYkjziV1siRI0OfPn1CXME43r4c48zd/jtgwIAQ22UjkAYBCcA09EIRYvjOd74Tjj/++DB16tTw2GOPJXP6TZo0KXzve98L73nPe4pwBVUQIECAAAECBAgQIECAAIHSCMRRdXGQymc/+9mwatWqJAHY8kox+XfHHXeErbbaquWhTj8/6aST2iwb31fnb3EkYcsEYDwek33/8z//kzzyy+fv77777knictiwYfkv2ydQMQEJwIrRF//Ce+21V3j44YeTTxjiLb3xk42f/exn4bvf/W5o74dc8SPpfI1PhtWdL6xkSQUamuaPtKVDoEddOuIQRQinrd8GQ0oEbt58XUoiEUYUGNBrCxApEXi88fWURCKMn7/yGwgEiirQNLt7UevLQmXxdtknn3wyeU8bE30rVqwIvXr1CnEk3rHHHhu+8IUvhC233LKiTTnnnHNCfO89f/78ZKTfK6+8El599dXQu3fvEKfk+uAHPxiOOeaYcNRRR4XNNtusorG6OIF8gbqmxR9q76dKvkCV7i9fvjycdtppyacjcUXf+KlFvC04f6n0+OlKHDUYjzc0NFRE4tidjqzIdV30nQINNfgHxjsV0vFK/7re6QhEFOHwN31OlpZvAwnAtPTE23HUBZ9UpKVHXm9cn5ZQaj4OCcB0fQuseWNZugIqIJqDBn+0gLNKe8rcFfeU9gJqJ0CgZAJWAS4ZbWUr3nHHHcNtt92WDDmO8w7Mmzcv+ZTiq1/9ajKnQmWjc3UCBAgQIECAAAECBAgQIECAAIFyCUgAlku6Qtc57rjjwqJFi8Ipp5wS4lLk8Xbg973vfcnIwAqF5LIECBAgQIAAAQIECBAg0IHAxqab9dL26CBkhwkQSLGABGCKO6dYoW299dbJSkRxpUexbgAANnNJREFUWfQ4aWpc1SiuWnTuuecW6xLqIUCAAAECBAgQIECAAAECBAgQSKmABGBKO6YUYR1wwAHJhKpxgZD6+vqwZMmSUlxGnQQIECBAgAABAgQIECBAgAABAikSMLt5ijqjHKHExN8FF1yQLP5xySWXhLfeeqscl3UNAgQIECBAgAABAgQIEOiCgNU6u4ClKAECHQpIAHZIVJ0FRo4cmawKXJ2t0yoCBAgQIECAAAECBAgQIECAAIGcgARgTsJXAgQIECBAgAABAgQIECCQEoGNwRjAlHSFMAhUhYA5AKuiGzWCAAECBAgQIECAAAECBAgQIECAQOsCEoCtu3iVAAECBAgQIECAAAECBAgQIECAQFUIuAW4KrpRIwgQIECAAAECBAgQIECgmgTcAlxNvaktBCovIAFY+T6o6Qh2q3t3Tbc/TY1/KaxLUzg1HcuqxvU13f40Nf6+zTdLUzg1HcvT616p6fanrfEf7LV92kKq3Xjq6mu37Slr+cJdd0lZRMIhQIAAAQJ/F3AL8N8t7BEgQIAAAQIECBAgQIAAAQIECBCoOgEjAKuuS7vfoAceeCBcc801oa6uLvna/RrVQIAAAQIECBAgQIAAAQJdEWhstApwV7yUJUCgfQEjANv3qcmjixcvDtOnT08eNQmg0QQIECBAgAABAgQIECBAgACBKhKQAKyiztQUAgQIECBAgAABAgQIECBAgAABAi0F3ALcUsTzogisWLGiKPWohAABAgQIECBAgAABArUoYBXgWux1bSZQOgEJwNLZlr3m+++/vyjXXLRoUbfr2XHHHTtVx9d3OqFT5RQiQIAAAQIECBAgQIAAAQIECBAoTEACsDC3VJ510EEHJQt3pDI4QREgQIAAAQIECBAgQIAAAQIECFREQAKwIuylvWgaVotavnx5pxp51YfP6VQ5hQgQIECAAAECBAgQIFBLAo3BKsC11N/aSqDUAhKApRYuY/29evUK69evD3vssUc46qijCr7yggULwuzZsws+P544ePDgbp3vZAIECBAgQIAAAQIECBAgQIAAgeIISAAWxzEVtcTE32OPPRbq6+vD+eefX3BMM2bM6HYCsOCLO5EAAQIECBAgQIAAAQIECBAgQKCoAj2KWpvKKiqw7777Jtd/6qmnwrp16yoai4sTIECAAAECBAgQIECAQOECcWqntD0Kb40zCRCotIARgJXugSJef/To0WHatGnJbcDxNt74PO3bK2F92kOsmfjWNG6ombamvaFD6rZIe4g1E9+vG16rmbamvaGDe26d9hBrKr7lG9fUVHvT3Ngde7wrzeHVVGwz/rxdTbU37Y09L+0Bio8AAQJlFjACsMzgpbxcfsLv0UcfLeWl1E2AAAECBAgQIECAAAECBAgQIJARASMAM9JRnQlz1113DdOnT0+GiY8aNaozp7RaZuLEiSE+bAQIECBAgAABAgQIECBQGYGNVgGuDLyrEqhSAQnAKurYurq6cOKJJ1ZRizSFAAECBAgQIECAAAECBAgQIECguwJuAe6uoPMJECBAgAABAgQIECBAgAABAgQIpFjACMAUd47QCBAgQIAAAQIECBAgQKA2BeIKwDYCBAgUS0ACsFiSGaxn5cqV4YUXXkgiHzhwYNhhhx0y2AohEyBAgAABAgQIECBAgAABAgQItCfgFuD2dKrwWENDQ7jsssvCyJEjw6BBg0JcOTg+Bg8eHEaMGBEuvfTSsH79+ipsuSYRIECAAAECBAgQIEAgOwJxEZC0PbKjJ1ICBFoKSAC2FMnw84MPPjiMHz8+PPfcc622YtWqVeGQQw4Jp59+eli8eHGyWnAcVp57LFmyJJx55plJmVjWRoAAAQIECBAgQIAAAQIECBAgkH0BtwBnvw+bWzB37twQVwJes2ZN82v5O5MmTQqxTNzq6+uTRN+oUaOScxYuXBjuvvvusG7dujBv3rwwZcqUMHPmzKSs/xEgQIAAAQIECBAgQIAAAQIECGRXQAIwu33Xpchj4u/WW29Nkn177bVXuOmmm8KwYcM2qWPp0qXh6KOPDgsWLEiOP/zww2G//fbbpIwnBAgQIECAAAECBAgQIFB6gaZ7tUp/EVcgQKBmBNwCXCNdPX369KSlffv2DXfeeec7kn/xYEwIzpkzJ/Tr1y8pO2PGjOSr/xEgQIAAAQIECBAgQIAAAQIECGRXQAIwu33XpcgffPDBZPTfqaeeGvr379/mudttt12YOnVqMi9gPMdGgAABAgQIECBAgAABAgQIECCQbQG3AGe7/zod/cqVK5OyBx54YIfnjBs3Llx88cVh+fLlHZbtboG67lbg/KIJbFNXX7S6VNQ9gccbXu1eBc4umkCfHr2LVpeKuiewd9iqexU4u6gCvw9ri1qfygoXmNLwVuEnO7OoApdttrGo9amMwMamBRttBAgQKJaAEYDFkkx5PQ0NDUmE7Y3+yzUhdwvw6tWrcy/5SoAAAQIECBAgQIAAAQIECBAgkFEBCcCMdlxXwx46dGhySmeSemvXvv2pfp8+fbp6GeUJECBAgAABAgQIECBAgAABAgRSJuAW4JR1SDHCueKKK8KAAQM2qaqu7u2bbRctWhTGjh27ybGWT5YtW5a8lBsJ2PK45wQIECBAgAABAgQIECBQWgGrAJfWV+0Eak1AArAKe3zatGltturee+8NkydPbvN4PDB//vzk+PDhw9st5yABAgQIECBAgAABAgQIECBAgED6BdwCnP4+6lKEjU0Txbb3mD17dli1alWbdca5AmfNmpWsGDxmzJg2yzlAgAABAgQIECBAgAABAgQIECCQDQEjALPRT52KcuPG7q88NmfOnNCzZ88wZMiQ0JkVgzsVmEIECBAgQIAAAQIECBAg0CUBqwB3iUthAgQ6EJAA7ACo1g4ffvjhIT5sBAgQIECAAAECBAgQIECAAAEC1SHgFuDq6EetIECAAAECBAgQIECAAAECBAgQINCqgBGArbJ4kQABAgQIECBAgAABAgQIVE7AKsCVs3dlAtUoYARgNfaqNhEgQIAAAQIECBAgQIAAAQIECBD4m4AEoG8FAgQIECBAgAABAgQIECBAgAABAlUs4BbgKu7cLDStITRmIcyaiHHr4MdBWjr643X90xJKzcfx+7q3at4gLQCD19elJRRxNAksrfcZclq+EW7YrFdaQqn5OLapeQEAxRawCnCxRdVHoLYF/PVW2/0f7rvvvnDwwQeH8ePH17iE5hMgQIAAAQIECBAgQIAAAQIEqlPAkJ/q7NdOt+rll18Oc+fODXV1RlZ0Gk1BAgQIECBAgAABAgQIECBAgECGBCQAM9RZQiVAgAABAgQIECBAgACB2hCwCnBt9LNWEiiXgARguaTLcJ3hw4d3+Spr1qxpPqfl+XFU4JIlS5qP2yFAgAABAgQIECBAgAABAgQIEMiegARg9vqszYiXLVuW3Mrb2Nj1hTXiOfH8/K07twWvWLEivyr7BAgQIECAAAECBAgQIECAAAECFRKQAKwQfCkv26dPn7Dtttt26hJxBOCf/vSnJHE4ZMiQTp3TmUI77rhjZ4qFqTsd06lyChEgQIAAAQIECBAgQKCWBKwCXEu9ra0ESi8gAVh647JdYejQockovvXr14dTTjklnHPOOaFnz/a7eObMmeH4449PYly6dGnZYnUhAgQIECBAgAABAgQIECBAgACB8gj0KM9lXKUcAr/97W/DmWeeGdatWxfOO++8sOeee4Z58+aV49LvuMby5ctDZx7vONELBAgQIECAAAECBAgQIBDiIiBp+0+3ECCQXYH2h4dlt101GfkWW2wRvv/974fPfOYzYcqUKeGJJ54IBx10UDjxxBOT1/v27Vs2l8GDB5ftWi5EgAABAgQIECBAgAABAgQIECDQtoARgG3bZPbI3nvvHR555JEk6bfllluG66+/PowcOTJcc801mW2TwAkQIECAAAECBAgQIECAAAECBAoTkAAszC31Z/Xo0SO5HTjeFnzooYeGV199NUydOjUccMABIb5mI0CAAAECBAgQIECAAIH0CjQ2bgxpe6RXS2QECHQk4BbgjoQyfjyu7HvHHXeEG2+8MXzxi18MDzzwQNhnn33CGWecES644IKKt65/qK94DAJ4W2DYBp8HpOV74bc9N6QllJqP482wseYN0gLw856vpyUUcTQJDAtbckiJwFtNM4TZCBAgQIAAAQIdCXjH35FQlRz/9Kc/HZ555pkwefLksGHDhuT24N122y3cfffdVdJCzSBAgAABAgQIECBAgAABAgQIEGhNQAKwNZUqfW2bbbYJV199dbjvvvvCe9/73vD888+H6667rkpbq1kECBAgQIAAAQIECBDIrkDTDcBN9yKk65FdTZETICABWIPfAx/5yEfCk08+Gc4999xQX+8W3Br8FtBkAgQIECBAgAABAgQIECBAoIYEJABrqLPzm9qrV69w0UUXhTffbJrhauPG0NDQkH/YPgECBAgQIECAAAECBAgQIECAQJUIWASkSjpSMwgQIECAAAECBAgQIECgegQaGy3yUz29qSUEKi8gAVj5PqhYBCtXrgwvvPBCcv2BAweGHXbYoWKxuDABAgQIECBAgAABAgQIECBAgEBpBNwCXBrX1NYab/W97LLLwsiRI8OgQYPC6NGjk8fgwYPDiBEjwqWXXhrWr1+f2vgFRoAAAQIECBAgQIAAAQIECBAg0DUBCcCueaW69MEHHxzGjx8fnnvuuVbjXLVqVTjkkEPC6aefHhYvXhzikPL8x5IlS8KZZ56ZlIllbQQIECBAgAABAgQIECBQGYG0rQAc47ERIJBdAbcAZ7fv3hH53LlzQ11dXVizZs07jsUXJk2aFGKZuMXVf2MycNSoUck5CxcuDHfffXdYt25dmDdvXpgyZUqYOXNmUtb/CBAgQIAAAQIECBAgQIAAAQIEsisgAZjdvutS5DHxd+uttybJvr322ivcdNNNYdiwYZvUsXTp0nD00UeHBQsWJMcffvjhsN9++21SxhMCBAgQIECAAAECBAgQIECAAIFsCbgFOFv9VXC006dPT87t27dvuPPOO9+R/IsHY0Jwzpw5oV+/fknZGTNmJF/9jwABAgQIECBAgAABAgTKK5A/XVNa9ssr4GoECBRTQAKwmJopruvBBx9MRv+deuqpoX///m1Gut1224WpU6cmcwPGc2wECBAgQIAAAQIECBAgQIAAAQLZFnALcLb7r9PRr1y5Mil74IEHdnjOuHHjwsUXXxyWL1/eYdnuFvhz2NDdKpxfJIHBdb2KVJNquiuwsvGt7lbh/CIJvNSwukg1qaa7Ag2NG7tbhfOLKLBd/eZFrE1V3RHYc0N9d053bhEFfhze/nu7iFWqigABAgQIFE1AArBolOmuqKGhIQmwvdF/uRbkbgFevdob35yJrwQIECBAgAABAgQIECinwMZGq+6W09u1CFS7gFuAq72H/9a+oUOHJnudSeqtXbs2KdunT5+/ne0LAQIECBAgQIAAAQIECBAgQIBAVgWMAMxqz7UT9xVXXBEGDBiwSYm6urrk+aJFi8LYsWM3OdbyybJly5KXciMBWx73nAABAgQIECBAgAABAgQIECBAIDsCEoDZ6atORzpt2rQ2y957771h8uTJbR6PB+bPn58cHz58eLvlHCRAgAABAgQIECBAgACB0gg0BrcAl0ZWrQRqU8AtwFXW7x0tDz979uywatWqNlsd5wqcNWtWsmLwmDFj2iznAAECBAgQIECAAAECBAgQIECAQDYEjADMRj91KsqNG7u/QuKcOXNCz549w5AhQ0JnVgzuVGAKESBAgAABAgQIECBAgAABAgQIVExAArBi9Om88OGHHx7iw0aAAAECBAgQIECAAAEClROId3fZCBAgUCwBtwAXS1I9BAgQIECAAAECBAgQIECAAAECBFIoYARgCjtFSAQIECBAgAABAgQIECBQ2wIbLQJS298AWk+gyAJGABYZVHUECBAgQIAAAQIECBAgQIAAAQIE0iQgAZim3hALAQIECBAgQIAAAQIECBAgQIAAgSILuAW4yKCq65rAK41vdu0EpUsm8PBmDSWrW8VdEzhk/bu6doLSJRP4ff0WJatbxV0T+EPj6q6doHRJBRY1/KWk9au88wJnDfD7u/NapS353eWvlfYCaq85AYuA1FyXazCBkgoYAVhSXpUTIECAAAECBAgQIECAAAECBAgQqKyABGBl/Yt69eHDh4fDDjsszJw5M7z11ltFrVtlBAgQIECAAAECBAgQIECAAAEC2RRwC3A2+63VqJctWxaee+65cNddd4WtttoqHHvsseHEE08MBxxwQKvlvUiAAAECBAgQIECAAAEC6RTY2NiYzsBERYBAJgWMAMxkt7UfdJwrYtWqVeHaa68NBx10UNh5553DhRdeGJ599tn2T3SUAAECBAgQIECAAAECBAgQIECg6gSMAKy6Lg3hhBNOCHPnzg0vvvhi0ro4MvCiiy5KHmPHjg0TJ04Mxx13XOjTp0/JWr9ixYqS1a1iAgQIECBAgAABAgQIECBAgACBzgvUNY0WM664816pLtmjR49QV1cXnnrqqbDrrruGe+65J1x//fXh1ltvDW+88UYSezwet969e4cJEyYktwgfeuihIZ5bzC13nY7qPHrIER0VcbxMAlvV1ZfpSi7TkcBH1lt5tiOjch3/ff3Gcl3KdToQsApwB0BlPvyXjeYaLjN5m5e7pr9VgNvEKfOBj1oFuMzi7V/uD6883n6BDBzd9t27pC7K11YvTl1MAiJAoHMCxc36dO6aSpVBICb0Pvaxj4UbbrghrFy5svl24HjpmPN98803w09/+tNwxBFHhEGDBoUvf/nL4cknnyxDZC5BgAABAgQIECBAgAABAgQIECBQTgEjAMupXeJr5Y8AHDVqVKtXW758efjRj36UJAYXLVrUXCY3Ym/33XcPkyZNSm4jHjBgQPPxru509hbgM/Y/ratVK18iASMASwRbQLVGABaAVqJTjAAsEWwB1RoBWABaCU8xArCEuF2s2gjALoKVsLgRgCXELaBqIwALQOvEKUYAdgJJEQIpFZAATGnHFBJWZxKA+fU++uijYcaMGWHmzJnhz3/+c3IolwjcbLPNkhGEt99+e/4pRd8/ZqcJRa9ThYUJSAAW5laKsyQAS6FaWJ0SgIW5leIsCcBSqBZepwRg4XbFPlMCsNiihdcnAVi4XSnOrIYE4Nbv3rkUNN2q86+rl3TrfCcTIFA5AbcAV86+4lfed999w2WXXRZeeumlcMstt4Sjjjoq1NfXJ7cIb9iwIcyZM6fiMQqAAAECBAgQIECAAAECBAgQIECgewISgN3zq4qze/bsGY488sgwa9asJBl4+eWXh9GjR1dF2zSCAAECBAgQIECAAAECBAgQIFDrAj1rHUD7NxXYdtttw+c///nksXixFZ421anuZ3+yomNqOvinm61NTSy1Hsj2YctaJ0hN+19reDM1sQiEQJoE+p+8a5rCqelYjvneqzXdfo0vvkBcvNFGgACBYgkYAVgsySqsZ5dd0rfsfBUyaxIBAgQIECBAgAABAgQIECBAoKQCEoAl5VU5AQIECBAgQIAAAQIECBAgQIAAgcoKuAW4sv5FvfrSpUuT+gYNGlTUelVGgAABAgQIECBAgAABAuUV2OgW4PKCuxqBKheQAKyiDt5pp52qqDWaQoAAAQIECBAgQIAAAQIECBAgUAwBtwAXQ1EdBAgQIECAAAECBAgQIECAAAECBFIqYARgSjumHGGtXLkyvPDCC8mlBg4cGHbYYYdyXNY1CBAgQIAAAQIECBAgQKADgcZgFeAOiBwmQKALAkYAdgGrGoo2NDSEyy67LIwcOTLEuQJHjx6dPAYPHhxGjBgRLr300rB+/fpqaKo2ECBAgAABAgQIECBAgAABAgQINAlIAFbRt8HBBx8cxo8fH5577rlWW7Vq1apwyCGHhNNPPz0sXrw4NDZNKpv/WLJkSTjzzDOTMrGsjQABAgQIECBAgAABAgQIECBAIPsCbgHOfh82t2Du3Lmhrq4urFmzpvm1/J1JkyaFWCZu9fX1SaJv1KhRyTkLFy4Md999d1i3bl2YN29emDJlSpg5c2ZS1v8IECBAgAABAgQIECBAoLwCVgEur7erEah2AQnAau/hv7UvJv5uvfXWJNm31157hZtuuikMGzZsk9YvXbo0HH300WHBggXJ8Ycffjjst99+m5TxhAABAgQIECBAgAABAgQIECBAIFsCbgHOVn8VHO306dOTc/v27RvuvPPOdyT/4sGYEJwzZ07o169fUnbGjBnJV/8jQIAAAQIECBAgQIAAgfIK5E/XlJb98gq4GgECxRSQACymZorrevDBB5PRf6eeemro379/m5Fut912YerUqcncgPEcGwECBAgQIECAAAECBAgQIECAQLYF3AKc7f7rdPQrV65Myh544IEdnjNu3Lhw8cUXh+XLl3dYtrsFGpoWIrGlQ2Bgjy3SEYgowntCPYWUCDzR8FpKIhHGZnU+s0zTd8FOm22VpnBqOpb6T51Z0+1PU+Ov/trH0xROzcfyrZoXAECAAIFNBSQAN/Wo2mcNDQ1J29ob/ZdrfO4W4NWrV+de8pUAAQIECBAgQIAAAQIEyijQGAyWKCO3SxGoegEfp1d9F7/dwKFDhyY7nUnqrV27Ninbp0+ft0/2fwIECBAgQIAAAQIECBAgQIAAgcwKGAGY2a5rO/ArrrgiDBgwYJMCdXV1yfNFixaFsWPHbnKs5ZNly5YlL+VGArY87jkBAgQIECBAgAABAgQIECBAgEB2BCQAs9NXnY502rRpbZa99957w+TJk9s8Hg/Mnz8/OT58+PB2yzlIgAABAgQIECBAgAABAqURiCv/2ggQIFAsAbcAF0syJfV0tDz87Nmzw6pVq9qMNs4VOGvWrGTF4DFjxrRZzgECBAgQIECAAAECBAgQIECAAIFsCBgBmI1+6lSUGzdu7FS59grNmTMn9OzZMwwZMiR0ZsXg9upyjAABAgQIECBAgAABAgQIECBAoPICEoCV74NURXD44YeH+LARIECAAAECBAgQIECAQOUE3AJcOXtXJlCNAm4BrsZe1SYCBAgQIECAAAECBAgQIECAAAECfxOQAPStQIAAAQIECBAgQIAAAQIECBAgQKCKBdwCXMWdq2kECBAgQIAAAQIECBAgkE0BawBns99ETSCtAkYAprVnxEWAAAECBAgQIECAAAECBAgQIECgCAJ1TROL+mChCJCqqE2BFStWhB133DFp/PLly8PgwYNrEyIFrdYXKeiEv4WgL/RFegTSE4l/F+npixiJ/khPf+gLfZEeAZEQIECgugWMAKzu/tU6AgQIECBAgAABAgQIECBAgACBGheQAKzxbwDNJ0CAAAECBAgQIECAAAECBAgQqG4BCcDq7l+tI0CAAAECBAgQIECAAAECBAgQqHEBCcAa/wbQfAIECBAgQIAAAQIECBAgQIAAgeoWkACs7v7VOgIECBAgQIAAAQIECBAgQIAAgRoXkACs8W8AzSdAgAABAgQIECBAgAABAgQIEKhuAQnA6u5frSNAgAABAgQIECBAgAABAgQIEKhxAQnAGv8G0HwCBAgQIECAAAECBAgQIECAAIHqFpAArO7+1ToCBAgQIECAAAECBAgQIECAAIEaF6hrbNpq3EDzCRAgQIAAAQIECBAgQIAAAQIECFStgBGAVdu1GkaAAAECBAgQIECAAAECBAgQIEAgBAlA3wUECBAgQIAAAQIECBAgQIAAAQIEqlhAArCKO1fTCBAgQIAAAQIECBAgQIAAAQIECEgA+h4gQIAAAQIECBAgQIAAAQIECBAgUMUCEoBV3LmaRoAAAQIECBAgQIAAAQIECBAgQEAC0PcAAQIECBAgQIAAAQIECBAgQIAAgSoWkACs4s7VNAIECBAgQIAAAQIECBAgQIAAAQISgL4HCBAgQIAAAQIECBAgQIAAAQIECFSxgARgFXeuphEgQIAAAQIECBAgQIAAAQIECBCQAPQ9QIAAAQIECBAgQIAAAQIECBAgQKCKBSQAq7hzNa00Am+++Wa44oorwvjx40P//v1Dr169wsCBA8Nhhx0WbrzxxtJctMZqffnll8Ptt98ezjvvvPDxj3889OvXL9TV1SWPSZMmdVljzpw54aijjgqDBw8OvXv3Tr7G5/F1W/sCxeiLjRs3hoULF4bp06eH0047Ley7775JP+T6dO7cue0H4WizQDH644033gg333xz+PznP5/0xbbbbhvq6+tD3759w5gxY8IFF1wQVq5c2XxNO60LFKMvnnnmmXDZZZeFiRMnhn322Sf52bT55puHd73rXWH48OHhU5/6VJg9e3ZobGxsPQivJgLF6Iu2KOO/l9gXuZ9XQ4cObauo15sEitEX8XdFzrujr7GsrXWBYvRFy5rvueeeEP8O22WXXZKfU1tvvXUYMWJEOOaYY8K0adPC6tWrW57iOQECBAikSKBnimIRCoHUC/zud78LRx55ZIhf87eXXnopxEdMKF133XVh1qxZ4d3vfnd+EftdENhuu+26ULrtojHxNHXq1HDNNddsUuiFF14I8XHrrbeGU045JVx55ZWhRw+fh2yC9LcnxeiLH/3oR8kbhtbq91rXBLrbH08++WTYf//9W32T9uqrr4aHHnooeVxyySXhqquuShJQXYuwdkp3ty+i1De/+c3w4x//uFW0pUuXhvj4yU9+Eg488MDk90pM0treKVCMvnhnrW+/Ej+Iiv1g65xAKfuicxEolRMoZl+89tpr4aSTTko+kMjVn/u6atWq8Ic//CH5GRU/RNprr71yh3wlQIAAgZQJSACmrEOEk16B+EnqIYccEpYvX54EeeyxxyajNuLovxdffDHMmDEj/PSnPw2/+MUvwqc//elkBFt6W5OdyIYMGRJ23XXXxLWrUf/bv/1bc/Jv7733DmeffXbYeeedw5IlS8J3v/vd8Otf/zpcffXVyUjOiy++uKvV11z5Qvsif/RSHGm2++67h/Xr14ennnqq5gyL2eBC+iO+UcuN0IiJwMMPPzx88IMfTEb/vfLKK8nIwP/+7/8OsdxnPvOZ0KdPn2QUbjHjrsa6CumL6NCzZ8+w3377JUnZ+O9i++23T34exTfbixYtSj6cePrpp8Mvf/nLcMQRR4Rf/epXPqzo4Buo0L5ordr4O+I//uM/QhyVGX92vf76660V81obAsXoi7vuuiu5y6KNSySjZts65vW/C3SnL/76178mf/8+/vjjSYXxDoo44i/+PbXZZpslfxfHn1Hxw28bAQIECKRcoOmNmY0AgU4I/PM//3O8Byt5nH/++a2e0TRSoLlMUzKw1TJe7FggOt52222NTbchJoWbRl80uzbdKtdxBU0lmkZpNja9uU7Oa0pwNDbdxrXJeWvWrGmMr8c+jeWaPr3e5LgnbwsUoy8efvjhxv/8z/9snD9/fuPatWuTiuO/ody/p/vuuw93JwW62x8PPPBA43HHHdf429/+ts0rNo2MbWy67S7pn6Y3eI1NI2nbLFvLB7rbF9GuKRHeLuGGDRsaP/nJTzb/W2m6Hbjd8rV6sBh90dIu2n/gAx9I7C+66KLGnXbaKdmPX21tCxSjL5rupGj+no+//22FCRSjL+KV/+mf/inpj6YpVBrb+xkUf1d09DOtsJY4iwABAgSKJWAEYMoTtMJLh0BDQ0O44YYbkmCa/vgPX//611sNrOmPrRDno3n++efDt7/97eQT0lYLerFdgQsvvLDd4505GEdtNL2BS4r+13/9V9hiiy02OW3LLbcM8fV4u0osF295vPzyyzcp40kIxeiL0aNHh/iwdV+gu/0xduzYEB/tbXGag6akUzKaI46WjaOg4vx0tk0FutsXsbY4ArC9LY6uOeuss5KRmbHcvHnzwoQJE9o7pSaPFaMvWsJdeumlIY54GjlyZDjnnHOaR5O3LOf5pgKl6ItNr+BZZwWK0Rdx1HGcxiNu3/jGN9r9+RPna+zoZ1pnY1eOAAECBEojYNKr0riqtcoE4twm8RaIuMXbgOObsta2+Ho8Hrf4xqHpk+vWinmtxAJNn5A0z1MTbx/+0Ic+1OoV4+vxzV3cTLTfKpEXa1Rg3LhxzS2PSUBb5QS22mqr5ovHRahspRd47rnnkkWo4pV++MMfJot9lf6qrkAgfQJxkaK4xcU+vvCFL6QvQBERIECAQJcEJAC7xKVwrQr8+c9/bm56R5Mq5x+PozVs5ReIidc4L2Pc4uT57W2543FRkGXLlrVX1DECNSPw1ltvNbe1rQ88mgvYKalA/ury8QMNW+kF4mrlTdNEhKZbH8NBBx1U+gu6AoEUCqxbt675w9T44XacCzNu8a6YOB92/JvJhxIp7DghESBAoB0BCcB2cBwikBPIX9E3NxIwd6zl1/zjCxcubHnY8zII5Lt39IY5//gzzzxThuhcgkD6BeKE7rltt912y+36WiaBP/3pT6Fpzsxw8sknJysFx8v269cvWZilTCHU7GViwvXnP/952HbbbcMPfvCDmnVIS8PjyrNxsbVevXol/wbiyP1zzz03xA/tbKUV+M1vftOc4IuLFMXFoc4444ykH+KiIsOGDUtGBsbk4Ny5c0sbjNoJECBAoCgC7U8+U5RLqIRA9gV22WWXZAXAuHLp/fff326D8o/HuQBt5RdYsWJF80UHDx7cvN/azo477tj8cm6F5+YX7BCoQYH4pu+OO+5IWh7f9EkAluebII40y0+85l81Jv9uueWWsM022+S/bL/IAnH15ZjgiFucx7d///5FvoLquiqQn1iKd2PER9PCUklyNs71+7nPfa6rVSrfSYH8D1ObFvhIVoyPU+Lkb3GU4D333BP+7//+L3zrW99K5svMP26fAAECBNIlYARguvpDNCkVeNe73hUOPvjgJLonn3wy/O///m+rkcbXn3rqqeZjr7/+evO+nfIJ5Lvnj95sLYLYt7lt9erVuV1fCdSkQLz195RTTklu8YoA3/zmN2vSIU2N/td//dcQRyd/+MMfTlNYVRlLXHDlj3/8Y7I41JQpU6qyjVlp1PDhw8OXv/zlZDGiRx55JMRHHJ157LHHhrjYRLz19NRTTw1XXXVVVpqUuThfffXV5pi/853vhJj8O/TQQ5O+iP4vv/xymDZtWjIKMM69/JWvfKX5luHmE+0QIECAQKoEjABMVXcIJs0CF1xwQfIJZ1wxduLEiSFOjH/iiSeGHXbYIbz00kvh+uuvDxdddFFym0r8RDRua9euTXOTqja2/Dlp4m1D7W29e/duPqy/mins1KhAnOT9scceS1off84dccQRNSpR/mZfd911ybxz8Y30X/7yl6Qf4pvrOAn/s88+G66++uqQP8ds+SOs7ivG0fvXXnttsoppXPgjJplslRE46qijkr+zWvbBvvvuGz71qU+F22+/PVmpPN6V8cUvfjFZmXb77bevTLBVfNU4D2Zui39XxVt9o31uXtg4QjYmYd///vcn8y3HUYJf/epXk/5o2Xe5enwlQIAAgcoKGAFYWX9Xz5BAnHfmyiuvTN4cxD86v/71r4eddtopSfjFr/F5z549w7//+783typ/9cbmF+2UXCA3UXW8UC4Z29ZF8xc72GKLLdoq5nUCVS8Qb9+KSaa4xTfal19+edW3OU0NjPNpxTfS8bbrAw44IElsxBHnhx12WPKmO/ZJ/vQGaYo967HE3wNTp04NMfl6+umnhz322CPrTcp0/HHF2fYSSIcffnjzKs1vvPFGuOaaazLd3rQGn/+3VIwxjgLMJf/yY46jkz/5yU8mL8XRyvl3wuSXs0+AAAEClReQAKx8H4ggQwKTJ09O5p6Jn07n3zoaE38TJkwITzzxRDJHSq5JcRJxW/kF8hOvHd3Wm/8Jd0e3C5e/Ja5IoDwC8cONr33ta8nF4sI4cRGE/J9x5YnCVVoKxDfgcWTglltumay6efbZZ7cs4nkRBOKt7r/73e9CnBP2wgsvLEKNqii1QEzY5pKEbc2dWeoYqr3+/L+l4mi/vffeu80m/8M//EPzsUcffbR53w4BAgQIpEvALcDp6g/RZEBgn332CTfffHOItwLHW3/jCLNBgwaF3CelN9xwQ3Mr3ve+9zXv2ymfQP7CHx2NmMlf+CN/QZDyRetKBCorEOcuPe2005Ig4mjmu+++O1nlsbJRuXpOIC4Asv/++yf9Mnv27BBHoNfX1+cO+1oEgTiyKW4f/ehHw2233dZqjbkPi+LXOBdd3AYMGNA8P3CrJ3mxZALRvm/fviGumG1F4NIw5/9NlP93VWtXyy/7yiuvtFbEawQIECCQAgEJwBR0ghCyKRBH/eX/wZNrxeOPP57bDaNHj27et1M+gVGjRjVfbNGiRc37re3kH7faaWtCXqtmgZ/97GfJXKZx7qY4n2lcybGjN3rV7JHWtuVWo423O8aER+wrW/EEclNFxNGW8dHeFv2PP/74pMiBBx4oAdgeVomP5UYAlvgyNVt9/ofYDQ0N7TrkH49/H9sIECBAIJ0CbgFOZ7+IKqMC8Q+gODowbjE5OHbs2Iy2JNthx7m0Bg4cmDSio1uD4sTvcYujOIcOHZrs+x+BWhCIyb7jjjsuGc0cR9LEkX8777xzLTQ9c23MH+FkqoLMdZ+ASyAQR5nFZGzccr/vS3CZmq4yjggfMmRIYrBs2bJkjsy2QOLCeLkt/j1lI0CAAIF0CkgAprNfRJVRgTgR9fPPP59E/7nPfa7VyZIz2rRMhR1HBRx55JFJzHGE30MPPdRq/PH13AjAWN5oglaZvFiFAg8++GDybyQufhAn3L/rrrtC/miPKmxyZpsUpzGYP39+En98Q54/L1dmG5WywOPiHx09on3c4tdc2blz5yav+V/5Ba666qrmhFQciWkrjcDRRx+dVLxq1apkhHhbV8l9+B2Px0VBbAQIECCQTgEJwHT2i6hSKpA/CqNliPfee28444wzkpdHjBgRvvSlL7Us4nkZBWJf5Far+5d/+Zewdu3aTa4en8fX4xZvV8n13SaFPCFQhQILFiwI//iP/xjiXGZxoY877rgjfOADH6jClqa7Sb///e9D/L3R3vbXv/41nHDCCc2rmZ944ontFXeMQOYF4kizX//61+224/bbbw8XXXRRUmaLLbYIJ510UrvlHSxcIP5tlJvj+swzzwwxEdhyi3Nf55Lh8XdLa9PjtDzHcwIECBCojIBJGirj7qoZFXj/+98f4ifN8Q+cOFqmd+/eyYi/W265Jfz4xz8OcR6t97znPeEnP/lJ8x9MGW1qRcP+1a9+FRYvXtwcQ+42n/hCfH369OnNx+LOpEmTNnken8Qk7FlnnRW+/e1vh8ceeyyZRP+cc85JbnGMt6rESd9zbzJiufe+973vqMMLIRSjL6Jjyz6LSajcduedd4b4pi+37bLLLkYQ5DBafO1uf8Tv/bha41/+8pek5m984xvJCMCnn366xZX+/jROth8ftk0FutsXL774Yhg/fnzYc889wyc+8YkkCbv99tsnH0isXLkyPPDAAyGOKo/7cYu/f77yla9sGoRniUB3+wJj8QS62xfxd8G4cePCmDFjwhFHHJH8+8j9/Hn22WfDTTfdlDziKMy4ff/730+m8CheC6qnpu72RZSItwDHZGtcgfypp55K5raOf0vtscceSTIwjvybNm1agtanT59wySWXVA+glhAgQKAaBZp+gdoIEOikQNNomfgXZ5uPpqRgY1Nio5O1KdaWwMSJE9s0bs2/rXqa5mRsnDx5crt1nXzyyY2xnK11gWL1RWv91tZr8Zq21gW62x9NCxy0+++htT45//zzWw+mxl/tbl/cd999ne6Lpg+dGl9++eUaF2+7+d3ti7Zr/vuRplt/k/6KX21tC3S3Lzr772LLLbdsvPLKK9sOxJHG7vZFPmHThw+NTdOktPkzqylJ29g0tUT+KfYJECBAIIUCRgA2vduxEeiswNVXXx1+8YtfhEceeSS89NJLYfXq1SGuzhg/CT322GPDZz/72VBfX9/Z6pQrsUCPHj2SETRxDps4X9Cjjz6aTBrer1+/sO+++4Y4T+PHP/7xEkehegIECLxTYP/990/mXrznnnuSUcpxrr8//vGPIa70G0fSxMWMPvShDyUrzsayNgK1IBCnI4i3lMZ5L+Po/fi3VrwLYMOGDWHbbbdN7r6II2dPOeUUI5PL+A3xrW99K0yYMCEZ7Tdv3rykX+KtwfFui/h6nFIlzidrI0CAAIF0C9TFpGS6QxQdAQIECBAgQIAAAQIECBAgQIAAAQKFClgEpFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAhKAGegkIRIgQIAAAQIECBAgQIAAAQIECBAoVEACsFA55xEgQIAAAQIECBAgQIAAAQIECBDIgIAEYAY6SYgECBAgQIAAAQIECBAgQIAAAQIEChWQACxUznkECBAgQIAAAQIECBAgQIAAAQIEMiAgAZiBThIiAQIECBAgQIAAAQIECBAgQIAAgUIFJAALlXMeAQIECBAgQIAAAQIECBAgQIAAgQwISABmoJOESIAAAQIECBAgQIAAAQIECBAgQKBQAQnAQuWcR4AAAQIECBAgQIAAAQIECBAgQCADAv8PaeKIwehUTKcAAAAASUVORK5CYII=\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.heatmap(vol_df)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAQABJREFUeAHsvVvMZUd+l73Gie3xoX2enkncdnvcbne7k8mEL2HmCzBkJqBIHwgE4qSgAEJRuACkRIpIBEgcLhBwEUS44pAQwgUCCZGIKCAhRBgCwwwwE5jgU/vQbbudid0+td0+znT39z7L83td3t57v/uw1t6r9nqqVb3Wu47/eqrWWlW//a+qD13ZC41BAhKQgAQkIAEJSEACEpCABCQgAQnsIIFz5841d911106k7JlnnmmOHDmyE2kxEZsl8K2bvZ13k4AEJCABCUhAAhKQgAQkIAEJSEAC2yHwxX9/V/Nth79lOzdf8a5fe/5S8//+f8+seLanSeBdAgqAlgQJSEACEpCABCQgAQlIQAISkIAERkEA8e/It189irSaSAmUBK4q/3BdAhKQgAQkIAEJSEACEpCABCQgAQlIQAIS2C0CegDuVn6aGglIQAISkIAEJCABCUhAAhKQgARmELjcXGku7/2rKWCzQQLrEtADcF2Cni8BCUhAAhKQgAQkIAEJSEACEpCABCQggQETUAAccOZomgQkIAEJSEACEpCABCQgAQlIQAISkIAE1iWgALguQc+XgAQkIAEJSEACEpCABCQgAQlIQAISkMCACTgG4IAzR9MkIAEJSEACEpCABCQgAQlIQAIS6I7ApSuXG2JNoTZ7a2I7Jlv1ABxTbptWCUhAAhKQgAQkIAEJSEACEpCABCQggdERUAAcXZabYAlIQAISkIAEJCABCUhAAhKQgAQkIIExEVAAHFNum1YJSEACEpCABCQgAQlIQAISkIAEJCCB0RFwDMDRZbkJloAEJCABCUhAAhKQgAQkIAEJjJPA5eZKQ6wp1GZvTWzHZKsegGPKbdMqAQlIQAISkIAEJCABCUhAAhKQgAQkMDoCCoCjy3ITLAEJSEACEpCABCQgAQlIQAISkIAEJDAmAnYBHlNum1YJSEACEpCABCQgAQlIQAISkMCICdABmH81BWw2SGBdAnoArkvQ8yUgAQlIQAISkIAEJCABCUhAAhKQgAQkMGACCoADzhxNk4AEJCABCUhAAhKQgAQkIAEJSEACEpDAugQUANcl6PkSkIAEJCABCUhAAhKQgAQkIAEJSEACEhgwAccAHHDmaJoEJCABCUhAAhKQgAQkIAEJSEAC3RG4dOVKQ6wp1GZvTWzHZKsegGPKbdMqAQlIQAISkIAEJCABCUhAAhKQgAQkMDoCCoCjy3ITLAEJSEACEpCABCQgAQlIQAISkIAEJDAmAgqAY8pt0yoBCUhAAhKQgAQkIAEJSEACEpCABCQwOgKOATi6LDfBEpCABCQgAQlIQAISkIAEJCCBcRK43FxpiDWF2uytie2YbNUDcEy5bVolIAEJSEACEpCABCQgAQlIQAISkIAERkdAAXB0WW6CJSABCUhAAhKQgAQkIAEJSEACEpCABMZEQAFwTLltWiUgAQlIQAISkIAEJCABCUhAAhKQgARGR8AxAEeX5SZYAhKQgAQkIAEJSEACEpCABCQwTgKX95J9qboxAMeZV6a6WwJ6AHbL06tJQAISkIAEJCABCUhAAhKQgAQkIAEJSGBQBBQAB5UdGiMBCUhAAhKQgAQkIAEJSEACEpCABCQggW4J2AW4W55eTQISkIAEJCABCUhAAhKQgAQkIIGBEri81/2XWFOozd6a2I7JVj0Ax5TbplUCEpCABCQgAQlIQAISkIAEJCABCUhgdAQUAEeX5SZYAhKQgAQkIAEJSEACEpCABCQgAQlIYEwEFADHlNumVQISkIAEJCABCUhAAhKQgAQkIAEJSGB0BBwDcHRZboIlIAEJSEACEpCABCQgAQlIQALjJHDpypWGWFOozd6a2I7JVj0Ax5TbplUCEpCABCQgAQlIQAISkIAEJCABCUhgdAQUAEeX5SZYAhKQgAQkIAEJSEACEpCABCQgAQlIYEwEFADHlNumVQISkIAEJCABCUhAAhKQgAQkIAEJSGB0BBwDcHRZboIlIAEJSEACEpCABCQgAQlIQALjJHB5L9nEmkJt9tbEdky26gE4ptw2rRKQgAQkIAEJSEACEpCABCQgAQlIQAKjI6AAOLosN8ESkIAEJCABCUhAAhKQgAQkIAEJSEACYyKgADim3DatEpCABCQgAQlIQAISkIAEJCABCUhAAqMj4BiAo8tyEywBCUhAAhKQgAQkIAEJSEACEhgngcvNlebSXqwpYLNBAusS0ANwXYKeLwEJSEACEpCABCQgAQlIQAISkIAEJCCBARNQABxw5miaBCQgAQlIQAISkIAEJCABCUhAAhKQgATWJWAX4HUJer4EJCABCUhAAhKQgAQkIAEJSEACVRC4tGflpcp61GKzQQLrEtADcF2Cni8BCUhAAhKQgAQkIAEJSEACEpCABCQggQETUAAccOZomgQkIAEJSEACEpCABCQgAQlIQAISkIAE1iWgALguQc+XgAQkIAEJSEACEpCABCQgAQlIQAISkMCACTgG4IAzR9MkIAEJSEACEpCABCQgAQlIQAIS6I7A5b1LEWsKtdlbE9sx2aoH4Jhy27RKQAISkIAEJCABCUhAAhKQgAQkIAEJjI6AAuDostwES0ACEpCABCQgAQlIQAISkIAEJCABCYyJgALgmHLbtEpAAhKQgAQkIAEJSEACEpCABCQgAQmMjoBjAI4uy02wBCQgAQlIQAISkIAEJCABCUhgnAQuNx9qLu3FmgI2GySwLgE9ANcl6PkSkIAEJCABCUhAAhKQgAQkIAEJSEACEhgwAQXAAWeOpklAAhKQgAQkIAEJSEACEpCABCQgAQlIYF0CCoDrEvR8CUhAAhKQgAQkIAEJSEACEpCABCQgAQkMmIBjAA44czRNAhKQgAQkIAEJSEACEpCABCQgge4IXL7SNMSaQm321sR2TLbqATim3DatEpCABCSw8wSuXLnSfP3rX9/5dJpACUhAAhKQgAQkIAEJSGBxAnoALs7KIyUgAQlIQAKDJXD58uVW+HvnnXca1g8dOtR86EMfauNgjdYwCUhAAhKQgAQkIAEJSGAjBBQAN4LZm0hAAhKQgAT6IXDp0qUG0e8b3/hGg/cffxPwAkQAvOqqq9qoGNgPf68qAQlIQAISkEBdBC41H2qINYXa7K2J7ZhsVQAcU26bVglIQAIS2AkCCH0Ifoh8CH4R/vD8Yx3Rj+2IfmyL+BcxkKVBAhKQgAQkIAEJSEACEhgPAQXA8eS1KZWABCQggcoJIO4h+hER9ogIfRH5SF7EPtY5PvumiYHf8i3f0h7PsQYJSEACEpCABCQgAQlIYHcJKADubt6aMglIQAIS2BECiHiTwh/bIu5967d+a+v1h1cgAbEPcQ8BsIw5PmJgvAQ5Fq9AthskIAEJSEACEpCABCQggd0joAC4e3lqiiQgAQlIYAcIxHuvHN8PAS9dfhHsrr766vd5/E0mG0Evot6kEMix7OM6EQZZT8x5k9f0bwlIQAISkIAEJFAzAccArDn3tH0dAgqA69DzXAlIQAISkEDHBBDqpo3vF289BLpVuu7OEgO5LoHrcu/yPoqBHWeul5OABCQgAQlIQAISkMCWCCgAbgm8t5WABCQgAQmUBBDf6OaLxx/r8faLd166+XbhmTdLDMy92B8bsLHsItzF/ct0uy4BCUhAAhKQgAQkIAEJ9E9AAbB/xt5BAhKQgAQkMJMAohuiH+JfRLeym2+Xwt80IyIGcm8Cy9hR7st66RWoGDiNqNskIAEJSEACEpCABCQwPAIKgMPLEy2SgAQkIIEdJ4DAhsiH6Ed33/yNGEhAWEP4i+i2CRwR81hiD4ElsfQMLNcjBrI0SEACEpCABCQggRoIUM25fKWuic++WTWrAa82DpiAAuCAM0fTJCABCUhgtwggpiH44fGHkJZYjru3yvh+XVOaFAMjBEYMZHnx4sVWoLzpppvaLsJlN+Gu7fF6EpCABCQgAQlIQAISkMB6BBQA1+Pn2RKQgAQkIIEDCSCYIfoR4/EXUQ2xre9uvgcaOOcA7IsgGJvfeOON5ktf+lJ71mc+85nmuuuu2/cSLL0Cc96cy7tLAhKQgAQkIAEJSEACEtgAAQXADUD2FhKQgAQkME4C6eab8f0uXLjQfOUrX2lh/I7f8Tuaq6++up19txahLGIg3n5lwJORwH5EwtKjMYJgLWks0+W6BCQgAQlIQAISkIAEdoWAAuCu5KTpkIAEJCCBQRCIAIa3H0JY/k533xiJ19+kkJZ9NS1JAzHegRH/IgZGHEQI5LiIiDWlUVslIAEJSEACEtgdApeaDzXEmkJt9tbEdky2KgCOKbdNqwQkIAEJ9EYAAeyg8f3w+EvYJY+4iHowIEQMRPwr90UMRAhEEMy+MHEpAQlIQAISkIAEJCABCfRDQAGwH65eVQISkIAERkIAUYsuvkTWEb/wgov4VY7vV4p+Ect2CVPSxzLpmyUGRvwruwjn/F1iYlokIAEJSEACEpCABCQwBAIKgEPIBW2QgAQkIIHqCCDyRfhD5ELwS5dfRC28/SJyVZe4DgyOmMdynhgYoZTjym7CHZjgJSQgAQlIQAISkMAHCFxqrtrrAnzVB7YPeUNt9g6Z5ZhtUwAcc+6bdglIQAISWIoAQhYi37Tx/bhQvNlYbiMgpl28eLGdlZeZeYcSJsXAeAVGOGU/MSIq62U34aGkQzskIAEJSEACEpCABCRQKwEFwFpzTrslIAEJSGBjBBCq4u2HSBWhiiVi1aKeaxybwDW7Ctj27LPPNufOnWvFSe5zxx13NN/2bd/WHD58uKEb8lACtoXDpBCIjdlfso2wmvOGkhbtkIAEJCABCUhAAhKQQC0EhtMiqIWYdkpAAhKQwGgIIEJNCn9sizhVju+3DShvvPFG88wzzzRf+9rXWptiA8La+fPn24gnHSIgYiCi4La8E2NbuYzYx7ZpYiC2sh2vS46NEMhSMbAk6boEJCABCUhAAhKQgATmE1AAnM/HvRKQgAQkMEIC6ebLrL4IUAh+QxnfD3teeeWVVvh74YUX9nOHMQc/9rGPtRFh8MUXX2yef/751m4EQmKO+fZv//bmlltuGZSINk8MzL7kBYlGBETczL59EK5IQAISkIAEJCCBOQSuXNkbdmQv1hSw2SCBdQkoAK5L0PMlIAEJSGAnCCAuIfjh8RexjyXiH6H0Pls1waXXGvdbJmAHgt7TTz/djvOXc6+//vrmrrvuaoW/2Mr4f0eOHGnTwTm/+Zu/2QqCpA2PQeKHP/zh1isQMfDQoUO53CCWEfXCiCWR9JX78nfyJvsGkQiNkIAEJCABCUhAAhKQwIAIKAAOKDM0RQISkIAENk8AYWmym2+EPwSl0tNs89Y1rW3l+H6x4dZbb22Fv9tvv33fky8CYI6hizICH5GJS+IJiAfhW2+91Zw5c6aNN954Y3sM3YSHPHkI6ZomBkYITH5FEAwHlxKQgAQkIAEJSEACEhg7AQXAsZcA0y8BCUhgpAQQjSaFP7ZFTBri+H4IXB/96Edb4W9Zr71rrrmmOXr0aBvpIhwxkFmDiadPn24jwiJCIN2JOWcogbQTWCICEsirSc/A5F/EwHQTbk/wPwlIQAISkIAEJCABCYyUgALgSDPeZEtAAhIYI4GIRXjDbWN8v4hYs9hj37Tx/RAj77zzzrZb77XXXjvr9IW302342LFjzb333tu89tpr+2IgXoEvv/xyGx9++OFBzyRMYhH3YFbGUgBkHW/OeASyPCgPFobogRKQgAQkIAEJVElgb2qxhlhTqM3emtiOyVYFwDHltmmVgAQkMFICCETTxvdDHIqnWESiTSHCpgSEKsbqY2w+BLkEuuMyvh8eeYhdXQfSftNNN7Xx/vvvb4U/xgt87rnnWu/IyZmE6UpMl+MhBdIQUW9SCMRO9rF9Wl7nvCGlR1skIAEJSEACEpCABCTQBwEFwD6oek0JSEACEhgEAYQfuvni8cd6PMLiJbbtbr7YhuB27ty55u23395nxgy9d999dyu2bUqk4j633XZbG0+dOtUwwzC2Tc4kTLfgO+64Y9/WIa2QhvAqxUDEPwIir2LgkHJMWyQgAQlIQAISkIAENkVAAXBTpL2PBCQgAQlsjAACX7r5sh7hD/EHEWjbwh8gnnzyydbTDtsICFerju/XXqDD/2B0+PDhNuI5iUcgYwa++OKLLVeEwQTSgVjJRCJDChEDyXNCBEF4l/vCHw9L0p19Q0qLtkhAAhKQgAQkIAEJSGBdAgqA6xL0fAlIQAISGASBeHbhVZfx/fD8QuAphb9tCTzY8Oqrr+6zQlAjdD2+3/4NOlqJfYxBiJfib/3Wb7Uei+mq/PTTTzdEJiWhizCTh9Q4kzD5k7KBEKgY2FEB8jISkIAEJCCBgRG4dOWqhlhTqM3emtiOyVYFwDHltmmVgAQksIMEEG4Q/PD4Q+xLLMd82+ZMsNgzbXy/D3/4w63nXF/j+/WR1UxAwkzCeAd+/vOfb2/BhCLMKowg+Oijj7aRmYQRA/FoHPpMwpQfIvkUAbBcRwjcZvnpIx+9pgQkIAEJSEACEpDA+AgoAI4vz02xBCQggZ0ggGiD6IfHH4INf8fjDyFn2918Z43vF/jf/d3f3SCedR3gQPo3FX77b//tbR7QLRivRrwEM5PwQw891HzkIx9pJzFBNOxjIpNV0xlGLGFWxlIAZD1ictlNeNX7ep4EJCABCUhAAhKQgAS2QUABcBvUvacEJCABCaxMADEGcY2IaBOBhnW8tbYt/OENx6QeiGHYSkBkQgCje+z/+T//Z39bu1L5f6QtMwmfOHGieemll9q001UYz0y8H4mIZ3gE4vHITMLk1VACaSASSiEw5Sv7Iwxie2LOG0patEMCEpCABCQggfkE9nz+m8vNcOoh8619dy82GySwLgEFwHUJer4EJCABCfROACEGMQ2PPyIiE3/ffPPN+90zEf4i1PRu0MQNsO/ChQvNM88805w/f35/b8bPO3LkSEP32XKm3/2DdmgF/oh7RGYShgWegSzJL9aJdAtGDKWbMHk4JBGtLEOlGIj4R0DYJDBjMh6cEQJZDikdrZH+JwEJSEACEpCABCQggW8SUAC0KEhAAhKQwGAJIMBMju+HiPYbv/Ebrc10P2UsvW0JL4hC08b3YxKMu+66qxW5EAETSjtJ2y4HBDE8/ojkITMJI/5lJuFMHgIrhEA8A4c6kzD5FDHwkUceadNDF27KHtsjDpLmjBdY5vUu57Npk4AEJCABCUhAAhKog8B7rZI67NVKCUhAAhIYAQEElXTzZR2RBQ+yrAdBxJb8vanlrPH9brnlllb4u+OOO7YmSm6CwbLiZTwhM5Mw3aOJeE2++eabzRNPPNFGuhIjBBIR14YUEPRKUQ+xL+Jf9uVv7C7HCyzPG1KatEUCEpCABCQgAQlIYDwEFADHk9emVAISkMDgCSDyRfiLmMI21hFcrr766veJMJtOEGIV3Xynje+Hxx8ClmE+AbpC33PPPW18/fXXW5Z4BjJ24quvvtpGZhOmiy1CIF2FyfehBMoiAVGPMklgW8prKQZmneOI+bs9yf8kIAEJSEACEtgKAcbTu1TZmHqOAbiVorJzN1UA3LksNUESkIAE6iKAcILIx9h+EftYlt0qI6CQMkSUhIgx+buPJfeYNb4fXVcZ329Rb7XS9j5sre2aN9xwQ3Pfffc1x44da4U/hEDG2KObN+M8EjOTMKyZURjPuiGEUsxjPWWRJZHym2PKdcrytjxXh8BNGyQgAQlIQAISkIAEtkNAAXA73L2rBCQggdETQCSJtx8CCTHCH8LJLKGEfQkRXfJ3l0vsYfIKPP7wTEuYNb5f9i+67NP2RW0YynHkKZOBEE+ePNkKf4iBjBs4bSZhxEA8BCkj2wplOcSG/M2SvC1jKQCmnHNc2U14W+nwvhKQgAQkIAEJSKAk8NRTTzX/4B/8g+ZXfuVX2nowvTf4sfaP//E/3vyFv/AX2knQyuO7WKcnyHd+53c2Z86caS939OjR5uzZszMv/dnPfrb5/Oc/P3N/ucM693s0FADfY+GaBCQgAQlsgAACyKTwx7aIJIwXh7ATQWXSpFnbJ49b9W8EJ8QnhL9y1l7EqbvvvrvZ9fH9VuXW1Xnk7+RMwnS5njaTcMYL3ORMwotUIklDyumkEAgn9lHGU+ZZT8x5XfH0OhKQgAQkIAEJSGBRAr/8y7/c/PAP//D7fvxGnPtf/+t/tfFnf/ZnW2GQHhxdhr/21/7avvjX5XW91vsJKAC+n4d/SUACEpBATwTSzReBDVEE8SNdfhE/Mr7fMgLIImLMoslhfL9z58614h92EbDl8OHD7cQeju+3KMnujsNDjjEAiYjGeAQiBmYmYX6hJl5//fX7k4f0PZPwsmWOMpQyXYqBKWOUfbbzN8dFCGSZ87oj6pUkIAEJSEACErh05aqGWFPYhL2//uu/3vyJP/En2gnaqE/95b/8l5vPfe5z7d//8l/+y+af/JN/0pw+fbr5/b//97di4KFDhzpByH3//t//++2QOrQHXnvttYWv+73f+73Nz//8zy98/NgPVAAcewkw/RKQgAR6JICwgeCHeBOxjyXiH6EUOxY1o0tRBPvo3vv000+3HmaxAS/EZcf3y7nzlqXt3NuwOAEqhIy3SHzrrbfasQLx1CT/+GV60zMJl3m5aCo4J+eR/4nxBGQf28rnI+MF5rxF7+VxEpCABCQgAQlIYBkCP/ZjP9aKfdSD/8N/+A/N933f9+2f/gM/8APN8ePHm5/8yZ9sRcCf/umfbv7G3/gb+/tXXaFd8KM/+qNtO+Gv//W/3vzcz/3cUgIg40nTddiwGAEFwMU4eZQEJCABCSxBABFjsptvhD+EDIS/CBtLXPYDh3KfVQICy7Tx/ZjMg9l86VpK5ccwTALk0z3fnEn44sWLrVcgnoHTZhJGyP3oRz/a+UzC6wpynE9MGWZJnCYG5pmJV+C69x5mrmqVBCQgAQlIQALbIvA//sf/aH7t136tvf2P/MiPvE/8i00/8RM/0XrbPfzww83P/MzPNH/1r/7VtetXXOfLX/5yc+LEieanfuqnWgEw93PZPQFbN90z9YoSkIAERksA8WJS+GNbRI2DxvdbBFwpfkQ8WeQ8jnF8v0VJ1XMcXVT4RZqxaPAGxCsQMZBZpSdnEkbYXXcm4WXL3EEkU55Z5tosiXlu2Feur+I5e5Ad7peABCQgAQlIYLwEfumXfmk/8X/2z/7Z/fVyhfrHn/7Tf7rtGvzKK680v/qrv9r84A/+YHnIUusM48LYf4R/+A//YXPNNdcsdb4HL09AAXB5Zp4hAQlIQAITBPDuQ3Dpcny/iVvs/xnBZH/DAiuzxvdDDGJij02N71faHrFnAfM9ZAECsM1MwvyKjPiHEPhbv/VbbbcSxg8kIkLjEZiZhMs8WeA2vR4SW1imfCD8HSQGduFN22vCvLgEJCABCUhgQAQuN3s/rO3FmkLf9v7X//pfWxx0qf2e7/memWi+//u/f3/ff/tv/20tAfDP//k/37z++uvNn/pTf6r57N6svob+CSgA9s/YO0hAAhLYSQKIEtPG90MMRMDo00spAklEklmAL1y40M7m+/zzz+8fglhy5513tmPJ0ZXUsHsEKHvM1kw8depU290bMZByQJl99tln23jttde2E4wgBiICR4CbRaQsbwcdO+sai27P9Smv3LeMpTcg6zxz5fOWcxe9l8dJQAISkIAEJDBuAnTrJdCjYt4wOCdPntwHlXP2NyyxwqQi/+7f/bvm1ltvbRhPcNXwyCOPNJ/+9KebRx99tB0jmrofAuYf+SN/pPmhH/qhtbsor2rXUM9TABxqzmiXBCQggYESQIigmy8ef6xHgIgo0UU330WTzv0nA3a88MIL7cQedAlNcHy/kBjXEgFtciZhugnjIfj222+3swhnJmGEQLoJ8+v3QWGTIhv3yv0mhUDsZB/bp4nvOe+g9LhfAhKQgAQkIIE6CPCj5kGBSdMWDUyuRt2ZcNB5CHbUk/Dce+aZZxa9xfuOe/nll5sf//Efb7f9nb/zd9rhWd53wBJ/pIdHTsmPvP/23/7b5u/+3b/b/Ot//a+bBx54ILtHv1QAHH0REIAEJCCBxQggrKWbL+tEBAeEB7yPNin8RfAoLc/4fufOnWt/Acw+uoUysQfdfbcthpT3nyZexmaX/RCYnEmYCjQxMwk//vjjDZEygxBIxEswYQh5RhlKOcKexIh/7GNb/ubZTBfhnJf0uJSABCQgAQmMkcDl5qpmz3e+qqRjc8KnPvWprM5cLlNnee211/avw9jKB4UIgEzEtkr4S3/pL7XDsjDLMDMArxKo3/ye3/N7mt/3+35f88lPfrK5/fbb29mDv/KVrzT/6B/9owbvxIceeqj53Oc+1zDBCUP+GJpGAdBSIAEJSEACMwlESMDjL+P7ISwg/rEvwl8pSsy8WA87sGHe+H4If4g5hv4I1Coq4RH68Y9/vI2ZSRjPQMoTXceJdCuhQokQyLiBCGlDCnnuUslnSeT5LPfxNwH7eWazb0hp0RYJSEACEpCABLZDAA/AhEUm4siPo9SZlg3/5b/8l+af/tN/2joOMPEHdZJVwr/5N/+mueWWWz5w6mc+85mGsQURFn/hF36hFRrxNuR4gwKgZUACEpCABKYQQERA8MPjD/EgcZpX0ZTTe9+UysKZM2daoSY3ROCgGyfdF6677rpsdimBuQTKmYQR/hACmTyE8v/iiy+2kV+RGVcmIWUwf29zGVtYHiQGcgwRIVAxcJu55r0lIAEJSEACqxHAo40fJ7sK5ZjY1H0OCgyhQli2rs15f+7P/bm2rvJjP/ZjzXd913cddKuZ+6eJfzmYHh8/+7M/23zxi19sxwb8xV/8xXbsZ8YAH3vQA3DsJcD0S0ACEigIIB5kfD9EP/6Oxx+iwSa7+RZm7a9iz/nz51ub2IhYQ6hlfD8YJkSoyd8ut0+A/KFCSWSQa8YJRAxkfBmeg3IyGboKHz16tLnttttW/vW6jxSnjLFMGWNJ5JlmO7FcRwhMN+E+bPKaEpCABCQgAQl0RwDx76Cx+pa526FDh/YPX6RbL+P/ERbpLrx/4b2Vv/W3/lYryNFD52/+zb9Z7up8nTbLj/zIjzQ/+ZM/2V7785//fPMn/+Sf7Pw+tV1QAbC2HNNeCUhAAj0QQAzgFz/EvwgFCB6sIw5sW/jDG5Gx2hhsuOymcP311zf33ntv65mFnQYJdEWA8pSZhCP+MbB0BslGDCTSDYaKOHGRmYS7sm+R60yKgREC84yXYiBp5O+ym/Ai9/AYCUhAAhKQQG0ELl3ZGwNwL9YU+rSXH9IZ8oReD4ylPS8wgUcEQIS8ZQKTchB+7+/9vc0v//IvTz0112bJTMGEw4cPNz/wAz8w9fh5G0+dOrW/mzqcwS7AlgEJSEACoyWACECjf9r4fkBBDED4i0iwDVCIfYh+eGFhawI2Yf+xY8fWmjks13MpgXkEEMUQ+JhI5j/+x//YHorYx+QhdGc5e/ZsGxkUm+Poho44PaRQPseTQiB2Zn88AxFAE9lnkIAEJCABCUhgdwkglv3ar/1aOxkaP7zTBpgWGB85YdnZddO9+Od//ucb4rzAD64/9EM/1B7y/d///SsJgNZfPkh4eq5+8Di3SEACEpDAjhCg8T/k8f3AjLDy9NNPt919sZdQju/35S9/ufVYbHdU9J8VkYoya4qpKYvsYtwayiRjBSJQM4Mev1YfNJPwlMtufFPEPm48Swxke7wCIwSytAxvPLu8oQQkIAEJSKB3Ar/rd/2uVgCkLkM9+9Of/vTUe9KVNuF3/s7fmdVBLhm/OYEfZw16AFoGJCABCYyGAJ49ePsREdgQARkkl0ijftvdfBEcGN8Pj7+M7Ufm0C2BcU74cE/+GlkKMrVlZM2218a6L3sZ/LqcSRghkK7q02YSpvwyk/BkGe7LtkWvO0sMjPjHfsoq7w8CIiDCZ3neovfyOAlIQAISkIAEhkngD/2hP9T87b/9t1vj8M6bJgBSF/jn//yft8cwXvLnPve5pRKzSN33nnvuaZ566ql2nGV6WKwaaOcw23DC7/7dvzuro17qATjq7DfxEpDAGAikm285vh/u+3gsMX4e43ds07OHD/S08f3oYnn33XdPHd8P8cEggU0TKCuuk2WQgbDvv//+5vjx480rr7zSlmnKNc9dZhJ+8MEH23Fs0p2Y525IIaJe0smSmG7BdMnnfcK4hwjz2J93xySPIaVLWyQgAQlIQAIlgb0psZrLzbC+waV909axuc/wqU99qvnMZz7TegH+3M/9XPNn/syfab7v+77vfbf86Z/+6ebhhx9utzGLL04EZfjP//k/74uCnP/P/tk/K3d3tv6rv/qrzW/7bb+tnbRt2kWpe/3oj/7ovq1/4A/8gba9M+3YsW1TABxbjpteCUhgFARotNNQZ6wNlvk7XjxprNN4x5tnGwExgYGG8ZpCBExgnDWEv5tvvjmbZi5JV20B9thdo+21sd6GveTvrbfe2kZmEkb8QwjMTMJ0GSZSacYjEM9Ajs8zuQ2bJ+8ZW1JW2U95PX36dJsOfjRg3J8IgxwXMZClQQISkIAEJCCB+gj8zM/8TEO3Xnoy/OAP/mDzV/7KX2kFPf5mQo5//I//cZsofvD8iZ/4ia0l8Bd+4ReaP/gH/2AbP/vZzzYnTpxoJ2JjBmO6L2Nnuv8ygQjpMrxLQAHQkiABCUhghwjQSOdXLyKNcyICYBrqEfzSSGf7psNB4/vRrfKgEIFCEe0gUu7vkkBZ3lIG512f5wxBm4jITRd3BG8GtuYZRQAn4k2HVyDx0KFDgxUDk9YIg3mv8He5nvfMIoxyTZcSkIAEJCABCWyXAF51/+pf/avmh3/4h9vhghAAJwPi36/8yq+09ZXJfZv8G7HvX/yLf9HGWff9xCc+0QqXDNdieJeAAqAlQQISkMAOEKDxPSn8sS2N8snx/dIwLwWNPjFwH0QPJvYox/ejKyHeRNPG95tnz6btn2eL+ySwCAGewYh8eObiBYhn4Msvv9zgDXvmzJk2MpMwzwPHDm0m4aQTgY/Ic52Yd03EQH54YB0PY47NM5truJSABCQgAQlIYHgE6C771a9+tfWaQ+jjh8prrrmmue+++5o/9sf+WPMX/+Jf3Hr95Kd+6qea7/7u727++3//762nHz+wvvTSS+0QJfSu+N7v/d7mj/7RP9r84T/8h7fW02l4OfuuRQqAQ80Z7ZKABCSwAIF088W7iIY4jfB0+aXRnQk+Jhvfk38vcKuVDpk3vh/CH55R2DmmAPuIJmNK9y6klXxLWOcZoiJNN3ci3WoQAomZSfixxx5riAywjRD4sY99rK3U5t7bWib9pD0RW1KeWfIOIrCfZzvCIOuJ67BrL+5/EpCABCQggTUIXL7yoebSXqwpYPOmwtGjR5u/9/f+XhuXuedn97rjpq6wzHnlsYtM/MEwJMQf//EfL091fQECCoALQPIQCUhAAkMiwIc1wl/EPpZpeKeRzXJWSAN83Y/0rOsfNL4fE3zEhlnXmLc95/Zl/7x7u08CXRKgyzuT8RARABEC6SbMM8RkIkQm7bn99ttbz0DGstnWTMJ53vL8hQN/ZxvHJPJeIvAuYht/c1z5jsp5uZZLCUhAAhKQgAQkIIF+CCgA9sPVq0pAAhLonAAN6MluvhH+0qimu90iDeocwzW7DIzv98wzzzTPP//8/i+A2IQXEx5/i4zvt4g9sX+RYz1GAl0RKJ+XPsog4/8RM5MwQiBdhXnu6UJPRDxDBKSb8B133NH+3VX6DrpO0j8v7ezLfo5PjCcg+9hW/mCR91bOO8gO90tAAhKQgAQkIAEJLE9AAXB5Zp4hAQlIYKMEaChPCn9sS4N6cny/RYxLQzsN+kXOmXUM10CYQPjDWykh4/sh/tEVuY/Qhf192DXvml2yn3cf99VLgDKSmYTp4sLzhWcgwjqifzmTMN2DecY2MZNwnreU4YMIcxwx57Ek5t2Vffk7noE576Dru18CEpCABCSwCoFLzVUNsaZQm701sR2TrQqAY8pt0yoBCVRFgIY+kwUsO77fIomkgU1Iw3yRcyaPwS6ECIQ/xjFLwIOJsc36HN+vC/tjr0sJLEqgfF5SBhc9d9Xj4vGH1x/PHCIgYiCiID8M8PwRM5MwnoE8g32EpH/ZtOd4lrkGS2LEP/aV6xEDWRokIAEJSEACEpCABNYnoAC4PkOvIAEJSKAzAjSIaeTTsEcA5O908+UmXTWK0yDn+suGeeP70c335ptv3u8CuOy1Fz0+9i96vMdJYBcI4O2LwEfMTMJ0E8bztpxJ+MYbb2y9AvEM7HIm4bwv1nn+ci5LrlfGUgAs151JeBdKr2mQgAQkIAEJSGDbBBQAt50D3l8CEpDAHgEawYh+NOpZp/Eb4Y+GMsJfxsnqAlga4dxr0TBvfL8jR450KjQsatMy9i96TY+TwCwCZXnLMzTr2L63lzMJv/HGG61XIJ6BFy9ebGM5kzCCIV2FOWedkPR3lXauk2tx7TKWAmDWyx9Act466fFcCUhAAhKQgAQkMCYCCoBjym3TKgEJDI4ADdt082WdGM8/GrurjO+3TCLToJ91Dvtnje+H6Iew0Nf4frNsYnsa/wfZP+8a29q3Kdthk3ttK63edzME8PI7duxYG5lJGK9AxMByJuGHH364nTQEr8BVZxLO89ZHueKauS73SeSdSGAf23g/sq4YuJmy5V0kIAEJ7CKBy1euaog1hdrsrYntmGxVABxTbptWCUhgEATSiMXjL+P7xduPfRH+ygZx14aXDe1p18YeBIRp4/vRzRcBATsNwyIQkWRYVu2ONTyfCXmG8vdQloz/d+LEieb+++9vXn755fY5zkzC58+fb4h4E/MMIwYuM5Nw0t932rl+7sE9EyP+sY9tEQd5F8VDOucNJT+0QwISkIAEJCABCQyFgALgUHJCOyQggZ0nQIMVwQ+Pv3j7xeOPRmvZiO0bRsQ7bCrD22+/3Zw7d6559tlnW1uzjwk9NjW+X+45b5lG/qT9885xnwTGRIBn5LbbbmtjZhLGMzAzCSPwE/HgpXsw3ry33HLLvvA2jVWetzx/047pehv3IubeLIm8O8t9EQPL8QI3aWfX6fZ6EpCABCQgAQlIoGsCCoBdE/V6EpCABCYI0FidN75f3918J8xp/0zDOI1qug0+/fTTrTiQbTSk8RDa1vh+0+zOtkn7s72GZc2218C3TxvzbHCP5GOf9+vq2gj+ePwRM5MwYuCLL774gZmEEQJ57qfNJJz0byPtuSfL2MGSOCkGcgyRdBPzd1c8vY4EJCABCUhAAhKokYACYI25ps0SkEAVBGiU4u2H+JdGKl3YWKdRug3hL+BoEBMYI+wrX/lKO4to9l177bWt6Let8f1ih0sJSKB7AuVMwnj80j0YT8DMJPzkk082RARAhEDidddd1xrCu4uQ90f7xxb+y/1ZxiaWxFIMLNd556ab8BZM9pYSkIAEJDAgApeaqxpiTaE2e2tiOyZbFQDHlNumVQIS6J0ADVBEvmnj+3FzGqw0wFmmEdu7URM3wD5mCiVcuHBhfy8N/lrG9wu7NP73E+GKBDZEIGVwQ7fr5TaI/UePHm1jZhLGM/D1119v8Aomnj59urn11ltbIRBBjTCktMcWlhEBsywFQNYzhmDZTbgXsF5UAhKQgAQkIAEJDJCAAuAAM0WTJCCB+gjQ4BzK+H6z6M0a349JABD+Dhr/a9Z1t7E9jf5t3Hvde8Z2xct1SW7+/F3Os8wkfO+997bCX8YIxEuYyUSICfxw8NGPfrT9MSPbhrDk2Sqfr1IIxL7sjzCYLsLpJjyENGiDBCQgAQlIQAIS6IuAAmBfZL2uBCQwCgI0JPH2I7JOgxMvkzQwt9nNNxmAFw+z+T733HP73eVoCGMrot93fdd35dDqlrssyFSXGSMwuCxvEZp2Ldmk66abbmpjZhLGK5CuwvzIQWC8UCYKQgSki/Dtt9/eDmswJBYR+7CJfEvk3UxA9GNbvAIVA1ss/icBCUhAAhKQwA4TUADc4cw1aRKQQH8EaDRG+KMRSaOSbazTkNy28IcdDPBPQ52xvRKuueaa1tuPsQkRBbG1xhDxhXQaJCCBfgjwnGUm4VOnTjX/6T/9p1YEZDvvO4RBIu8VZhJGDByiJ/E8MTD78h6HJO/FjBfIfoMEJCABCewWAX4KunSlrvf7uz9f7VY+mJrNE1AA3Dxz7ygBCVRKgAYijV7Es4h9LEuPkniRbCuJ2IOnDuIeY3ol3Hjjjc3dd9/dzgKKjWfOnGl31Sqg1dwoj+21sk+ZGvsy+TgWDuW77ROf+ET7YwfiHz808E7kxwYiE4YgBDKJEO+doQXyjZjnjyUxXtvZl7+T7pw3tPRojwQkIAEJSEACEliUgALgoqQ8TgISGC0BGofx9qNRiEcdA+PTIPzkJz/5Pm+RbUFadnw/bCekEbwtu9e9b+32r5t+z98sgbGXt6QfD+fDhw83d955Z8O7J+MFMjbgm2++2c4inJmEEQLxDsxMwpvNsdl3yzuQZdLFkhjxj33lesRAlgYJSEACEpCABCRQGwEFwNpyTHslIIGNEaDhVwp//E2kwcssujQCr7766v1B5zdmWHGjaeP7YRceOEzswcD+00Iav2n4TjtmyNtqt3/IbLVtNoHyeUkZnH307u0p05/UMZPwPffc00a8jvEKRBDMTMKPPvpoQ2QmYcRAxg2ky/CQQvKSZdLIu551lmwnluu8Z9NNeEhp0RYJSEACEjiYwOXmqoZYU6jN3prYjslWBcAx5bZplYAEFiKQbr4MeJ8GYLr8RvTjQuxLw3GhC3d0EPel2x3dfMuZOWlUHzlypPXKQZicF2I316oxxP6aba+VfY3MtbkbAimzs54/fnC47777mmPHjjWvvvrqvmcgP5pkJuGHHnqo+chHPtL+SIEXISLakELShl2kt4z88JOxU0+ePNkOBcE3ITHnDik92iIBCUhAAhKQgARCQAEwJFxKQAKjJkAjL8JfxD6WeHwQ0sBjmQZrGoabavRhz6zx/fD2w7MG+xYJsZk01Bxqt79m9mO2Pc/P2BjkeTso/ey/+eab23jixInmpZdeasXAzCT8/PPPN0TepTXNJIyQGQHw+PHj7Q9AMOHdTJrL78RBjMZWdkyvBCQgAQlIQALbJ6AAuP080AIJSGCLBGi8TXbzjfCXBh2N1LIxV65zfvl3H0mh0fnss8+2EVsT7rjjjrab7yqzbsbmNOhzzVqWsb8We7VzNwjU+rx0RT/pX+b549jbb7+9jcwkfP78+babMEvetZMzCdNNGPFwmXt0lb551yntidAHD9JAYFv+5tgcw7I8d9493CcBCUhAAhKQgAT6JKAA2Cddry0BCQyWAJ59k8If2zLGE4Pcz2q4sT0hDeL83eWS7mbMqvncc8+1DUuuzb0Z34+uvjfccMPKt0uDtE/7VzZuiRNrtr9m25fIop06dOx5lvTn/bFs5vL+wuOPyPuXdxvjBU6bSRghkHfdkGYSLtNPWvI3S2K+H/DJ3zDiRySOZ/uq7JZl7fESkIAEJDCbwKUrVzXEmkJt9tbEdky2KgCOKbdNqwQksN/Nd974fgc10soGHA2+dAnuAi+NRrrLIfytOr7fInYkDdyvxlCz/bG9Ru7a/C6BMeZh+a7oIv2MU8oPGcS33nqrHd4AMTAzCT/xxBMN8aabbmqFQMTAD3/4w1stgrzvCYh5hHBgGT4siZNiIMcQOVcxsMXnfxKQgAQkIAEJbJiAAuCGgXs7CUhg8wRojCH44XFCdy3+TjdfrEmDLI26gyxMo4/juFYXAXu6Gt9vEXuShq7sX+SeXR5Tu/1dstjGtWotN12xSvnr6no1XKfM867Tj7B3zzdnEmb2YIRAugYzqzCTiRCZSfi2225rxcCPfexj7Qzsm+YWBtPSn20scxxLYikGluvLfns2nV7vJwEJSEACEpDAbhFQANyt/DQ1EpBAQYCG12Q33wh/NNJofE2O71ecPnM1DT0OSENv5sEH7Jg1vh9jZt19993NKuP7HXDLdnfSsK79i9zLY95PoE/2r732WiskX3vtte34kOt0E3+/1bP/SnpmH7E7e3xe3s3LPvOcMlvOJIwQyI8jvCvxjiZmJmG6CTOjcJde2PNKazwAD0p/9rOkzJSxFADL9bKb8Dwb3CcBCUhAAhKQgARWJaAAuCo5z5OABAZLgEbVO++803r9sU6M5x+i37zx/RZJVOkpyLVXCYzvx2ySNGwjKnDdLsb3W8SeNFBz70XOGdIxtdvfJUvykDHU6Db+yiuv7F/67Nmzza233togkmzLY2rfmB1ZqfV56QJ/mfY8f11cd9Y1uEdmEj558mQr/CEGMm4gHt2ZSZj3eTmTcJ+2hUH5DZhlf7ZjT2zi/DLy/cj+rHPtxJyXa7mUgAQkIIFuCOy9fRtiTaE2e2tiOyZbFQDHlNumVQI7TIBGFSIfHn8Z3y/efuyjQUVDMY2tdVCUjTKuvWjg2Hnj+yHUXHPNNYtebq3jkoZl7F/rhh2fHPs7vmxVl0MwQEBG+KOrZAKTJvAc4DHFOJJEPKYOHz7cioF4TC0jYOS6Lt8jMMbyV74rNp1+7jc5kzDdhBEBed9nlnTen/yIwruUsQO7tjM/+Kx6Xc7LufBMLK/LNr5dHBchkGXOe68UuiYBCUhAAhKQgASWI6AAuBwvj5aABAZGgMZSOb4fDSli2YBapZvvvGSWDTHuf1DAFrxW8PhjfKsEhJq77rqr9V7ZtCCTNCxif+wd4rJG+9dlj7iH4HHu3LnW0zX5cscddzR33nlnO2sq5enNN99sBUJEQp4RyiCRyRfwCEQk6auLeWza1WXycFfTNy1d5bO2zfTzPqf8EnkWKNN4BvLjCp7fTz31VBuvv/76fTGwq67wYdDF+xqG4ch1E/PtYh/bIg5yz3zLct60fHKbBCQgAQlIQAISmEVAAXAWGbdLQAKDJkDDiMYfDb40kmg40ViicbRuN995iS8bf9x7VsA2RBrEGmxNwJMF4Y/umdtqyOW+8+yPvUNc1m7/KkyZKRURGbGDsk6AA0II40UiciD05RlgwgREwQceeKD1lOK8F154oS2LXIeISIIQSGTdMJ9Arc/L/FQttrdMe56/xc7s76jJmYTxCiQyaQheseVMwpRxnpV1ZhKOGNd1+rkeMYxZEvMsZ1/uX44X2LUt/eWWV5aABCQgAQlIYNsEFAC3nQPeXwISWIoADSCENQS1NJAQQ1hHmOtT+IuhZYMrDbLsY5nx/fBMyX5so/GJ8NeVN0p5z2XXyzQse+4Qjq/d/mUYMrEH3Xzp7kg5J1DO8fY7cuRIw4Qf8wJiAd0iiTw7mWH1woULrUjy+OOPN0S8ASOSbKor+jy73TcsAil7WDXE5w9h7+Mf/3gbeQdHDCxnEn7kkUfarsQ8C4wbiIC4TAiD8kegZc4/6NhwZZl7sSTyLWF79mUdW4j5+6B7uF8CEpCABJrm8pWrmkt7saaAzQYJrEtAAXBdgp4vAQn0ToDGDyIfoh8eTvk74hoNn67G91skMdwvAVsILOmChlcVywSEFIQa4pBElaQh9sfe2pY12h/281inPCH8MYZfAiIHIjICBmV+2UAZPHr0aBsjkuAZSHdhJhAhPvzww+3MqoiBjBvYl9ixrO1DOD7lbZE8HIK9XdqQtHPNoaef4RWOHz/eziaM0B0xEAGcCXOImUmYZ2nRmYTDYBPpzz1Y5r4siaUYWK7zrKabcJd577UkIAEJSEACEtgNAsu3HnYj3aZCAhKogAANHQQ/Gm00chIRA2kUbauxw73TKMM+BBSEv3J8P7z86JaJl8kQBRTsJ6RhWUFxeJ+Jtds/iz1lHE8/hD8EuoRDhw615anLCTxKkQThj67qGS8wM6w6XmBy4P3LlL/3b93tv8p3RS3px048W4knTpxof5zhfY13Nt8RlkTEdN7ViN50nZ+VPp5Pwqz9fZWA3I8l+VBGbGI7kfV8H8tuwn3Z5XUlIAEJSEACEqiLgAJgXfmltRIYBQEaMXj7EVmnsUOjJg0dGmuIamkUbRPKgw8+2IqUsWEI4/vFlnnLsCsb9fOOH9q+2D80u1a1pxSSmb03ATECIbnP8SJhyfWJjBd4/vz5VtRmyTOY8QKvu+66/fECY9/YlrU+L13kU5n2Gp8/vhmMiUnke1KWc56/zCRMl3qGa0AMnJxJOAy2+aMO7MMfexL5PhLYh335XrKemPO6KA9eQwISkEDNBPZcCZpLTV1darHZIIF1CSgArkvQ8yUggc4I0CiL8EejhgYM21inATME4S/j+2ETgYYjtg1pfL9FMiQNwTQe8/ci5w7pmOTDkGxaxhbEPgQ2vJIoSwTyAm8khD+89DYZ8BqiLBPxvMUjENvwEKSbcCZVwCPRMC4C5bNW6/siOVaWc745lHO6CTN8A89kOZMwQiDdhPHqjsjGO38IgXxIXuRdzpLvJgE78zfH8XdizhtCOrRBAhKQgAQkIIHNEFAA3Axn7yIBCcwgkMYJYkPEPpZlQysNlhmX6H0zNk4b348bI9Qw1tSQxvdbBEjtjb+a7Y/tCA6PPfZY20AnzxAlMrHHOjOVLpL/ixxDmUaEJNK9HSEQm5lUgYlJEr761a+2k5EsOo5azqtxybuAkDysMQ2r2py071r66ebOuJpEZtrOeIGZSTiT5Nx8882teDbU9FMmUy7Jq8R4ArKPbeW3lXdOed6qZcPzJCABCUhAAhKog4ACYB35pJUS2DkCNETi7UeDhBjhjwYJol8aJ9tKPPYwPtS08f1oKLIf0aM28Q+eaSiyTl6Uf7OtloDttQRsxZMOAY0QEY0uh8zmi/i3ysQem0g/3k+ZVIE0MEYhQgnhhRdeaCO2p+tkn12WN5Fe7/FBAuWzVuv74oOpev+WaTMJI3zj/cpkIgn8IHTu3Lm2vA/xmSV/iMkzlsRpYiDH5Ue2nJd0upSABCQgAQlIYLcIKADuVn6aGgkMngANkEnhLwIgjY8hdPPFG5HxoGjgYWtCOR7bF77whX2PxeyvaQnrhDQS83cNy9hfg+2Ub8YbQzSL6AdjhONjx44NdqKYaeUA7oh72B4BEBEcEZAuzDwzRIQUuk4SN92NeZrdXW1LeUv56+q6NVwnacfWMaS/nCQH8Q8hkLLN88w34v/+3/+7P5Mw5bzLCXq6Kg/JJ5bJP5ZE0sF2YrkeMZClQQISkMCuErh8Ze/dtxdrCrXZWxPbMdmqADim3DatEtgiAbzlpgl/NERoaNANK42RbZlJN0e8/RgPigYRAdvo5kv3sFLIwFZCGlXtHxX9F/trTUPsHzJ/yjyiAWUKj9EEPFvZR5libLHawyc+8Yn22U3XyZdffrlN75NPPtkQmUgh46jh7Wiok0D5rOX5qzMly1lNWjOTMN8wyvn111/fjhXIc1zOJIwHLM/0vJmEl7t7d0cnz1gmL/nOsV4KgOU6379te+J3R8ArSUACEpCABCSgAGgZkIAEeiNAw4IGUjm+36OPPtqKInhLMOMoDQzitgI2Ilgg0rz44ov7ZiBIplvmtC6+sTlC4f6JlaykMYi5aQxWYvrgzaS8xxOunNiDMn/06NGGZ4Dxxco8GHyiDjCQ5yXjBdLFGeGTyDppJZJuZmBFDDx8+HArLBxw2cHtzrOyS3m3KOSknePHmP6SE+X4xIkTzfPPP98Kgnj4lh6wCN0IgcTJmYTL62xrPfmHuEe+lrEUAFnnG57vNMucuy3bva8EJCABCUhAAqsTUABcnZ1nSkACMwjQmJj09qMRUYplNCK2OXYStuC5QbdMPP8SGOsMzyy8/mgczQppBJWN4lnHDnF77Me2GtMQ+4dkO2IX5an0IKUMIQJQpq677rohFmycQUcAAEAASURBVIXObcI76r777mu7N6frJF5TvBMQSohwyXiBQ/SW6hzKDlxwSM/atnDmG8b7J882zzeiP98TRG9+UGIm4bNnz7aRbwrHIHzzbAwtkJbyfUo+E8u08jffcI5TDBxaDmqPBCQgAQlIYHECCoCLs/JICUjgAAI0GCaFP7YRaTgg+EVUS+PigEt2vpuGGuP7EVlPQIRApFlUjCgbTLlGTcvYj801NuxL+7fNHZHrqaeeasfBiy14jcaDFO+4MsT2GrmX6ThonXSm6+TJkydbPggkeE0hJuQ5ZLzACCSHDh066LJb3Z88Sx5u1ZgN37xM+xjTD+4wiAd4soDnne8HkQlD0h2eMT/5gamcSRghEPF7iN3hydfkLWlNjPjHPrblbzjwTS/PCxOXEpCABIZM4HJzVbPn3zxkEz9gGzYbJLAuAQXAdQl6vgQk0DYGENPoAkXjAHGPBgLrNBDK8f0iALJvk2Ha+H40WmiI0Wgrx/dbxK40ADedjkVsW+SYNPI4ttY0bNN2mDHxBcIf3VsT8PChK+xBHqQ5fixLnhe6/RL5kQAvyXhLMT7imTNn2uh4gcMtEXlPlO+O4Vrbj2WLMMDT9957720jAmDEwMwkzA8GjzzySHP77be3wjfvim16w88iFVEvaWZJ5PuOeM83la7QPNMEvu085zlv1nXdLgEJSEACEpDA9ggoAG6PvXeWQNUEaAgg+NGYj9jHMp59NAQSy4Sm8Zjjyn1dr2PjrPH97rzzztZDa9r4fovYscl0LGLPssfEfs6DU20h9m/adso44hVdfWnQJ9x8882t8EeDOLZln8v3E+AHgdJbCiGQiKCQ8QIRSMrxAocikKS8jTGPx5z2lOB8txbNfzxaicePH29eeeWVfTGQ7yY/IBAffPDBVkTDM5Ayz3dzSCFpZZkygBcvY+Yi+iFkZh9LYr79+XtI6dEWCUhAAhKQwJgJKACOOfdNuwRWIEADYLKbb4S/VPzTJWja5dO4SUNq2jHrbuPajMfExB4XL17cv1yX3lmklZAG0f5NKlmJ/bWmobR/E8gp85nYg/UEJvbA4w8BcNEQ22stO4umc5Hj8JY6duxY6y2F+IcQiMcUHsURSHif4CWFQBKxYZFre0y3BCyv773v8x1blDDP/K233tpGusMjnlHW0x2eHxWIiOMp6xyfd8Wi9+n7uNiTspBvPX/z3WU/sVyHVY7r2z6vLwEJSEACEpDAfAIKgPP5uFcCEvgmASr06ebLOjGef1Tw8dBhmQbCLHBpOKUBMeu4VbZjH40qhBrWE2hIIdIsOr5fzpu3TDr7SMe8+3a1L/ZzvVrTsAnb8fJDSKZcUeYJlOFM7DHEQf1bIyv7j/KIiEpkdlUEEroZRiCBP5Fx0xACidsYLzDPSvn8VIZ6ZXOT9rzDV75QxSfmHbBO/sOPHw6IeNFTxhG9EbzLHxoyNibvGsr6OvfsGnk4IOyRHspGGdmPvUTWqSuwnuOHlJau2Xg9CUigDgKXr1zVEGsKtdlbE9sx2aoAOKbcNq0SWJIAFXoq7xH++DvefqxT8Uf4S0V/kcun4p8GxCLnHHQMXQcR/WhE5brcZ9Xx/Q66H/vTCM79FjlnqMeQl7WFlKO+bMcbjW6+NM4T8M5Zt+t4ruVyNoFJgSTjBb700kvt7KoZLxBRBCEQgQSxxNAvgb6etX6t7vbqYZD3/7pX5/sZQZvvLGWd7xhDV5RjYzJGLeWcOIQfHfLdy3uYZdZhVEaOzf6swy8x563L0vMlIAEJSEACEjiYgALgwYw8QgKjI0DlvRzfj0o7Mb/iU3FftUtPGk5cb52AjfPG90Oo6XOWxTRasKPGEPuxvcY0lPZ3xR8OeJ4h/DFeVwLdVBmzjsY35X7dENtr5L5u2pc9H4GE2ZSJmV0VT0C69jPBwqOPPtpGugYjpPQ9oULyLHm4bHpqPj5pzzu85rSsanu+W33kP+PR4qlOnCzrlPfHHnusjcysHTFw1TFsV01/zguHaWUBNuFDmUnMOexjW1mf4DrEnJf7uJSABCQgAQlIoFsCCoDd8vRqEqiaAJVyuiDhicA6FfZ4/FExX7Sb7zwIqeCnMTDv2Gn7OG/W+H6INHj9dSHSTLt3uS0NHzjVGJIP2F5rGrqynTKF5w1dffEmTWBGWhrjdNUreWW/y80SyOyqH//4x1vxjy7CGS8Q4Zb40EMP7U+ogCiY53Szlu7m3Wp+T3SVI2HQd7lKWWc2YYRuRG/KOl6B/DhBzEzCCN/MxMv3eVMh3++DOPDezLsTdokR/9jHtvJ6+XEx520qTd5HAhIYF4G9gQkaYk2hNntrYjsmWzdXWxgTVdMqgcoIUPlG9EP8S2WcCjrrVPC7EP6CJA0Grr1MwDYa/JsY328Ru9I4ScNlkXOGdEzsx6Zl82II6SjtX9UeyhQNa4Q/yn8CM3EiJuNp08V9cl2X3RAgTxBniRkvkHzMeIEIJUQ8gPGUQiDpagy1PCtjLBdjTntKbt73m8x/yi7l/P7772+FP8o6P1jw/spEOXxXEQEp65uYSTgc8j0Pn3lLmBFTjlgSuVa5L9dGCOT62Tfv2u6TgAQkIAEJSGAxAgqAi3HyKAnsHAEq3oh8NCLo7pu/U/mm0o3w13XlOw2G3OcgsG+88UYr0NCgzznYRFc/vLMYG2kbARsIcKsxxP6a07Cq7XjRZGIPngECPPAepUzdcMMN7ba+/6u17PTNZZnr8z4pJ1TAOxiBBG/At99+uzl79mwbeU8gjhAdL3AZwu8dm/Javjve2zuOtTDId2yTqYZ7ZhJ+4IEHWvGP72KEb0RBImOV8i5D/O5rJuF8i1fhkPLDMjxZErku27Mv69yHmL83yd17SUACEpCABHaJgALgLuWmaZHAAgSoZCP44fFEZTsxXXKoZKcLzgKXW/oQKvCEVPynXYB9dHFCpMHDISGTMPQ9vl/uN2+5SDrmnT+EfaQB1vPyYgh2TrNhFf50pcvEHkkzIncm9uhzzMgyDbG93Ob6+gSSl+QnIi/iCGIg+c4YaqdPn24js4EjBCKScM4yIeVmjHk45rSnjAyFAd9pPP6I02YS5ttJzEzClHc8CbsK6wiApQ15jliGLUtiKQaW633XUUr7XJeABCQgAQnsGoHlar67lnrTI4EREaACjbcfkXUq2Ih+qVjTEM4v7H1i4R4E7jsZ2DaE8f0m7Zr297x0TDt+iNvS6ErDa4g2zrIpDceDbGc/k8U89dRT7TLXQ+zD2w8vmWVFoFzD5XAJIHwwVuDkeIF4BTKbMDHjBSIYOl7g4nmZZ2/xM3bnyHy3hsSA91e8W/lhDy9AhG9+RJs2kzDHMsbgOiEc8h1c51o5N0xZ8t4uI/djO5H1/GBZdhPOdVxKQAISWITA5StXNcSaQm321sR2TLYqAI4pt03rKAlQUY7wR4U6lWfWqbxvSvgL/DQY0oBgO/bNGt+PsdhonKdxkOtsexl74Fhr2IU0zGJP+aJrHB5/eH8l0BUU4Q/PmZTF7NvUclPcay6bXeYFnk8nT55833iB/NDAuzHdJplNNeMFMrZg8mjSjjCdtX/y+F36O+/sMaY9+Zj839a7I3bMWpYzCTN8Bl6wRN6BkzMJxwt2lZmEUxb64kAZSzmDeWLuyz7uzd9Z529izpvFyO0SkIAEJCCBMRNQABxz7pv2nSVAZZnGLd4ALPN3Ks+pKLPcdCgr58y4yqQeNFBiG/sZ3w/hr8suS12nM+mAba2h5jTMsp3ucHi/0P0Nb68Eun0i/PU1Jlbu43K4BCgzTJBATLdJygrDDPCuxEuUyBiQ8aha11NquDRWtyzP3upXqPfM8js19FRcf/31zbFjxxpmEkb8o6zzrS1nEn744Yfb5wHxmx9FFvGGDgPSv4k6BOUtZS5CYOo0sSF/c1xZv8l5Q88r7ZOABCQgAQlsioAC4KZIex8JbIAAleDJ8f0QAKmwp2Lc5/h+iySxrJB/6Utf2j+Fhsemx2Lbv/kKK2n4wLzWkLyoMQ2xPewR+xD9aOTyDBA4ZtuTxcQ+l8MiUHabRBBJt8lXX3214YeJxx57rI0IxryXKEeMQZpnZbL8DSt1/VgT4WeMaQ/R5H/e/9k+5CX5Vc4kzJAIvCfxgsX7/vz5822kboAIiPg9r0t8GJDmTXMgLSl/2JGYOg772Jayin2p8+S8IeeVtklAAhKQgAT6JqAA2Ddhry+BDRCgsptuvqyXkUrvprv5TksyNtElk1k5y4CXAt5+DMhPRb2WkMYE6ao1JA1lg662tMAfLxYEnKSDckQjlnI1xBlfw7021rtqL2XknnvuaSMThpSeUoglxIwXaN69K6zvalk4KF1539daDrAbb2jiqVOnWu9XyntmEk6X4cwkzHv0lltu2Rfd4BMGrG9aAOSeCaSFmPc+SyL2lfvyN7YSsy/XcSkBCYyTALX3S827ExPWQqDeFkcthMdhpwLgOPLZVO4oAbz7Ivyl4psuv1R0qcRvu7KLfTQw6OpbdskkS2iA4FmDjbWF2JzGR232Y2+taYA5XloEvP1otBIYywrRD4+tRbqytSdt8b+ay84WsfV669JTiolCeHchLvNeZZnA+GpMsnDzzTfvP0fZt6vLlNe8N3Y1nbPSlfSzf5vC1yz7lt1OGvD4I/IexSOQd+mLL77Y1ivKmYQRAukmzPMxFAEw6U15ZJk8YkmM+Me+cp20J+Y6LiUgAQlIQAJjIKAAOIZcNo07RYBKLY3RIY7vV4KmgUwDggZFGgxUwhl/iy5HhEnPgvL8oa+n0ZEGx9DtnWZfbWmgHFF2mNgDT60ExmxjfD/E5F1omCddLrdHgGeDbpBEfqjAQwoxMO8uug1/8YtfbPBgRhwhsr7LIe+6vDd2Oa3T0pb0s2/XGGQIDn484Ye6dIm/cOFCO2bgk08+2RARAPmGJwztfZt8YUl+lbEUAMt10pBuwkmXSwlIQAISkMCuElAA3NWcNV07R4CKbLz9qLwSEQJTkR1CJRYb8YpB+GNg/YQ0Lo4cOdI2nNKIxvZaQxo+NachjSXybciBco6QjPCH8FIG8uFTn/rUzjXIyzQOcX3oZaZLZogDeD8R6W7ORCFso1zyQ8fjjz/eRsYLRAhkOAO8r3ctJM/z3ti19B2UnvJdn/f/QefUuP/aa69tjh492sbMJIz4jdc1P7yUP77wXkY0XGUm4b7ZUE5TVim7ZUy9if2s8yyTp4k5r28bvb4EJCABCUhg0wQUADdN3PtJYEkCVE4nhT+2pQI7pPH9EP7KxgEzaNIlk4YzDWYCaUlIgzJ/17RMA8E09JdreLnSdZyYiT24G13W8B49ffp0e/PkRX+WdHvl2Ftz2emWSD1XS9dyxL6TJ0+2XoGIIwjTk+MFIgZ+5CMf2Rmv1JTXlN96cq0bS/nmJoyFQTmTMN92yjqRdzPh0Ucfbd/DeAVS3nk351sfVkNYkl/JM8pxYvKUfWxDCGQ9QiDLnDeEdGiDBCTQHYHLV65qiDWF2uytie2YbFUAHFNum9aqCFARpZKN8EHFFM8TuuMgpvGL+5DH90OcoUsm3ecmK89UqBNS+c7fNS2TLvKm1jDUNOB1grcf3dBSRig3NDARlBGWGZ/NIIFtEeDZufHGG5v777+/OX78eCv+IYxQZjOeGmOqzZtMYVu2r3rfvOvy3lj1OrWel/Rjf/kdqzU9y9hNnt90001txMOV7u8EBHHKO179RMQ/hmKgnjJvJuFl7t31saQlZZg8TYz4xz62ld8e0lWe17VNXk8CEpCABCSwKQIKgJsi7X0ksAABKp1UpvGSozLK3yypiOJh8uabb7Z/b7t7GQINXll0/8E+ApVjKv4INIwTNCuk4s1+0ldrSAMwjYQa05G8GEo+IHAj/KWLOEwp65nYoyz3Q7O9xvzX5uUJTHtWKIuZWfWBBx5oy++zzz7bDoPAuzyTKdQ+XmDSnmdveXp1n5H0k4qxMiDtJYfPfe5zbTmnLpCZhOMlSLdgxELEwKGO90s+EpMmlkS+6+W+fOcRAvn2Zx88DBKQgAQkIIGaCCgA1pRb2rqzBKhwTnbzjfBHRZMKZ7qepaK6aRjcNwLNrPH9GDvooBDhjONSqT7onCHuJ18I28qPLpgMIQ3wozwh/FG+EhBLEP5oQE7rUhbbc3xNy9hec9mpiXeXth6UZ5RVyiwRD26EEQQRynY5XiCCSMYLHOL4adOYJe0pv9OO2eVt5fdqrAzI33CAAeWdH/6I8XylvDOTMOWf9zoRr22EQMo8nrNDC8lPlinnLImkl+3Zl3XqMoqBQ8tJ7ZHA4gQu7XX/JdYUarO3JrZjslUBcEy5bVoHR4CKZbr5sk6M519Ev1QwWRI4ZpOB+/HL/iLj+y1qF2lJehc9Z2jH0QggpLEwNPsWsWebaaCc012ScoUwknDzzTe33ccZUyr2ZV+5zL6a+Zfpcb0uAil/86xG2MtkCkygEM8oPLmZLInI0A6ME5jx0/Ken3fdbe3Ls7ZI2rdlY5/3Tfq5x5DzqU8GXDt1kEkG/EjJ8CSZSRjxm4j4TZkvZxKmvCMIfvjDH+7b3KWvn/LNMnnOkkja2U4s12GBGJpzl76pJ0hAAhKQgAQ2REABcEOgvY0EQiCVyAh//B1vP9apSFKRTiUz56Wyncp3tve1xCORBitdfd9+++392+C5gmfWQQLN/glTVkgL6SC9tYZN50cfnLbRWKFcZWIP1hMQQRg3EgFw2UA52kZalrXT43eHwLLl7YYbbmjHCrzvvvta4Y8uwhkvkB9YiLz38RxEQBlil8m8r5dN+67kevntHSsD8jIc8g2clr/0BrjnnnvaiPgdT1h+7GEyESYPIWbmbDwIh+gJm3xmSfkvIxzYTpoQ/0gzzzDrsMm50/i4TQISkIAEJLAtAgqA2yLvfUdHgIrjtPH9EP+oKFJhnPcLcirbqXz3BZBf6vHKosKObQTsY3Y/hD8GAl83pGLcd1rWtXPe+UlDGsXzjh3qvqRhE/mQcoWonPtRpvECoVzR5XeZENs5hzwo/17mOts4NrbWXHa2wW0I91w3z8h7RA/iqVOnWuGPZ4JxL/k+RBynyyReUkTEwyGEpD3ldwg2bdKGpJ975nu8yfsP5V7l+3sRmyi/CN/Hjh1rXn311bZuQf2CHxbLmbP5EYjvQW0zCX/hC19ov0Hf8z3f0058Ah+eEcpI4lifmUXKh8dIQAISkMBmCSgAbpa3dxshARoNeDrh8cc6lcN4/FEp5BdjKokHVRDT4Ejlu0uU2EU3HYS/cgIGbMMb5ciRI+2v213ds8+0dGXjQddJfpWNwoPOGdr+TaSBBh9jQOHhlMBkHilXQ/T6iJ0uJTBJoMvnnfdgOV4gHoGIgXQNRjB/4okn2ohXbLpMbvN5Sdrz3phks+t/l9/esTIgj8Mh3/FF8x1mlGXiiRMn2pncEQInPWH5IRSPQMo8k+sse59F7VnnONJC5JnIc4GdJRu2lz/wsp845rKzDnPPlUDXBPZ+Om725PquL9vr9bDZIIF1CSgArkvQ8yUwgwAVQUQ/xD8qgvxNZZB1KoGLCn+5POcQUsHM9nWWXAvBD+EPoSYB75NMwICdXYdUgLtMS9c2HnS95Ecq/wcdP8T9yYeu08D1GAQe4Q8xI4Hxnujmi5cHjbx1QmznGl3bv45dnjsOAmX56yLFCHs8G0S6FCIEElnnxxniI4880g69gHiOt9S6z9Cyduc56zrty9qxreOTfu4/VgakPd/tfAPZtmyA3+23395GZs5mIqh4wlJPSvnnueB7QUQ4HBr3sCD9/LDFM0k5IbIPe4n5m+PgxnHZxzaDBCQgAQlIYFMEum/Zb8py7yOBARKgkkflFdGP7lz5O5VEKnwIaqtU/FLZzrXWST728cs7wl/X4/stYlfSAp9aA3lI6CI/tsUgaejq/rB47rnnWuGPcZ8SDh061E6GgGjR1T27uk5s3OQyttdc/jfJa2z3ojt8ukwioCOG4CXFe5sfbIh8R/AexEuK7sQpU32ySnndxL36TMeq1867nvSPlQHswiHf8VV55jzEsMwkTBnnG0L9JDMJP/XUUw2R5yJi4FBmEqa+l1CKenlWWBJhlnJT/g1DYvblWi4lIAEJSEACfRFQAOyLrNcdFQEqdAh+ePxR0UukckjFjgpeKoergkllO5XvVa6TcdioXKfiin1dju+3iF1dpGWR+/R5DNwI5H2toas0UPaZ1ABBmWcgAQ8PPJr6ntCg5jwIK5d1EEhZy7PTp9XcI+MF4iWF8IcYSHf6crxAPGsRAol9CiNJe59pHvK1k/58v4Zsa5+2pQ7SBwe86BhyhPjWW2+1wjdlnh4KeMOmWzxjEUcM3OZMwmEB75JH3g8sU25YEjmH7cRynfMT+8w/ry0BCUhAAuMmoAA47vw39WsSoPLGL9ZE1qncIaylUrdsN9955qRyybWXCdiUcdhoQCZgGw1GKtqbrkCncoxttYbkB/aTjqSppvTE5lXzgQYaoh8NtFJQxjMJ4a/PyQtie/jXxF1bJbAsAd43pZdUxgtkEgWewyeffLKNCCO81xFHmJW0y5D3RPnu6/L6Q79W0l++e4Zucx/2pQ7SdzmgXnLPPe/NJMx3hh8vEQKp0xCZSZhxAinzPB8IiJsM+e5xT37knRZSXlimDKW+mLoi+8p12K77o/E0W9wmAQm8R+DSlasaYk2hNntrYjsmWxUAx5TbprUzAlT6IvxRoaPixjbWqbh1KfzF6FS2udcigeMQ/CbH96NSzfh+NBCxcxth2bRsw8aD7plKPcfBelbl/6DrbHN/0pBGyaK2XLx4se2ShSdSzqUsZWKProWHaXbF9mn7hr4ttofd0O3VvvcIJM+Sh+/t2dwaIgfvcCJiCKIIHriTwsgdd9zRCiNdzaqatG8upcO6U76928z7IRAJh3zHN2ETPyYdP3687RqP8IcYiAjOECYvvfRSGx988MG2NwN1m02NkRkWMFiER8oO9QWepzJyLfYTWadOyTrHcu2cuwne3kMCEpCABHaXwHZa/7vL05TtMAEqalTI6OIYsY9lKoBUzhBBWPZRUUvlMvebhZquYVSOJ8f3YwBtvLJoFPZh3yx7pm1fNC3Tzh3KtpJhrQ3jpGER+zkGbyMm9qDBlYDYhxCBB8a2BOVF7I+9LrsnkHLU/ZW94kEEGBft2LFjzb333ttOFBIvKX6g4gcgIgJCxgvEW2rV/Mpztur5B6Vl6PuT/ny/hm5vX/alDrINDpS9zCR88uTJdpxABHDEQOpjjB9I5FuERyBiIENR9FVmuWfCsjywKXZRthLDl31ck7+zzt/EnJd7u5SABCQgAQksSkABcFFSHjdaAlTKJsf3i/CXShkNrL4rZKlcpnI4mSGM73fu3LkPdMfc9Ph+k3ZN+zus0qCadszQtyUN2FlrOpKGefZT3vD0Q/jD8y+BscYQlClfKZvZt4llbOde8+zfhC3eYzwEUtbK8jeE1GMPY20SEUYyqyrPLt8rPASJeIAjiiDYMznPMmGoaV8mDescm2/v0PJ+nTStcm44bOO9X9pLPvCDJvHUqVOt2I0YmDEyU+b5kSoCOF3ku8y/sOCa6/Dg/NjFc5YYgZFrs42/cy+2EXNeycZ1CUhAAhKQwCwCCoCzyLh99ASo2KWbL+tlpMLFL8ybrHxxL0IqnMmgCxcutOLMUMb3i13zlrPSMu+coe1LGrBrMk+GZusse9JwSMO+PG6WJykeRAh/m5p5tLRp19ancd+1NJqezRPg3YQwT+QbhkcUYkjGCzxz5kxDRABECCQu0m0/5TXvjc2nbLt3TPrLd/92LdrO3YfIIV6uCH0p83jD4q1ON+HJmYQp812MUVsKdF3lBs8XMZxZEqlnlPtS7yi7CI/12eyKvdcZF4G9J6q5fOXdCf1qSTk2GySwLgEFwHUJev7OEaBCF+EvlS62sU7Fn/GXUgnbZOLT6KDSR8TDA68sxsNJGML4frFl3rJMy7zjhryvrGhTNmoOpf00lvAkRTBABCSQVsQEhL9lvYb64lIz/9L2vvh43X4I5FmpJQ/LWVXxEkcUIb7++uvNa6+91k6iwEQK5XiBs7ry15b2rktABJda8r7r9Od64ZDveLYPZVmWeSbIwSuQSF1pciZhhEBEQ+pOq4SwQITrOqScscyzx5LIfdmefVknT4j5u2ubvJ4EJCABCdRPQAGw/jw0BR0QoEKFyDdrfL9UqrZZ4c29sfOLX/xiO/Njkj6k8f1i07wllVNCKrXzjh3qvqSh5nSkTJEPCAIIyoyllHyhUUMDiTH+Vm0g9ZV/u8C/LzZeVwLTCFx33XX74wVmIgWEEb57/KBE5Jln7DSe+8mx0/JeKJ+9affZ1W1Jf96bu5rOg9IV0asGDny3Pv7xj7eRISwiBpYT5jzyyCNtWadr/LIzCVMfI/TNIs8cy5RDlsRSDCzXsSnxoDx1vwQkIAEJjIeAAuB48tqUTiFA5SneflSciFToUomi8kSDKJWvKZfYyKZ4bnAzbOZXbWxipju8shjXpqaQyjKcaw1JA/bXnA7sp5sUwl/CNddcsz+xB94UQwzbfibXYRLbeZYNdRFIniUP67L+XWuxPRMpnDhxop1IAa9Augrz/YuXIN2CEUWY3bv0/K057evkV97zY01/2IVD+Q3MviEvGbc2MwkzdErEQATwF198sY0PPfRQW6+i3C8yk3BY9OEBOItlyh9L3kdlxB62E8t17CO/cu6sa7tdAmMisDeiZnOpeXd4pVrSjc0GCaxLQAFwXYKeXyUBKkaTwh/bUmHa9Ph+syBSSWU2Xwa1LgMeWUP0yiptnLeehgO8aw1lRTqiQC1pwd7MEIrNNIAIjImEoIwXRPKo3THw/2rjv0mcF/e6dN+4J+QYJDCNAM85QgeRLv/8EIAAmLHTzp492xARAMshAaZda9e35T1Tvvt3Pc3T0pfvdk3fiDId5F8mzEEAp6yXAvjkTMJ4w86aPXvbLEhLyiPls4ypz7I/6+RZYs4r2bguAQlIQAK7T0ABcPfz2BQWBPBuQOygIUNFiUoR21inUrSt8f0KE1ubpo3vh1dWhBq6tMwap6m81lDXU/GEe60hacD+WtJBWcfrga6+eJEmUO4feOCBD3T5y/4hLkv+Q7RvWzZNcjn9wkvN/3Pnt23LnJ27b571Sc67kFC+KUeOHGkj74d4AtJ1kvECEzKbMD8U1PwdSnoWXW5b7FnUzr6P2yUO1PsykzDfR35s5RvJD2TUEw+aSZhzCFxn24F3Ut5LvKcSk1/sYxs2sx4hkGXO23YavL8EJCABCfRPQAGwf8beYcsEqPBQkcPjj4pPKkCpFJWVoG2aio1UPPH4K8UZumrh7Yd31pe+9KXWxNi+TXvXuXcqyzWnI2mAA2VqyAHhOBN78BwkXH/99e2g6JQxGkG1hqHzn+SaxtYm7H7kxRcUACczwL8PJMDYaffee287dhriH2IgM6lSZhk/7Td+4zcauksyOVDGCyzfiQfeoMID8rzuejoPypp8t3eNA91k6fpL5DuJNyx1snjDZiZh6mI5Liw22QX4oPxhP9+Y8jtD2SVG/GMff8d+8pI0lOctch+PkYAEJCCB+ggoANaXZ1q8IAEqN5PdfKn8UOGhklNWeBa8ZC+HMb4f4gwNLOxLoGGF8Ic4QyhFwVTacmxty7JiWpvtsTdp4O+h5gcNdQRlGjGxkXKfiT3KRn3SVcuy5M+zbvggAbg8sde9zdAdgZS1svx1d/XhXYl0MsYsESGEyUNYZ9Igvle8W4iMF8hsqhkvcBf5jC3vZ5XG8lsy65jat+MVn6FWqKOlazxiOGX/8ccfbyPlnjDk8o5txJRflkTysdyXfC3HCxxyumovY9q/fQJXrux1j9+LNQVsNkhgXQIKgOsS9PzBEaASE+HvlVdead7eGwOLShoeDVRmhjK+Hw0pumLS1SQVMypeiDN0w2LGxjKUv7anolbur2k9aak9HalUJ/+GkgeMHZmyFZto0KR7H+uEVO6HZn9snreM7fOOGfu+5y6+3vxm0XVz7DxMfzcEEPmIjJXGjwhMoMB3Nh5STLbAdwwvqcnvWDcWbOcq+V6N/d0zNg6UYYZdIdIdnjKP6I0wSLkn8Az8z//5P9tyP9Su8Sm3LPPNZ0kkT9mefVmnrkbM39t58ryrBCQgAQl0SUABsEuaXmtrBFKBmRzf77HHHms9Fo4ePdpW3rZdicFOBD+8shBpEhAn+bWZBtOsMZUimnFOKuA5v7Zl0rIL6cALhnzddsCGjB1Zli26+VK28M6Z7KbE80AYgv3r8KvN/nBfJ82LnHt27weQ1745wcsix3vMwQRS1jaVhwdbtLkjyrTznYoQiHc6ggjCCB5SiCSnT59uI5MnIAby/pn1bdtcCta7U9Kf79d6V6v37Hy3x8gBcfv+++9vZxPmB+YHH3ywLe/kZmYSZhs9OKjPMbnOEDnl/cUy5ZolsRQDy3XSkW7C9ZZeLZeABCQgAQVAy0DVBKisTBvfD1GGik0peGyzEjZrfD+6UjHrKuOvHWRfuT8V8FozL2lJxbPWdKQSvc38oKzTPQlRmS6/CXQdT9mKndmXZbbXmA+xnbTUaP8m7H76lVebd/bKh0ECXRDIc1Y+e1yXH7DiIZXxAhED8Y6i2zCxHC9wke9dF/Z2fY285yfT3/V9hn69cMh3fOj29mEfZeDWW29th2hB8KZMM1EbXrH5JvNdRvRG/EYMnDWTcB/2LXPNlGeWPONlJK/ZTmS9rFuT/zl3mft5rAQkIAEJbJeAAuB2+Xv3FQlQQaGbLx5/rKdiksoKlS4qJ/E4YPs2Ap4RGd8PETBhcny/bJ+3LCta20rPPPuW2Ze07Eo6KIObDpR/ZiikfGV2aGygIYLHa8aOnGdX8mEb9s+za5F9sX2RY8d6DN1/3y7eO2Pl0GW686yMsfwtkvZDhw41J06caL2k8IhCCJwURRBKEETwDORHsFpYJv1jFr54lvLdHjsHWCCIEfC0P3XqVPs3MwlT7vHIp97HN5rIUDQp9zwnQyz32BS7KO+JyfPs52/WKQOJOa8F4n8SqIDA5eaqhlhTqM3emtiOyVYFwDHl9g6klUpHuvmyTqQCRiWFSkiEv1REUkHluE2GZcf3W8S2VLaS7kXOGeox28qXrnmknKVh2PX1p12PcYcysUcaH/DEywCPPxoii4Zt2L+obcsct0n+y9i17WO/ticA6gG47VzYnfvnOct7Y17KOIYfI4iIIKUowjc84wUyoypCIHHo4wWmHrFI+uexqX1fOOQ7Xnt61rE/LNLbhCUiH5FynpmEX3755dYj9uzZsw0x5Z7jlvlmr2PrsudSzlPWefYTk2byn23xCuTvxJy37D09XgISkIAE+iegANg/Y++wJoFUMPB4oiGRv1MJoaKB8FdWVnLLVFAjlGR7H0vsmjYGG92jmHyBBk48Ele9P+kh3Un7qtfZ9nmpHMKs5pDytYn8mCYqU54ysQdeNcuGmvMhti+b5iEcH9v7LP+X956t519/o3nnsl2Au8zz5FnysMtrD/1aq6ad91REProFZ7xA3mnMqMpYvUS6VHIcP2ZkoqIhMUn6894fkm2btCXfu7FzgHnqltNY8E3mRzkiP9xR7omZSTjlHm/9lPvMKrzJ/FzkXrzv8s7jOUikLGQf28qykfECc94i9/EYCUhAAhLon4ACYP+MvcOKBKhMIPjxKyqVisTy18ZUMGbdIr/KplIy67h1tmMjlTq8sujym7DM+H4556BlKpl9pucgG7rYvyvpSMWWstpH4LqMn4W3DAOOJyAq06jAeyBlPPuWWfZt/zK2rHIs9sOoL/6r2DSEc+DxtdcuNq++83bzzjcUAIeQJ7tgQ56zvDdWSRMCxz333NPGjBfI95NvJ15SxIcffridRAFRBA/CfC9WuV+X5+S7u076u7RnW9cKh6Hky7Y4cN+wOOg7jHfrvffe20bKPWWebsKUeybtIj7yyCPN7bff3n7XhzqTMGmm/Ofby9/5BocFP4TDA0EfIZ9yQsx5nGOQgAQkIIHtEVAA3B577zyDAJUJRD88/qhQ8DeiH+tUIPAmSGVixiX2N6eCmorJ/o4OVmaN78esb4gzi4zBtqwZfaZnWVvWOX5X0kF5JKRhvA6T8lzKK+NmPf30062HTPYxbhBlq6uZBfuyP/Zuatk1/03Z3fV9KDd0OWNsyK++8lpz/vWLzdebfsTprm2v5Xopa3l2arG7Czu7Tns5XiA/dCCIUH753rMkIiDwQwdiIN/UbXLvOv1d5Mk2rhEO+Y5vw4ah3DN1y2VYUO6Jx48fb3/YQwwkUudFPCNmJuGhieAl9zyLLFMm+EH8f//v/90e9ulPf7p9ZmHEMUQ4JZbXcl0C2yBw6cqHGmJNoTZ7a2I7JlsVAMeU2wNPK5V+KkBEKhNUGtjGOhWGZYS/JDWVMq7TVaDbEt5+jGmUSg+/dlJRoztmn+MYJT2pdHaVpk1fJxXH8Nv0/bu6X9f5QeU5E3vQVS4BrwCEv1tuuaXTBnDt+YD9lKHaylHX3Ck3iCe8l1Juzrz8SvPyXhn61qs+1Pz6r/96c+eddw7Kmypl22U9BPKcpfx2ZTnX4x1HZCKFcrxA6gP8EEJkrDS+s8RtjJuW9Oe931X6a7pO+b4dM4fkWeqWq7Cg3OMlRzx58mSTSXMo/1y3FMHxCKTcc2zXz1/Sss4yNuUZ4VrUi/m7FADLdZhxTM5d5/6eKwEJSEACixNQAFyclUf2QIDKARUdPP5Y5u8IXFQMEP5YrlJJoHJByPVWTQJ2TRvfj+5Md911V1sxw86+QyqZ66anbzsPuv6upCNlkvKxTsCbFPEGEScNCq7NWFiUrxtvvHGdy888tyv7Z96g5x2xv+fbDPbyvDczwyQiIAEmeIh+49U39v5omm/slU0akniUZvZVxEA8UMbOb5WMzbM+RnabSDvfbDz+iJPjBb7xxhvN448/3kaEEASRTY4XmO/uGPM+z0oY8He+49k3xmV4pK65KgNY8t5u391773JEQLwCqXciguc9z/AfeT6G+A5P/QUOfG9IF++NRHjx/BBZ53iOSRzzs7Vq2fE8CUhAAssS6F+xWNYijx8FASoDNFjL8f2oCKRyQGWgi18GuQ4hlbRl4WIjjWfEGQZxTmB8P4SZrrpi5roHLddNz0HX39T+pCMNyk3dt+v7pLK6ajouXrzYerYgzuQaCMk0bClffQ8Ivq79XfNc9Xpht+r5tZ2XmaARjPNu432JsEfZYf3SM1/bT9Zte+OovbLXxZL3bWZfRVTO8X2Xs31DXKmaQJ6zvDf6Tgzl8p5vjhfIu5LyTizHC3zooYf2xwvs+3uc9Of71Xf6h3j9vG+wbcwckjcRvNYVAHM9lqkD8C7nnU0dFDGQ8TEp+2fOnGkj7/CIgdvwiC1tznp48Hfq8Hlf8Pwkphyxj22cxzplKjHn5douJdA1gct73X+JNYXa7K2J7ZhsVQAcU24PIK189NPNl/V8+FnnY0/Fh49/Vx/+VFDLSskiGA4a3w8BsCsbF7EnxyQ9qTxle23LsEtlMH/Xlo5V8oM0U5GnSxvjXiVs2puU+4Y7NtUYYn9ttsfuZbkjgiDglcMPMEYaYjFiHuu8697c6/p74a33upB/xyc/2Vy9l8c0IuliziD0XOvRRx9tTp8+3XYN5nwElC4bsrXlyyL2Js+Sh4ucsyvHbDPtiB33339/O24a70+EQIQRfqTjBxQi5R+PQISTrodLIA/z3R1j3qcMhwF/5/uXfWNchkdfLMqZhPGApcxT9nl/EzOTMOU9YiDnbCuUde3JbwnPTZ4d3iWJOQeGbONvjuPvxJy3rXR5XwlIQAK7REABcJdyc8Bp4YMe4Y8PPJUmtrHOB56Ke1k56CopqYCkknbQdWeN70fFikZ2n+P7HWQb+2FFWDQ97cED/C/pSFqSTwM0da5JqZRSjg8K5BnCDcIfFfcEGraM73f48OH9/M2+vpfL2N+3LetcfxH+61x/2+cyAzTCH2NEJcybCfqZC6/tDWz9Xpl8Z+9de+PeLJT3fNObivccQiCCIB4m58+fbyM/wPCuQwzc9oQLSafL4RDIc5b3xjYs49633XZbGx944IG23CKIUIapY+CtT8QjirKMGHjDDTd0YmrSX36/OrlwRRcp6x5j5pAsK8WrbOtrSZkuZxKm3PMO5wdrvhHEzCRMuadOwTt9kyE8eE7nvSeyP88USyLlq9yX8kYdkfKWfZtMk/eSgAQksGsENvtl2DV6pmcuAT7mVAZoYEbsY5kPOh/zxLkXWmNnKqi557RLYSfjrNBooAKVEI8sGhEIlEMIi6RnCHYeZEPSwXHkzS4LgHioUElH+MsEDaSbMayOHj261UG9U0HnGagx1G7/PObkCYIfwt+FCxf2D0XMoNzME4yf3JsA5K3L744JyIkIgGXAg5l44sSJ9t2HGIg4TVmNgMJ9aEQiBiI2Gt4lkGclZW9MXJL2oaSZ7wYef0TqGbxnEUV4XvCWeuKJJ9qIdxRlmePW8Y5KPWKMeZ88DwP+Lr/j2T+mJc9DeGy6DsP4f7y/8YrFI5ayj3cgIjj1WSL5w3eCsn/H3jAQm8ivCIDca5HnJMewzPuFZdiyPfuyzrXhzd8GCUhAAhJYnoAC4PLMPOMAAny44+1H5YgY4Y8P9iY/3qmUpVJSms42Kk00eMvx/ahY4ZHV93hCpS2LrqcCl0rnoucN7biy4pZK39BsXMSe5Me0NCD2MXA34grCCoF0UyGnfFHOth2SD9Ps37Zti9w/9i9y7JCOid3TuPNsI8Yh/L3++uv7ZiNiUG6YKTXn7++cWHliTwB89Z33ugC//c3yN3HYfgORMpmxpiivCCjcO93LuCdCIMdt2qNk0mb/3h6BlNe897ZnyQfvjLCHME6k7CIEEvm2xzvq4Ycfbr/r8Y5aNh1DTv8HifSzpax7LMuvH4u2d9WUByzYFgu+BaVHLMIf5Z5vCHmFKEhM93h+0O5zJuHUtVf5TuS7xjJsWRJJC9uzD945fnslwDvXTuDKlav2xgB8t2dVLWnBZoME1iWgALguQc/fJ8AHelL4Y1s+3FQINv3RTqUMGxKmCTPsQ/Cjm++Qu75NS0/SVdMy6cDmMm9qSgO2pgJapoHGJ95+VLpTiUWIptFJ+RqSN1Xsj5218Y+9tdtPOmg40XCb9BTFcwNRg/fSouHJl15pnn/jPfFw0gNw2nXKsabooo4QiD28L/FEJFKO8aJCDByKV/S0tPS5LWUtz06f9xrStZPuIdk0yxa8V48fP97cd999rfhHOY53FMIIkfpIyvKi4wWGwdjyvuRcfuvK73h5zFjWI3aR3vzYvM20kx/8SEPkR0fKOWWfd3fZPT4zCVMn6fqHyDBZl0eeMZY8d2XcJmPvLQEJSGAXCCgA7kIubjkNfPDxHqHCwUeaCiLbWKdC0tf4foskOxVUbMGrBY8sKkX8TaCSwi+iR44caccMWuSa2zwm6Skr4du0Z9V7Jx2cX3NaUkklDXiZIN7wC3wCogqiHxXtIQomsT/21raM/Xmea7Mfe2mY8V4isk4gXR/96Edbjz/GiFw2vLjn9fRO8aPH1y+99wPIItfinuleRuMRMZBJFnivs0505uBFSO7OMeUzVr6/h5xCniO8nYiMF8i3H69/ltRX8twxti/vaOK88QLzrcp7Z8hp78u2MOD6Y+ZA+ksWQ3smELhTptM9nrJPPWVyJmGOox7cxRjXEQC75EE5I8I776Gxlz3Kn0ECEpDAqgQUAFclN/Lz+AhTgabBGrGPZSpEfPwTt4mqrIR8+ctf3jeFxiuiHxWfIQoz+4ZOrCQ94Tyxu5o/y8pbKnTVGD/FUBqUiCIJNCIR/vAwSZ5l35CWyYda8yD2D4npMrYwTtkXvvCF9h3KeZQV3kmUnVUbY299/RvN23vv4jK8fem98QDL7QetwxcPRCLve7yoKOeMOVWOZ8m7lfcp5X2Vrl8H2TGk/XlWai97yzJNupc9byjH82xRPonp7o53FIII3YQzXiCethFEJscLzHd3yO/0vnmHAeV/bM/AJNuIXWxf1+Nt8tpd/l12j+ebgxBIzEzCzARPRChHCOQZmSz7i9oTJn3wqP0dtChDj5OABCTQNwEFwL4J79j1+QAj+hGpCBIj/FEZpGLMh3/bFUNsooKDR1YZ6O5A45ouEjVW4mNzKuFl2mpaTzqwuca0pHzhHUWI59Yy47S1J275vzyntVesa7KfLuLMWEqIiIZohoBGXLXhlaJ0Zk/QuO7q93/aF+kCnPNnLUsbaUSeOXOmHT+V4zPG2kMPPdR6LtJFeJGxCmfdy+3DI1A+Y+X7e3iWHmwRzxjjaRIpywiBRNbpKUBkNlWEb8RA6gvUa8Kg9vQfTGj2Eflej5lB6IQFf9fCg5mEjx071s4m/Nprr+2LgXgF8sMOkbEyKfuIgZT9ZX7U6VMADHeXEuiKwKXmQw2xplCbvTWxHZOt728ljCnlpnUpAlR0JoU/thEREaggUAGKoLDUxTs8eNb4ftziO77jO9rKzLZtXCe5qWSWFc91rretc8s8SKNqW7Ysc188R+g2hhdURD/OZ0yd7/zO72xnVl3mets+NvlAHhDz97btWvT+sbeGMoSowA8SEf9II/YzThkNrWUaWfP4PPHiy823XPX+Cu2kR+C88xfZRyOScQmZQImA/XjBRhjnxxe8rBFPEANX6ca8iB3bOKaGstYHl11NN2WZZxBRhGcUIZDyy/udZ5XIs4lXVMSNvHf64Dz0a6bukbrI0O3t076UB+7Rh8dbn7ZThjMbfGYSpuwz1ENZ9kkXIiDvcn7UOSjfw6QvHmN+9vosD15bAhIYFwEFwHHl91KppcJPZW+o4/uVieGXTBqjVF7SUKECwjhaVGoINEJrrzyk8pVKeMmgpvWkA5trSAueIZQvGoaxlzTQTROPLrqNUZmuLZTPA89N+XcNaRm6vTB96aWX2hl98ZJLYNgBGlnxSM72LpZP7E0A8o0r7x/zrwsPwHm2nTp1qv2BJV3h8Yzlxxi8BIk8HwiB63Qtm3f/bewbetnrmkm+q1x3F9NOmvDgJp48ebIV/qg7lOMFhinbGeZhl4TtpO2gZfn9O+jYXd8fFqSz5mcC2zOTMO/ycibh8kcdvlv82EOcNXFO3wJgylTNvJMGlxKQgAS2RUABcFvkB3xfKvrTxvfjw85HF+EjcZvJwE4amnjVlI3rcnw/7IwAWFbWtmn3OvcmPYRdSQvpGHJapnltUQlOd82zZ8+2AuCQ0zCvvO1KJboUJ+ald1P7sAfh4KmnnmrHWcp9EYnxnEM0fvLJJ3tpND7x0svN63viYhm+PjEmYLmvq3W8pPASITKmGu9dPGXLbpV0LcObBDGQLmZ5n3VlwyavsyvPzirMdj3tlEt+PCQi1DP2JeWZ7pEEfmgk8jxT3hFEqHeMIeRbV/Oz21U+lWLXrjwT5CvvaCLtAMo5P3wiCvIsUN8m8uNnxMByJuGSSVecvY4EJCABCXRLQAGwW55VX41GKx94PP5Yp6LHx5wllZuhdPPFJirkeGTRuEyINw0Vl1ROS2EgFZMcX+My6UolvMY0xGbSQjrKPMq+bS6xh8oulVwEwAQqvIwZhRdTurek0j+0NMTmg5axn+NqTEPsH4rteTdRdhDBEvCuQPiL10T53soxXS0RAN/80Psn/Xj7G++fFKSre826Ds/K/8/emwBbUlT5//WAFrpxRBAc5//D/7+bhqabUIS/K24so4ThboSKM6JoGBqGG8YQ4qARDs4iow6OOE6M4zK4B44a4wwyMWE4AQjqyPIHXOjt9aLiKEg33f16e9t9//rk6+99eevVvbeqbi2ZdTMj8tV9VVmZ55zMyjz5zZMn5WeKbwggkEmkJpRMKvHDxgQSMN2eQPbL05X7rrS1uuVhjzn67uqmoYnyWPDBbzDxv/7rvwwJuHzAZ9q+fftM3Lx5c9dnGqChxocm6K26TLUD6SJVl+dy/m2XBTo/izVELLoFhNOnM76xiEWk/xYYKD27ym9gnPofl9t/G2jrLMQGFQu9LlNc5wuaQwgSGFUCAQAcVYIteB8lRtt8WeFmcspqNttbUPJcAf76+ffDkgTlXJNru0pQFAQ0SVmzn/v2W0p3G3iREucKLyiuABOANzZAI6st2ploVrvR/76CAqIffnzlQXXR5BVgC5CLRQn6UgXAAEDjJMAluZct86l4kjY1PRMdmIgtAC2dtuotwOI3eYVP+mUi2yqxisSSCoAdOWEhSUQ+TDJ9sKQqu86SMvPhf7VfH2gti0a73s8++2wD8gnYZuFU/gIBPlgkwjIQ4L9tstJ4LV2kLPn6mI/ArnGQBXMCFrGI6Ecs6NCXY82OCx4iJwkDmIcQJBAkECQQJOC2BAIA6Hb9VEYdyizKC4ork1f9zwQWRZaJ2IknnmiU16YV2DT/fihc0AjwhxPvQYG0KK1SXAeldf2ZFM3AS3k1xTfARI7DPWzwBsAP8Abwol/wvT7sb9ue4Pbj17X7or8p2lmUoM+k/diTQfom2g6WcHUGrP9OPj72S3modwtwFQBgXpkDjCAXInLTFuH9+/ebySOnrsqSCjDQtuSuU4ZZy1Lby5re93T2mDNuvFN3dnun38evJRFgO+kzjf6AiKUg7R0wMLkI4Gt7UDvQ2OcrH2XQLVlUae1WBp1l55E8SZi+HECQfh19iiAreNo+ffmoMuL7I45j31N2/YX8ggSySoAF2k996lPRTTfdZHRdFgLY3fHa1742euc73zl0/p21HDsdCwwcqoj/aAKLDjtjd0vDAu99+tOfjr75zW9G27ZtM/0RGMFLXvKS6D3veY/JZ1ge4/Q8AIDjVNsxrwygAH4AHSgvikxeGVjtQbpJBQ868e/H5Fp+d6gqto2xZYwJYtaVRvGhCbrPVS5epHj6zIsUOeq6icAWFtoXiqvaBvLFeoNBAyfvw0LTPAyjb9hz0U+6puphGI2Dntv0D0pX9jMUDSY4bInSt6jtUrQd+qkmwmR8AvBjYgUtWtp9bMiYnu/dElw2bXnrASVyzZo10erVqw34B1jCJNK2pKJ/Bzyhr8cKN28ZZfMY8luSwDjWhd0/ahxGIvwG4CDSfrEipy1z+A/bhHUQDgCg/AUCDPoa1N/ZMvCVl1HptvWGUfPy8X36Afpm4plnnmna/L333tsFAW2rWKzh6c+znCQ8TBbj2P8Mk0l4XkwCnYXYQCSOPoW66L3xxhujSy+91Li5kHzQfe+66y4TP//5zxtg8PTTT9fjUq4f+tCHuuBf1gwnJyejF7/4xdHWrVt7XmFRmQitX/va16KXvvSlPc/H+Z8AAI5J7aO8AvqhoKLA8T/KC78ZTLXNlytBik3d4qHcNP9+bEfGoqaIVYhATSmudfNUZnlSugMvxaWKRSmrWiinmtTR7nWwRx7wRoqo8ilOVTNvin5K95WHOmlX22ErqwLtBdAPoEr9p571u9py75emyP1tMQC44pijl706O997KvCyBA3dQA72BJJvEvAE+TJWAbISAeORLwBK0+CJvpOq6rChqhharPgm4bjxDs9Z+Ae01jiig3Boz9omqckI1uWyjMraZ0CDC0G6h3QRF2hqigbJQjpmU3S4UC59AuAe/TN9N98BV/p09Hq+AyLjJYusgIFpbnuG8TKOfc8wmYTnQQJlS+Cee+6JLrnkEuPrk/n3VVddFV144YXm/xtuuCH63Oc+Z7b8Y10HIFiWhTvlfvKTnzT9COMpOvewQBroEPj31re+NXrd615nduDcfPPN0TXXXGNATPj54Q9/GJ1zzjnDshyL5wEAbHk1M/AyCBNRYFFYuMdvFDiUT64aVKXIkKbOwNYBbZuBVoVB/v2UZthViqqUtWHpXX4eeClWO7R3LDIAE2yLUpRVwBv5i/1BAABAAElEQVQmY2r7eUpQfdiTwzzvN51W3z10+MiD6K+SdvLmlHFAY9qQQtqhMHqW9Vo23WwBjiaWW9RWsQU4K49Z0/EtYSVCZLEKy1zGBA5aAEDBvxRR4AnpinyzWenpl67sOutXjmv3bb713blGY5X02PpDFv7tg3Bow4AftGnaNluGibRf2jHjD+BJlnyr5DFL3pKDxr4s77Q1TZDF8prV3AEXQizasOOIRX3aPrt6aP9a2OEb0RZ5QIYQggSCBNyQwOWXX27APjCC733ve9F5553XJeyiiy6KzjjjjOjKK680Otm1114bXX311d3nRX/QdwDecf2Lv/iL6Atf+EImAPDjH/+4oYNyP/axj0Xve9/7uiRA9wUXXBCdf/75xnfpe9/73uiWW27pPh/nHwEAbGHto6jzAQGk2f79pKygZPJRc00qnFLqlLZq8eD/CWWAbTOaYEADSgHAzDD/flno0yRRikmWd1xNY9cP8krWn6t0p9El2lXvaWlGvUc71sEegAgKrFZhUXrKKacYAFz3817FQ13fS176hqUX/aSrsh6G0VH0uU1/0Tz6vYc8sF6gf2ICr0DbwScJbafK8lVe1iv0Tu7aHf3hCcu3rle9BTgrjVnTYSUih/Os7sp6hIUigSeMYViSMMksYkmSlZZ+6Vyq+340lnnf7h/GjXfkaPOvcTiLfJGV/AWyTRIQhPbMuIROorbNtniBIVjFuho01uWRgau8jEqXdErpmKPm14b3kzKhn5ZVLFviBQamnSQMEM43MMzKexz7nza0jcCDHxK44447ottuu80Q+5a3vKUH/BMHV1xxRXT99ddHGzdujK677rrogx/8YGa3XMojeSWfu+++27gTeP/7328AwGSa5P/gHPgoJGzYsCGCrmR49rOfHcHHP//zP0e33nprdOedd0ZPf/rTk8nG7v8AALaoylFQB/n3Q2FDURk0eEqR0SBehXigsyz/flnok6IqxTXLO66mES/QhxwH1aWrPIgu8VJFvfAdMLHCxx+ggQJWFjrYowzZKQ97cqiyfLiKfmj1lYeyaac9MkkB+MPfiQIWDYBSOhxJ91257o59Wu45PB0du3L5sO6DBWA/OQK4Apyw4sy4gVUgW4T5xjm4h8hCEZNHYhmLRv1oGef7dv9g9xvjIhN7nCrKP2MeCwdE2q/8BdKuGad2xo7OiVhDAWxnAUPqlr/agcbvust3qTy1iSCLpVrR3EFziaUnkQH2Vsd+X4ksyGIViJ7GOMtCj7bIc3o2bZ8Fnqy+vu1ywu8ggawSiGdRUewIK2tyJ9JBc5XhO9/5Tjf7N7/5zd3f9g/6vDe+8Y1mazA7ZNhqe/HFF9tJcv1mhw2+/wif+cxnMvvRplwWEwiXXXZZX4OON73pTQYAJN2//du/BQAwlsPymQLSCcErCaCEgIIT+Y2CxiDMbxRVVuD4WLMorRq0NYiXKQjy7OffD2s/tsJUoUgpzyp4KlM+WfISL6Slfu3/s7zvUhrRDh9lBVaYAQQACVTftHvaFsBf2dtM9E1pUlQWH3XlI/opz0ceRH8ZtPcDjZmsA/yVaZVTJt1qKxwAglr40MElS1c9m5mr16WDyi3zSn8h8ISxjrGE7xzlkwkkTqCJTB4BApk8MvaVHdTWVIdl5+9qfuIb+saNd3i2+dfYxf2igbYJyEdk3JIlILsiiAJDWLSiPTOGVdGe89Kv8boMGeQt27X00jGCLBZrhm9EMtFcol+d4deVwwM4UVRb5OnTAcJxtUG8//77TZ9P+8f1QwhBAkEC1Uvg9ttvN4XwjT71qU/tWyDbahXwrTcKAPiOd7zDLAq84Q1viC6It+xmDaKV9DY9yfef9rSnmcVhdEVoDSEAgF63AQZaAX8MvChm3OM3CgkrZyjqeZR1DdoaxMsQEAM6EzUi9CqU4d9PeQ26iicproPSuv7MVjR950ft0p5YFZV/2lZy6p3JFeAy26uqCKoP3+sC2ZRRD1XIuOo88UkkSzJAQAJtEwAJ4M8XizL8/518/KrowdkUADAeF9oUGNv4rolYkgCeML4ApGjyyNYUQBP6AEBB9TdtkkOdvNj9wzjK0u7jy+afLY+nnXaaORlbW96xjkJ3wjqQCBjCIWiAIYCCGnvqbAOUJTk0VX7d/A4qT7KQjjko7Tg8o49QP5FVJnxL2iK/fv1603/Tn2Mdy3iMtTeR/Gj/gIZlLsaNQ70EHoME8kgA3YnAtzZo0YnvVUHv6P88Vw4V+c///E+zuwZ/gnkC46KCTY/u6Qof8PPTn/7UbFvW/XG+lr88Ps7SrIF3BlfAOSatAvu4ShFBKVMsQo6UOuVXJA+9AyjDFkxW9aQUkD+m/fgEYXWhjlAmT3XQO6gM8UKaMupoUFlVPxMvRfmgTXGgB1s17cMZAPsABpgoDRq8yuBPE0G17zLyrDMP0U+ZPvIg+ovQzimd9E9MNtQG6wCNq6pfTgA+ceVx6QBgp10AoC1DxhG2B6Pc0Q8ABCb9qwGw0B8ABo467qitqe3ZtLT5t/iGx3HjHZ5t/jV2cb/MgFztU7EB/gSGoOcBChLxkYkeRZsmfZ31ob6yKhmUKc+q86JOCFnBrqrpaTp/yaOoTGjHgNvEs846y/jgpb0DAKr909fX2d6blmkoP0hgkAT4PoYF5ttZA4uo+FkmDHsPdzjoUyzCoksXCczhOJiD8Ld/+7fG4jdPPizeE6ADX9CDAvNCAEB8e7O4VpVhyCAaXHoWAECXamMALSifsvZDASMyIHJlMEQZQwkZdWCUImMP5APIWvYIOpmEAcrYp62isNKZMAGr26eHFNWiPC1jssEb4gUSpIg3SM5IRYuXvHyQXoczYC2hwPZetvmySqy89ayqq743e3JYVVlV5Cv6ydtHHkR/HtpZmMDfCJMKvSeLsrr6pyJ0D6t/tgCvetSK1GTTLdgCnMqYdROZavIo/2qAgYxHKLXbt283ESUR4AQApe6xyCLXu5/6VtR2vWNgRILtcaoOGVAGuySIsoSiPQMKsgBMH0Zk3FN75lTVqoPkUNcYWzU/o+QfZNErPVvH1lyiN0X2/3gfK3yiXD6wVbiONp6dypDSdwnML0xERJ+CTe8znvGMoaRr7B6aME6QnFMNe0cAIHp1kcCJvSzYclovJwDnDaI3i3sne/EXegMAmFfaIX3tEsBh5Xe/+12zIsZJNigdRBRELJxQxMpSSDVoM5DTaWTNl/RY+oHG26et8lGCulfl3y9LZYgnKWtZ3nE1ja10+86P2lbWwYlJEKtdrDQxoVdgFQrgr4ltfqqPrDyIZleuqgPo8ZEHm/5hMsVXHBNmJtAKWIbRdgCD1E/omU9X6o4twOv/8HGpZPt8CEgqQ0NuMi4C5hKx9NQWYfy/0A6ImzZtMosFpMmzpVLfSZ62N4RcLx6PK9+qHPHP/+r39azqK+0ZkI/I2Mc4SJtm8sNEZsuWLSZW7f8SPqV31C2DqmVcJP8gi16plQkA2jmzUEM/zTg9bv2uLYfwO0igagnYcysMd4YFgWjoWXnDD37wg+hf/uVfDI7BwR9Fvm3Rm4dW6CxCb17+XE8fLABdr6GYPo7k5iPBwaZOuWFA5GMp8sEMYtmeBKPwDstf/rNYmbb9+zGhAvhz4cRMKapS1gbx7/oz8QKdvvMjXobxgam2DvYABCTQLrH0A7zhlNCmgr6PYTw0Rd+wckU/6ewJ7rD3XHvej3buA/gB/OmkMGhnJRD/fnVai1Yps99O7Y8Oxv5VZ/ps9Z09slWtShpczRuLERzN42MN4I+xisUq+hKuRJRYJpdMMpvsT1yVIXTpG7P7DJfpLZs28U++TcqARYs1a9b0+AsEDGScxNqVaPsLxIJQY20ZMtFYV2aeZdDVRB4CvGy9uQk6XClT8oCeKmSi705XV/gOdAQJNCUB8AF0l7IC44sC8/thgXGHkNcyl/fe9ra3Gb3i8ssvj84+++xhRaU+F715aC1Cb2rhnt8MAKAHFSizVRBrgL8qFS87bwZz+39bVKw6p/n3w1wf4E802+809Vs82MpJU7SMWq6t+EgRHzXPpt5XvfTjA0tStpIzQdfkC6USKwi2k+cdcKrgU/Uh+qooo+o84QH6feShn/xpU2zxBfizLZLZ/glozAKF3q1avmn5q+yyZI71H2FqNl1hmx5jAFDyR+YsSBE3bNhg2gdgIP5uUEZ37txpIj7VZG2VtqqsOlMdKv+2X8eVb9WrximX6h2w+swzz4zWrVtngD/as/xfCtymDTNBpE2X4S9QctD4LfmM41U6ZZDFYu1LHvwXZDKOX0TguW4J0LcP89WXhyZ7ATTLtl7p11m24Np0/M3f/I056R684MMf/rD9KNdv0ZuHVgrIS28uojxJHABADypKDZXtS1UPqvaqHYO57SOJCQCrywB/XBVQMOmAUDDTJkxK19RVPElxbYqOMspl8kEbgBff+dFEShNL5MNvLLUA/uSIlvtqY1jo2G2SZ02GNB6apKdI2fCA3O16KJKPC+/QZ2ENQ/vRyiR0YQWDxR+nDbYxcAAI4eFDB1PZG7ctwKlCsG4yJqA4E7WlEvAEJRI/U8TNmzcbh9T0OaecckrlY69FnpM/1T+oz3OSyAqJEv9V62BFWKBO5P+SPhAQkH4w6S+QhVmB20UX0KR3jGs7sOtHspCOaT8bx98CAJFHaB/j2AL847mzEM+n4uhTqJJeLOoYSxg7dMBGP9ng518AIEBenvDRj37UJH/BC14Q3XjjjamvKm+unBRMYNfORRdd1E0P9vCTn/zE0MEOj0EHgeigEvQ5bV3uZjSGPwIA6EGly5pOH0OVJNuKjAZzKZR8PDYNLvj3yyILKexS1rK843KatgCAdr0wudLBHky+FVatWmUstrAsVXo9c+EqmqCf6KPSK5o1wXVBrllpEO1s59yxY4dRWOSKgGf4HsXiT4soWfP1Ld1kbAF4fHwAyNR0bAGY4s96en5x67xvfNVBr7ZUrl692gB/AIH4WKMdYUVKZNEBsBAwUOOI2l4dNLpQhvqHceNbsle9q8/Xfdeu6HAC+VgEkb9AxlX0t61bt5qIJSzpGFvzLKr5Ioc66iXIolfKmjPY84jeFOG/IIEgAdclwAnct912WzQ5OWlcpeCDNi3gR1mBXRV5grbsXn/99RFxUMAY5E/+5E9MkvPPP78HAITWb3/72+YZ9DzrWc9KzYo5wrZt28yzvLSmZtiCm+m12gLG2sSCAEAsAKsOtnKL8sjkh1UATaop3yX/flnkIZ6knGR5x+U04kfKp8u0DqJNE0m2tv/P//xPj1NWV7ZqDqKfZ+KB3wEARAr1Bn0DTHIFUPB9MLEF+JN/kHqpGl6a2o1oHv7G4BRYAJ4cg+VTB9O3AAcLwMHy4yl1goUocf369WZBAjCQhQnGP6xKiQr2mKh7bb6qrarttpnXNN585B8rB4BtItatWAUSsXrFeoO4cePGrqVrFn+B6nOlh6TJalzuSacMgNdijdclj3Htg8bluwp8NiuB5z73uQYAZMHo7rvvjp75zGemEnTrrbd27z/nOc/p/q7zB7QqQE8/APCuu+7qGjA1RavodOUaAEBXamIAHQIAbeu7AclHemQrdffdd1/PpNpF/35ZmJVyJsU1yzsup1Ed+cwPqz86lMFu15hms1UTX0U+BNUFtGqC6APdNo1Spn2inzYDGKMTfaGdVUq2AxBddEVgy7zM33OxO4Adj+yJzjzlpCjqs0Y0O98ps8jW58V3jfUokb5KwIltnfyzn/3MWFdhFci2FI0zbRWO+gf1F23lsx9fGm995R8raHwFnnHGGcaFC22arcJYRnAlytKVBRSA8DReJQd77Osns7bfD7LoreEqAUD1P70lhv+CBEaTQCfeMtFZmBgtk5rfhuYqwytf+crommuuMUVgnZcGANL3ffnLXzZpMNi48MILc5GU5Xtm4Qo/3swJ8dGcFi644AIzVjGf/NKXvhRdeeWVqePWF7/4xe7rr3rVq7q/x/lHAAA9qH1tX7OBkrLJ5mOUfz/lzT0UQibUTHJ8nVRLUZWyJv58vfrMD1asbCXHYsuuD9oXPiTY8utTsCdI8OMjCCAesgzITdeN/ENilWUHJqtPecpTDAho3x+H3w/s3Rdh4bci3vrXL0zHk/wQikmAcQ9FlDg1NRX96Ec/6oL9bE0hAj6zQEY/hjKsb6pYiW6+5UP/UKXkxL/G3yrLqjJv2qb8BbJ9il0egIG0Y9vSlbFYW4ntcbktcihDxgK8fG8TZciCPCQPH/WgsmQQ8gkS8F0Cz3jGM6LnPe95xgrwC1/4QnTZZZdF5513Xg9b1157rbEe5yan+CbdSNxyyy1dUJD3bQCuJ6MR/0E/e8973hP91V/9laHn7/7u76L3ve99Pbn++Mc/juCDwBbipz/96T3Px/WfAAB6UPOyAGTbBgNsmYMr+bHym/Tvh1iYzJx++umllteEuCUvKSdN0FBmmVI2bQCtzPyryAvLGVZybOAGPuABgJuTDH0M9kRfEyPf+BAPrtIPXSxO0H5w8qvApBSlA1CQNtTPT4nSu3QtU+Y6AKQTLfRlMWwB7iuaXA84cY5+i7GEsZFtlbKiwlUGkXbJ2Al4UvSghVxE1ZRY/YPGn5qKdaYYjbf6dp0hbARC0I10GA6WrvIXSJ/KYh0+oIiA2rRn0koO49oObHFLFtIx7Wfj+Fs6dlXyaNO3N47tI/DsjwSuu+66iK2yuGi6+OKLow984AMG0ON/DuT47Gc/a5jBqvyKK65olDEAv2984xvRli1bjAUgY9brXvc6o3/dfPPN0Uc+8hFj6Y4+9slPfrJRWl0qPACALtVGH1oEAPIYpUzHXvdJnuk2yh7+jdL8+wHWsBKMVU1VA3kmIktKJEVVylpJ2TaWjS/8MGFkiyZbNW3ghk4Y/2woi3TUPgdbIdUE2Td+xINr9PO9AhgD/AG0KLA9nC0B+KvCfxWTVddoF611XDkAhHBwbrZvcQEA7Cuawg8ARQABGSsBThhPBZzooIWTTjrJgIFsJfYJoE4Tyjh/Y8hD/Gv8TZORz/ewpKBfJdLfCgxkwsf4TaS/lRx85rUM2hmfJIs26MllyKRqAFA0SmfR/+EaJBAkUK4Ezj33XAOqXXrppeZwNADAZAD8u+mmm0rBJJJ55/kfTAQ6XvziF5sDrgAnBVAqH+YNX/va16JzzjlHt8b+GgBAD5qAtgBD6qgAINuIsfb73e9+17OSa/v340htJjUazD0Q0UASpbCjrKG06f+BLzn8UPS7CmhCF+0L4I/2qkAHDPCHnz8UOLYdEVzlQ3QPuqoufOZDyrQmM4P4reMZ/Y7aD5NPBbat0X7sLZau0S5a67zKAnD39JKskuXPxt9kJ+7/joq/uxBGk4C+E7U9rFBpl8TkQQtYrhLvv/9+408Qy0BAQb07GiX1vi2+7T6vXgqaLU3jlI91l1dy6Jz4CgTg5qAQxmr6ZPwFKuAjWluE7T5Zz9t+VXuAz3H9JpJ1rDlDlYDoOHx/SbmG/6uTwAI+ACv2qVc29dBcR3jZy14W/fSnP42wBgRgw2CIhSLGhde85jXRu971LmfcNkHTPffcE/3jP/5j9M1vftMYl2DohGspgEG2KbO4FcKSBAIAuCQLZ3/ZFoBMMLAmyBNQ3FHiAGSYjCj08++nwVuDudL7ehU/0I/S5ruyJvptBdSFugE0llUpHa8ClloCbnSPqxQ5TSztZ778Fg/Q6ysf4qFp+plcahul3X7o72g/ZVg+u9auypD5ZNynHx0Dew8f7A8AwvdsDKweG/uqC6E6CQCc6KAFrJ8FnDCW8pvIydTaImyP7dVRVU7OZbTVcihpJhfxr/G3GSrqLZWxAcCauGHDBuMvEOCPQJtmMZmIVb/AQJ/a9CjStPWvcWoTg2SmOYOtcw9KH54FCQQJuC0BQLNPfOITJuahlMM5NGbmec9O2+/gDzuN/Zuxh0NAiCEMl0CYDQyXUeMpbIXKtqgaRhgKCqu2Sf9+5AcqzsQ6baCWMqPBfFg5rj8XP9BpK22u092PPvHjCi9YaQHcMLlVm4FG26o0jRfX+Eijcdg9gWekG3WwG1ZWVc/FQ1P0T09Pmz4K8NhuP/ibAvgb5EetadqL1onoLvq+3uNwj1/v2RedfPyq6LczS9uk9dy+sg04AIC2RIr91ncyqA55xsIHkYMWGIdp3yzE4ct327ZtJmI5BRhIX5l0ol2MuureysJ3daU3n/O484+uyGnXCmvWrDELymx7RwdQm8Z1jNo01iJtDRqr4C9Nj24r34P4kkyCPAZJKTwLEggSCBJoXgIBAGy+DoZSwASYCQUKaBYAUP79mHDYljSs4jKhPvHEE7vWV2mFa/B2BWBKozHPPfHDO1JQ8rzvWlpXgDNOxMSqlFMENTnCzxXKPwDzMOXfFT5GqV/xQB6SwSj5NfGugIy66acvo/3ga0pl52k/yKop2puop7Qyf7lnbzQfjwsnrjxuKAA4HQOAf5CWSbhXqQRo06eeeqqJtHkWShibk77VWJDDigrQUO26UsJyZq5v1EXacrJSKLn0oXHlH6FJBvxmnOfwLtzKyLqVNg0gSMRfIO4+aNNcbT2M930Pti5p6wG+8zUK/ZJJ2XVN30Mc529vlHoJ7wYJBAkECSQlEADApEQc/B/lgpMFUbRsZ/hJUvv592NiASBj+xJMvmv/r8Fbg7n9zMfftnJmK7A+8gLN4qcJXlDC2EYOcIM1iwLb2mhjWG1ldXYvZU4TS+Xl01U8QHMT9VGGrMRDXfUAcMzBHgDHCoDFtB8mlVnbD++KduXjy1V0jyrzyV2L3+BxK4YP5eEgkHJah+pMdZgnV8ZxfNWsXbu261sNAJyxlivx2GOPNaAJ30LWMTsPDUXTjsJ30TJdek/8a/x1iba6aLHHOMmBHSXyF8hBIYCBWLziEoQ+nkifjpUrYOCwBei6eBm1HFsW0plHzdP39zVnUNuogp8i/W4VdIQ82yGBzkKsuy/U41OvLIlBcwhBAqNKYPisYdQSwvulSAAlC4AvaQGIUgoQwzZffA4p9PPvp+eDrlJmNJgPSuvDM1sZsZU2H2hPo1H81MkLZT344IOmndkgNH7ZsCplhV90pdGcdk/p6+QjjY5R7tnKqCaIo+TXxLvioUr61U8l/ZDqRGgmh+p3isigStqL0FPXOzoAZOKo4QrszNx8XWSFcoZIgG/O9q1G34pVIGM4W+J37NhhIgcnAQSysDLMonpIkaU9Vn9RWoaeZKRxalz5p5okA35r/OY3AbkA7hHxF8gJ7oCBAIC2f1f5C6RNuwRwL3KR/nd2fk+04ujH9jwcJIuehGP0j+YMeRbx8ohnnL+9PHIKaYMEggSCBIZJIACAwyTkyHP5AQQEJLDVYuvWrcafkA3IDPPvl4UdKXa2gpPlPVfTiB/ok4LiKq1Z6BI/ddQPijtKPAAzE1OFtBNZ9SzrVXz4DN6gkBLhoY76yCrbPOmkVFdRD+TJRBCLPyz/FACOcS6sE6F1P++1Strz0tJE+q27Fg91mplfOp2zHx3BArCfZLLft78Rtb3sb6enBPjWAQr4B6S/BQxkrN+3b5+JmzZtMv7XAAPZIqy+Mz3Hau6qfyuL72qorC5X1X0Tsq+Oq3w5qw3w1iA58IydJ0Tc0GARSLvGQjDpL5C27xLAnSaRqdn7o5OOfnbPI+mS8Dqu30SPQOJ/bJkkn4X/gwSCBIIEggTckUAAAN2pi4GUaKWUyfRVV10Vff3rXzenDfKbgDUBW+i4jqqMyBJHg/lAwjx4iDxQ0lBebQXWA9JTSZTiXSUvaQczIEcUeiz+1B5TCcx4U+2UiRVR/2d83Zlk0C0enCEqByGSuya4OV7tm5S2yaQPiz/bahnrEIC/tmwD6yuAIQ/Kkvm23YtbgPfNLoHz/YoOAGA/ybhzH1cKp512WsQBC/hRAzRhWzDbKbESJGIJCGACGIiFYN1BbbfucpsuT+PtuPKP/CUDfksP4fegQHtFZyDKBybtmt/yFwjADbANGMhBI9JBB+Vb57ODs5ujk47rBQAli6xyqJPepsrSnKHK+hvn76+peg3lBgkECbRPAgEA9KROUTLYVvHhD3+4e7DHPffcY3wDPulJTyoFkJEoNHhrMNd9n6/ID4VNSpvvvEB/FbxgTQpow0RTgBDtgckmADP+qcoKtuIML2p3ZeVfVz5SSCWvusotq5wy6e9nMYqlH8Bf2YBFmbSXJc+68tkfW9b8dmrx5N+H4sn0sDCdwUpwWB7j/tz+xtX2qpAJeXNCMHH9+vVmGyVWgQ8//LAZ/7GoJWJJK+vBMvvmNJ7Ee5V8p5Xryj3xb49brtBWFx22zlFEDrYPzCTAzeI2ke2jLDTSrstY0C5DNofnti3LRvqxr3rLMoZKuCGZVLUFuAQSQxZBAj0S6CzEc8M4+hR8o9cn2Y4TrQEAdLi2UThvvvnm6BOf+ER03333dSllEv26170ueu9732tOFuw+KOmHFBoN5iVl22g2UlbbwJN4sZXxUYRLO2NrDsCf7UeSCSWgH4p4FQqd+ID2AACOUoOjvasJvSa4RXJjm9cDDzxgIiAggXzx7Qfwx8SviiDaq8jb9Ty3795jSDzhuGOjR+YODyU3WAAOFZGTCegn+Y6IWGZjEQgYyJZ64ubNm6MtW7YYCyoWaqo6cVX9w7h+cxpvx5V/Pg7JgN+jyIF3bYAbYBurQPkLpH0TsYgVwF3GrgPozh0WZqODc79a9ppkYesxyxKN2Q3p10EmY1bxgd0ggSAB7yQQAEAHq4zJ9A033LAM+GMrxUtf+tLo05/+dKkWf0kRaPCWgpN87uP/AjXbwFNZ9YMsWHEH+LP9s+FHku06rMKrrCrq3J5AaHJZRTlV5ykZ+dq2VA9F6gB/TviHZPIm/vnWqrAYHVSPRWgflF/VzyTzUcrZdsT/3+NWrYwe2R8AwFFkWeTdMuowb7ksyqxevdpE/AMCkgAIojPIgooDwAAL+QZPOOGEkYAamz59Y03wbdPR1G/xr/6+KTqaLFd9PG2grHaAPNn2S2Sru/wFcrgdPjG3b99uIgvfgIFsf6/a2rVHxp1fR1PxISCznQPRiqOO7z4KYFdXFN0fkon07e6D8CNIIEggSCBIwCkJBADQoerA+uozn/mMAfhQghRe8IIXmFPUbrnlFrMlouqVUA3eGsxFh89XKe1SYMeZF+oVwAbgBgVbAb9sAH91bbtRnVC+z/WiiZAmiJKnL1fRn4detoqzBRGLDfEN8IDFKMADv+sIol001FFmmWWMQvfkEf9/jz72UVG0uBN4IGnBAnCgeDI9HKW+MhWQIxGACPHMM880W4MBA/keAVHo24ks5gCa8E1iTTVKEO/65kbJy8d3x51/6kzjtD12l1mXGkMYR/ARCLiNrmIfiIPFK4eQ0a5ZpJS+WiYddl4T8zujvXN7YwBwXw8AKFlUXb5Ni8u/kYe+kbJlonzhf1z7H5fr3mfaOgsT8RbgCa9Y8I1er4Q7RsQ6DQAywfzUpz4V3XTTTUaZZdVv7dq10Wtf+9rone98Z+FtZTt37jROtvPUM9vYeK/K8Pa3vz361re+ZYrA2u/1r3+92eZ79tlnR29729siAEDboX5VtGjwbhMA2CaepHxLAc3aDrASYVLIRNHepsnKO8Af/qTqDOKDMvPyUiedw8qSQmorqcPecel5HvrZKk6/bG8VB1ig/WCZoe+sLv7y0F4XTXWVs23X4gEgxxydzX9NAABHrxn7G1fbGz3X0XKgH5UFFX08i4f08fhYAzjZunWriYAmAIFFQRPx7grfo0kt/9sao8aVfyQmGdhjd35JZnsDtxHo+xyKg7UrQKCsXdkyTGS8wdoVMLCqhcv5zv9Gc9FsNNPZG62K/qhLfJ2y6Bbq8A/JAxKr0gPG+dtzuOoDaUECQQIeSsBZAPDGG2+MLr30UjPwS66AX3fddZeJn//85w0wePrpp+txpVdW2asO7373u43Pv3e84x0REcVGQVZ/dQCAUu7sAV10+HptE095eWESCPDHxFB1ioIGYMNK+8qVKxupVluZ0+SyEUJGLDRvfYxYXOmvqx761QH3AfwA/gAVFOiTAP4AHyQDPQvX6iWgE4DnFjqZCgsAYCYxeZ3IPnEVK12AQIATfAfyDRMFmgAG5jmNW/2D+guvBVWAePE/zn2d9Ic6ZUB7Yys7UdautGmsXVmkpo0TMRAACCSWuZg5N/+QaS0AgHbQAnlVYJddlg+/tagMrUEmPtRYoDFIIEhgnCXgJADI6baXXHJJhH8pJplXXXVVdOGFF5r/8Y33uc99zji9fslLXmLAwLyDPYrvz372s6H1fs0110Rf//rXTbrLLrtsaPpREzzvec8zQE0aIMNWHgJKfdVBg7cUnKrLqyN/Kaxt4Em8SBnvJz8d7MFKuQITxFNPPbXWbZoqO3kVH9wfxkvyXZf+14RYE0SXaMtCSz/6qRNOg8ZHJCCyAs7bsYiuyuJC5WS59qM9y7tNphmV7kfisXHXwUOGhYNzs5lYmY4nyyGUJwHVYXk5lpsTuhOAybp16wzwB0jC92yDJugaACboRMMO6lH/5jrf5UpxKTeNUePKP5KQDOyxe0lC1f+iXFm7AjjJX+Du3bsNyL1jx46IqNOxWeQcdev7fGdRf2ILsB2kSzYlC5sWF35LHtCiOUSZdOm707XMvENeQQJBAkEC4yYBJwHAyy+/3IB9nDz6ve99LzrvvPO69XLRRRdFZ5xxRnTllVcaEPDaa6+Nrr766u7zLD/wM/KkJz1pYFIGM7bcElAmXvWqV5nfVf5hYEsD/yhTAGAdFoAavFH4UfjaoOCIJymwVdZj1XmrPtJ4oc50sAfbZhSY3GGthVWp3tezpq62IqfJZVO0jFKu+PCVhyT99H1YWAD8YTmkcPLJJxvgD0uMEJqVgLb/QsWuw4tA4DCKZgMAOExEQ5/7+I3zffPtEpOHLLDIum3bNhOxBgQIZIxIO/VdvKu/GCqsliUQ/66Mn02IVzqHCzKgjbKYSaQdy18gi+Q6HVv+AmnXAIdp7XqYHDudRVcLMwkAULKQbjksn7Y/lzzgM8ik7bXdHv46UewDMI4+Bd/o9Um240SrcwDgHXfcEd12222mDt7ylrf0gH+qmCuuuCK6/vrro40bN0bXXXdd9MEPfrB0p/Pf//73zSSYMl/96lf3BeZEU9XXJgBAeAIMcEHZG1W+4sFWUkbNs6n303ihnlgNB7RBGVYArMFaC/9Prk3cxAe0+lwv4kMTRMnel6vaBW0I64kHHnjAAAXQzzN8htGG1Ae5xJdo91X2RWWpA0BWxBYxxhIwg/46PRcsAIvKW+/53s6ShyxoizBjBqeuEu+//37zzQOa2OOGeNc3J5mMy1Vj1LjyTz1LBhrzXKl7Fs7xFbhmzRoD/tGu5S/Q3vrOWIbFq92uB/LQ2R3NLSxaWPcDAF2TxUB+KnwYtgBXKNyQdZBAkECQQMkScA4A/M53vtNl8c1vfnP3t/2DAfeNb3yj2RrMNsebb745uvjii+0kI//+8pe/3M2jju2/3cL6/JAPwDq2ANsKjRS+PmR5c1s8AXL4HsQLdYOFFtZaNmgDf6eccoqx+HPdWgte4MPndqYJoa886Jtge6Am+dQLEyWsRkfdQlXl9ybZi+4qyyoz71HplgXgKY9eFT1weCoTacEHYCYxZU6kOsz8gmMJsQpnNwV+lAH+AE1YRKI/ADwh8u3TDxD1jfnOd9FqEP8af4vm4/N7GuNclQFt0z4dG/AP/Uhb3/lNxF8g24Np16TvFybmd0SHFhafzvbxAeiqLPrxVNV9u22Max9RlWxDvkECQQJBAmVLwDkA8Pbbbzc8Ym3y1Kc+tS+/559/fvfZD3/4w1IBQLYPCIhcvXp19PznP79bVlM/ZH1T5xZgeBU40BTfZZWrLQlSUsrKt4l8bIXzxz/+cRc8474O9hjmz6kJutPKlKKoyVVaGtfv+coDfv2wGMWZOoE60LYqDofBWsj1INm7TmfZ9OkAkMfGAE0AAMuWbrb82tL24AN/nsQNGzYYsASQBPDk8OHD0fbt201Uf+BzX52tZtNTSXdoS72nczn4rmRg6yCD32juKTSyEErEOg0QUO2ahdOdO3eayOI61q6p/gI7O6ODnTnDRPIQEMlCumVznLpRsuYKVctjnL8/N2o6UBEkECTQBgk4BwCyrZfAqvQgfx3r16/vyl/vdG+M+ONb3/pWJKDtDW94gxNbJwMAOFqlSmGV0jZabs29jV8/fDYpwA8TMx3swSEfPgXqBcXR53qRQurLxJiTfDnR1z4chjaDr9Nzzz13YL/ratvyRfZlyA9eJ3ct+qU6dsXRmbOcnl+cyGZ+ISRcJoG2tzN0LsAQItuCAUywDEQfwn8ggf8BVEiDX0GNrcuE1bIbqnv19y1jLxM7Gqd9q3O7XQNqY91KO2ZHDRFfgfIXiFUgW4V5BwvAqc6iH9zkFmABXr7JIlNFF0gkeVQBAOrbK0BWeCVIYKAEFhZiH4Bx9ClAcwhBAqNKwCkAkIFZk1IAjUEBh9WAYlix/PrXvx6UNPcze/svW41dCNoCbJ/GWRVdtkIjha+qsurKVzxJSamr3DLKQfnBGgNrLba822Ht2rUG/KtC6bLLqeq36sXnduYDD7QhTkoE+LPbEJaibPPjGX6UBi26VNUGRsnX18m46C4ysXnowMFo/8zMothi662sYXa+kzVpSNdHAkXqq09Wzt+mP2B8wbcaiwb33Xdf178s1lREFpwATAADWUBoc9AYpf6+zbz2460NMmC8w1dg0l8gVoHyF/iLX/zCgIBPOnVzNDV/wIgjeQqwZOGr7tWvjovel24d5FFUguG9IIEggSCB+iTgFADI1lsFAV76P+0qALBMv3iALLfeeqsp7tnPfraxREwru+57sgBkFX4mnvxVaenF5BQlFwVHg3rd/JZdnpQSKW1l519FftCqgz1kkUo5fBtq86xUi7cqaKg6z1GAkKppy5q/yzzQhjgVGuBPbQa+8HvEwR5Y8ExOThoA0Gdww2fas7YzpZvctVs/o8M5rPqCD8Cu2Er5oe++lMwczgQ+H/vYx5o+A6tAtlQS6FfQRbSVEgAQIJCtlPhYa1tQHzPOAGDbZECbZTfRmWeeacA/LF7lLxArwfWnTEZ7YmvX6KgoOjz7iHGToe9euvE4twf7G9chIFXpo5K7XWb4HSQQJBAkECRQTAJOAYBYACpkAbikZNqnnur9otevfvWrXWfXrlj/wYsAQH5jBZhFPqQtGhjE2wQASknzAQAE5GV7Cgd7MMFSAKzhUAYsM/B7SfCBH9GfdvWpXtLo554UU02O+qWr8z6TE8BjgD+7X+X0Q9oQE3rRratL9GeVlc+0Z+UxmU4HgHB/78zSmJlMl/w/bAFOSmS0/9X2RsvFn7fVP7AABWCiA6i0lZIF3E2bNpmtlICEWAY+/vGPb80WYfE/bvVut1DpGxq37Wc+/6ZO0a+IAFn4xf3t//4qWnHMoVjHitG/OByKAUD0Lto1ILdkURXg5Zs865LHOH9/vrWJQG+QQJCAuxJwCgC0T5u0gY9+4kMBJQCIlBW+8pWvmKwAFy+55JKysh05H9siEmswtkBXGVBqAKK0ylllWXXkLYXVZX4AatjOziq06ETZecITnmBAG4HAWmlFblK66pBhFWWoXnzmwyUeaBsAx0nwGEtRgL+0bXpSqDXBraKeq8pTtFeVf1X5jkK3DgCBtofixaCsIVgAZpVU/3Q+fiP9ucn3JMk7OhLbKFevXh3hm5Zxi4jeAIBCxD8tYAmWgVgdj9Lu81FbfmqNUT7zMKpUJAONeaPm5+L7uMEA5Pu//vBQFO07KYpmjrhdOfpwNLV/KtqyZYuJAv6S34WLPNVBk3RWyaWKMsf526tCniHPeA7loQ9A33wWhnbmpgScAgDtyam9Xa2f6OQPzwbH+qXNcv+OO+4wK9ikffnLX26sZLK8V0cagT+UJb6rLFeDuBS+KsuqI2+X+cFyQqexSpmU02p8YcrSVXKylW/f60cKnfgWjz5dXeCBxRDAY6xxpIjTTph8y2q0n0xdoL8fbVnv+9p+itCtA0BOihe+Hp4/mFVEUQAAM4uqb8Ii9dU3M88eiHd7/IEF+o8TTjjBRCwD2RoMEAgACBjI2EZEhwEIBFyxF3t9EUM//n2hvww6pW8k20AZebuWBweAzB2FX8sjAODEQvT/rHl89NBvp4xVvcZZDg/Bry7tGgvCcZBNWl1JHtK109KEe0ECQQJBAkECbkjAKQAQpZAtajjixYJlUHjkkUe6QNgTn/jEQUkzP3Px8A8RXzcAKCVGg7ro8PUqfqTANs0HkwkOXmBiRFtW4BugPQPc9DuQQYAN77jCj+jPe3WtXvLST3rVhyaIRfIo+g7WwLQh/BWpfIHHtKMsrgKapL8o33rPZ9rFQ57rfOzTcceR/uKkVcdFD0/lAADn5vMUFdIOkYDa3pBkrXms/mUQQ/TnWBsT2cWh01axEGThUtZTACUAJj75sNVYO271bte3ZKBx237Wtt8T8zujmei4Hrb+79MeH21Y9/8a3e3uu+82+hffBe42iIy37Nhog8VrD+MZ/tFcIQCAGYQVkgQJBAkECTQsAacAQGRx1llnRbfddptxTM92tn4gCL5mFDZs2KCfha+sVN9www3mffzWvOhFLyqcVxUvolgwsDLI2gdCVFEWeWoQ16BeVTl15esKPyjQWEYA2thWrli/YqmF76RhyjUTENKQlxTyuuRYdjni1Wc+muABq1H8+9GWFOgjAP2YfPTrN5XWvmpCm2WCb7/nwm/R7gIteWgoSvdv9sXWJ0eAvFWPWpGnyGABmEtaIXFSAuof1N8lnyf/pz/ioCEi/ZW2CGOt/PDDD5tIPyXAxPZLmszLhf/z8u8CzWXToHE6axsou/w685vo7IhmFnr72JnO3ujRE080hgrIAHmwDR5djjYN6I1uR2TRHpCbxdxVq1bVSXojZWmuIF27LCL47ohFx8yy6Aj5tFMCYQtwO+s1cDVcAs4BgM997nMNAMhqMStsz3zmM1O50Em9PHzOc56TmibPzZtuuslYHvLOn/7pn+aaQOcpp2haBj+2Ou/du7cHOCqa37D3NIhrUB+W3vXnUlilwNZNL2A2EyC2acp3JTScdNJJBvjDp2MeBUfKZ1P8lCU/8azJVVn51plPXTwgI6xFmVxgPaqAD1Qm2Uyk1c71LMu1Lvqz0FI0jc/tJ8/kxvb/d8zRi87ps8psOl48CmE0CdjtTN/NaDn687bNe16qWeBie/AZZ5xh9CxcFbB4YfssBSQBMCG6CJhorB23erfrWjIoMs7Y+XjxO7YAPLTQ6198trOvS7pkgTWrDsXB4hU9TxavW7dujYjod7Rrxmj8YrYxaK6guUMVPI7zt1eFPEOeQQJBAuMrAecAwFe+8pXRNddcY2rk+uuvTwUAGXi1XZdV4wsvvHDkGlR+ZHTZZZeNnF8VGbCiCABYhwWgFDwpOVXwU2ee4odJDDzp/6ppkG82lEImOwSUGB3KUNR/pej3vX7awIeU0lEmyIPaIfniVwuLPyxpFJhUA/xhNSoa9CzPVe9WRX8eWvKm9Zn2vLyS3j4BeDbux/KE/YcOmcWHpE/RPHnYaX1sLzb9RX6PI8+Sk3hXn637ea68S39FZNcF2yYBA/Ghhl4zOTlpIgtjAkzyWDPnoSVv2jL4z1uma+mlb4zSBlzjqR89WAAe6KzreYwFIIG2kJQF/erq+EAcIhaBsnjlcDcW7ogbN240bR8r/bb5C6waANRY31Mh4Z8ggSCBIIEggUIScA4AfMYznhE973nPM1aAX/jCFwwYd9555/Uwd+2115qBlJuXX375shW1W265pQsKAuZ98Ytf7Hk/+Q/WNFgAEp785CdH55xzTjKJE//LD2AdAKBW8TSoOyGAEYgQP2RRBwCIAoil1oMPPtj1zQYNKH4c7DGqE3Qp4FJCRxBNo6+KD02uGiWmYOFV8UDdMkGmHdnfPNYEAH95rUb7sSfF2sc6EO39eHP1vk03crf/H0Tz5O4lf6EH5mYGJV327EAMADI2Ar7QD3FV212WOOeNrPTnzNbp5OPGc9n9A5ZQuCwgsuMDwAQwEMAEnYwIYMJiGe0VULApmcO7+G+KBhc+BukbZfUbLvCUSkPn4WhiYSra35nueTxzxAJQcuChrVsqMQu769atMxavAH+0bcZyFoHRCYk6IRugm0N0fG9XmiukyUNyCdcggSCBIIEgATck4BwAiFiuu+46s633UDxhufjii6MPfOADBtDjf/z0ffaznzXSY4C94oorRpYkeeK7g+Cq9R+0yVqszlOANahTvs/BVlht5a1MnpggYMkAYMNBNgqsDDPJQdEry5pB/FTFi2iv+iql12c+yuZB28VpR+qXqAcAG4C/xzzmMaVWi+jXBLfUzGvKzGfa84jItgB80LIGzZIH9sfIia2XRHy00ScBrmBNGkKQwCAJ6BtTfzEobd5nLG6yPfj00083wB9AICAJ+ocsqVg0U3vVYmjecoqmF++8r7G3aF4+v6dxuoo24JJcOAGYMDW/v4csbQG29eJB7QE5AVwT8VWOJT9tG3+B9gnZrm9/7xFCn38kkyoBwLa3uz6iDbcrlEDwAVihcEPWTkvASQDw3HPPjb7xjW9El156qfGlAQCYDIB/WO2VMXHR9l8Grte//vXJopz5X0pvHQCglBopfM4IoSAhtlIiRaVgVsteQ0YodgA29hZN6ouDPbBgkDyXvVzwhvLzvX7awIeUUnuSWKRaAfs4/ZxobxfHbxDAX1V+scqivwjPo77jM+15eZ+NwZCdj+wxr62IJ5ZTuBSYyJ7L0THgx9jKBJT+iva2c+dOE7FAAQjEYX1bfVRll1T/lPY3rrbXP3U7n1TJN3k/7nGPM1HWUrRXrAGxDNy+fbuJuH4BDKyrvYZ6X2zL0jc0brezhcdcdXYa1vbOLW75FZ/aAiw5cN/WLZUu7Uo6xnIifa/8Bcqtj7a/q22TjkUaX4L06qzy8IWvQGeQQJBAkEAbJeAkAIigX/ayl0U//elPjTUgQB+TYgZDVohf85rXRO9617tKmRDjoPcnP/mJqdsXvvCFZnB2taLrBAA1iGtQd1UmWemyFVZbecv6flo6ZIN1Agd7MDlRYGsmwF+VW5bET1m8iPa6r23gQxNie5KYR45YNgMeMyFQffL9AchgOVqWz7Z+NI1Kf798w/3+EpDMSZGl3bDoc/sv7o/mYws+wmNWHB39PsrnA3Am7q9YjCDim1RbLnFXwCSUuGnTJvMcNwVV9l+GifDHKwmob7LbbpUMYC1PH0jEBYIsAfmNpT2R9vr4xz/epAE41HhSNl3inXzr4r9sHsrIT3KoSs5l0FhGHlgALkycEM1Hi36blacAQFsvLiIL+4Rs+l+BgegCattsf6dtA3SX6a5BvJR9lUw0dyg7/5BfkECQQJBAkEB5EnAWAIRFrF4+8YlPmJiH5QsuuCDTpIo82XaSZQKWp/yq0moLsO0PrKqyNIhrUK+qnLrytZU0KbFFy+5nqYWyBvBXhlXqMNrEz6i8DCun6ueaTPnyDabJo2hdoPhzsAfbMcW//GIx6a3LEsvnOvCZ9rS2lLyHRTFWeljs/Xzv0na0x8XWxb8/sGgNmHyn3//T80uTWUDlNWvWRKtjh/UAf1hZMQnF6oorkS2XAmCqsj7tR6ur9/WdQp/anqu0VkVXE3zT/lj8Xbt2rQFIaK/yqcaVSJvGIpA2W/YYbNe7+vuq5OtyvtI32i6Difmd0fzEY+Oq2N1THdoCLDnwULpyT8Ic/6DXa/s74J/dtm1/gVgEAgZiIdjENziMJc0VRpXHsHLC8yCBIIEggSCB0SXgNAA4OnvtyiFYABavT1thlaKSNzeAVyy1mGxIAUTZYdKBpdbKlSvzZlk4vfgRHYUzavjFNvAhZdyeJA4SK0o+wJ/tJxKwBfCYtlS3Ap2X/kG81f1MtNddbtXlAcoB/NltZFdn0fqPso9fdVwUHchHxez8cotB5MeEkrh+/Xrjd40JKOVi1bxt2zYTsQbEKhDrwbrbZz4uQ+qqJKD+rclvjrKxsCfiU43FE9orPtWwauWbIeInFSCQ/rSMbZT2ONsk/1XVbdZ81QY0bmd9z7d0nAA8F+FrtxcATDsEpCxZJNs2iz5YvXLFXyA7TYjomQCBRM0JXJCv9Oqyxwe1OXgc52/PhTpuIw1oVZ08vlQcEMKSJugAMYEEbyUQAECPqk6DffABmL/SUBxQ1FDkbWU+S05MxgFsmGQoMKlgQlynpZbK5iqlMy8vdh4u/JZCZyt5LtCVh4YsPMAfoArtiPakwOo/wB/Wo6pTPavrmoX+umgpWo5v7Ucyt/mFB06MBMAAJFbAmml1bKl34x336FZURAGci/u++TgeHfeDaYGJmyaW2nKJ6w37VFa2ZWKJQt+XxkNavm26Z7ezceNfvLvCN+0VgI9IG9UWYSyr9+3bZyJbhMs49Vq805ab6qdd+I6kb7RaBgvxgYCd/41mjvrDZSJPbgHmW6hCFrRt+lkiO05YdKZ9My6wTVgLM/LdSroygO5lDGe8wfehtlE2ACgSXOl3RE+4BgkECQQJ+CyBAAB6VHsCAMMW4GKVhqKGkiJFZVAuKDQAfgA2TCYU2IoEYNO0JYyUziy8iHYXr23gYxAP1A/beLActYF7LK5wceCCnzUp1vYk18W2kkaTz7SLH9oIhxwA/NmHCDG5A/hTG9m2ewkUPGxt51U+Wa74AVzZBwC037e3XEIbQCDtmC3C/CbWafFs0xZ+NyMB9Q/65pqhIr1ULKhPO+00s62d8RqrQLaxYzmlU69xqQBYyKIdFoJ5+LDH2TzvpVPr713JQWOev5wMoLzzy9geqBNNLyyfHmkLsKzd6pADwB46JxEdAiCQts08gMVEIv4CAbpZwOFaFQjXT2qSB8/rLrsfTeF+kECQQJBAkEB/CSwf4fqnDU8aloB8ANpAQlUkaRC3B/aqyqorXylrg3jiGautADastCowGUcBO/nkk3NNHPR+2VfxIoW87Pzryq8NfGhCqAkysqMdoajTjtiapkD7AfijPbkS0uh3hbZhdIj2Yelcfn733XebyZxoBPAD+AMkVjgUAxm/2bu0EPHI9NKhQ0qT5WoAwBgIyRqQr05lBUxh4gm4wqTT7h/vuece4wbBB2f1WXlPS2d/421oe2k89rsn3l3mG9roW4lsaQf8ox/WNkr6YyK6lLYIAx4OC+KddBqzhr3TxufSN9osAw4AIRxKMbOeWzgYdRbmuovI0pPrqmuMAGx/gbRt9FUb6JaVNmAgW+Xr+F5tnboKmYgHXeuSdygnSCBIIEigrRIIAKBHNSsLwDoAQCl4Uvg8ElNfUqWYpPGEAiXLFn4rMKEF+HMJsIG2ttSPFDp7giXZ+3K1eUhrRzzHYhTgT9+wS7zZ9LtEVx5afGo/9D8AEwqy6KavoY1gnZQMO2LrP81HJ+KHDx3M6QDwSIYAgEUDFlSyRGGb5fbt2w24Qn5YSxOxVsHKii3CZR/EUJTu8F45EtA3pv6inFyry4UxUtsoWYQBvAYwwUKQ9rt582YTWZQBDMQNg3SEJFXinfu+8J/kYdT/kYHkIP1j1DxdfJ8DQAgH+lhZsw1YOmRTcqAN2r4w5S+QcSVppU1/DBgoAwLDXMl/qgYASyY3ZBck0JVAZyG2942jT8E3en2S7TjRGgBAj2pb4IEmjFWSLkUYRQelrw1Kr5Q1KW/IDysWHCszMdB90qE0cbCHq6dfpvFSZXuoKu828CEe8NXzox/9yFj/IS/uo3gDmmSxMqlKxsPy1betyd2w9C4994n2flahAA+cyKv+PU2+k7sf6d4++fhV0YOz9QOAOEHJKAAAQABJREFUXQLiH0wmV8dWivSbBMBLAEC+AdwmEAEyAQLpS+s60doQU+Ef+xtR26uwOKeyFu8+8s0JwbRXItvssWKl7dJeBV7LcgowEOtbm0/pBlSI+nunKqcGYsZFBhwAQjiwkG5lzTbg+flFK2oX2gI0sMBIZAFS/gLxJ4t+y0INkUVs9BFAcb6HMkMAAMuUZsgrSCBIIEigegkEALB6GZdWglbwWL2uOggApBwGd5Rj34N4gh+sANgKZFviMElF+WfS2qRD5SxyluJpK+VZ3nMtje98YI3LZJKguuBbATymHfkAfGiiqwm+a21kED2ifVCapp/JIoOFBlkXQ7fkfeaZZw5tJ9t2LQGAJ648rnEAMCnTc845x1ieAKrwPegghvvvvz/iIAYmp/StbCf2oc6S/IX/o2579b3+sExle/C6desM+EebTfq3ZOGP9gpggq9L9e20A9/5L9qWbRlo3C6al9PvHbEA3DeXrmcvWgCeZFiQTukKP+gb6B5EDAVo20TbXyD9MVavtO1BVq95eLIBwFa3jTxCCWmDBIIEggQcloD/qI7Dwi2bNFmI1GkBCA9tAQCluLPVd3Jysls9KPgoTFiquKbQdYlM/JCSZSvliSRe/Ks6ERjiBdExkfhAw8oJ6xE74J8HxdqXdgTtqgObDx9/04Zc4gXrIkA/+htNkPhuARaYgOE3L2uwLQBXPiq7D79k/tMjbAFO5pX8H6sSLBmxsmKBBb7ZdgkAypWIJSz8E121rk7yZf+vfsqldmbTV+XvtvHOtwgAQgSYp33KvyU61tatW03EJ6f8cVLv41j3tCtb15D+UWV7aypvtgDHI0m0b37J56pNy4yxAFz04euyHOyDnNBXAAJp47R1tgwTWaxkcQadRYdN2bxm/a3xjW+jSpmM67eXtR5CuvwS6MTfum9baqE5hCCBUSUQAMBRJVjj+00BgLbiVyO7pRUF/WyLkOXk4cOLWzvYosb2TLav+aZYSMnyvW584oMJMCeiAvzt2bN0IiugBm0KXgCSfQtq+5rg+0S/aIdm6Lf/b4oP2gLAH2CCvk8mWliEyrrYXsTJIvdtu3Z32Tn6qOLK3yg+ALsEDPlBHdgHMWBdhSx27dplvpNt27ZFRCacAIFMQNtgYT5ELN4/Vjt14RsrW5hYTtn+LWU5xbdMn08kIAPa8ShgSdm015Wf+jLK07hdV9m1ldP5fTy13h8tTJwQzUdzqcWyBViy8GGhj+8VAJuI5SuLlrRv+Qukbyaix7AITp+s3UapAki5KQCwin5c/U5KseFWkECQQJBAkEBBCQQAsKDgmnhNgzLbDhkUq1TEbQVPg3sTPI9SJiudKDpMxrHGUUDROeuss8wktUoZqrwqrqofKaJVlFFHnj7wgYxZLQf4E4iMbACQObSBScC9997b3SJXh9zKLEPfAH1K1f1KmXSTl2gvO98i+QHq4VYAKwtNWrQlC+Cv6ORo3+Hp6KEDB7skzXSKH+RRBwDYJTT+wbeBdQkRf1SabPJbwArbhDXxTPpes/Ny4bfq1aV2V5dcxoV39Cy2B2PNDdgnyynxf+edd5ptwbRpXy1Zi7QZW9fQuF0kH5ff0QnA8xEnsC8tutg0my3ARyypfZMD9NpWr1qcwV8gYPeOHTtMRLehfdMvZ/EXqDmCb/Kw6zX8DhIIEggSGCcJBADQo9qWBSCKKBOoKrdQ2SubGtx9EZUscFDcRTsTNhQZnuGHSlt6fOEpSacULVspT6bx4X9NpDW5colm2g6WowB/tBsF2g/WIgIrZA3oIg+iedBVdUAaeLD/H/Sea8+akj+gMG2EyZQCfQ1thEmU3ZfqeZ7rNusAEN6bml1azMiTD2ln5oqDh3nLSqbH1cLpp58erV271oB/gIF8X3xnOoGdMQ5QBbmxUBOCOxLQ9+Vr/5BXkvDJVn0iuwTuu+8+0zciB/QvWbJyGivtlcMVfPD5mlcOSm/rGtI/9Kwt14nOTsPK3MSj42s/AHBfdFS8KEgYtW83mTT0h7Yqq3TaM/oyEQMDXDgQOSkbfYf2jaV2P36lZ/d7PiqL49LnjCqn8H6QQJBAkEBWCQQAMKukHEgnABBSsDapEgBkwEXJQ+nT4O6ACAaSwOl+OthDkxWsbphQouhwEhrWOb7wM4hZKeC2Uj4ovavPXORDhzYAStiWoyjAgDo4kbeDeOAe7c43ZdU3em3Z27Trm7efV/mbCdLOnTt7/EACcmEVChhgt4skHXnoTgKADx9asgZM5jvs/ywWgBt/+1C04Y8ePyyrws/hnUklccOGDQYEBAwESGfyuWXLFuN7DeCFvhuLlUGyLExIgRfVxuz6K5CNl6+MM++qb0CTZz3rWQYooc0CnGA9Rdy4cWPXnxptV+94WdkpRNu6hivfYwqZo92aXzwBeCbqv/gw29kbrTgCALZFDoxbLMycdtppBviT1at9SjbgHjoQfXJyC7x06qJW7lkrrW3fVFa+Q7rqJID/P+98AMY0hxAkMKoEAgA4qgRrfF9bgCmSiRJKZpUB5Qalz1b8qiyvSN5MSlC+scDhqiALHLYwSCnR6qTL/Ij+YVcpnr7zIj40uRzGd5XPp6enu77bpNBCH20I4A8lOS3YSin1oXaWltbFezb9LtSDizISTcgHoIr+Rn7BeMbizOr48AvAKlueem+U66R1AvDx8QEgU9OxBWBB/W96Pt2vlU3fv92zMVr/hHr8omqLNL4zsaQEVGHyybcoR/WkwQKFRZwk+G7THX5XJwG7Xyi7fVdHdXk5i3/GA/twBXQOgSWMGSwwEtE/aLOAJbbeVh5F9edk6xptbQPaAjy90H9qxCEgRx3ZAuzbWD+s1VCv8t/K6fRsgadPxl8g7Zu2TlT7po3TJ9v60rAyij5va5srKo/wXpBAkECQwCgS6D/KjZJreLcSCdgWgACAVQeUG6yhNLhXXV6e/FFGUUqw+LP9sqFsA9akWY0IbLIV2TxlupS2LbxIqWOCRdT/dco6zXebLEcBJh71qEcNJMemWRPFgS849tBn+uuinXoF8MPijxMVFeQHskqLH/sAkJNXrYqmDo6wBfjIxFX0p13v+tX/RgdnZqPjjx3c7tPeHeUefTeTTnyv4aheE098uQK4EpE3oAoTT4DBuoO+b7vd1U1DE+WJb8oeN97hWTqDzTu/sYQiYskqf2qAJgDY8qcGoEJ7ZSFp2FhCWa4GWwa2HFyltwhd2gJ8aKH/2wCAK470o9LD+qf29wm8sfWdyDwAdw2Af4yDdvsGAFS7bhsg6m/tBcqDBIIEggQGSyAAgIPl49TTJgBABOASAIgighLCwR4oIQoo4QB/+OPpp5xKWXOJH9Gf9ypepJTnfd+V9OIDeuClTgWSLZzaMi55oMjSjpiwyXJUz/pd7fZmT5T7pXftvs/027RXIVfqE0s0gD97oQH/j1j8DepvBtFj0z2ozfDM3gL8mONiUK74DuBo2BZgyvvVnr3R/tjKsG4AUPKiT5Cjerag0d+zHR/5276pSINVIFuJbXkqn3AtTwJ2Gx1HWYt/e7yypcu4xZhBxF8sbRYAm4VaFgyImzZtMu0aAJsFg3552fm69Fu6hm90Z5bhQqxPdn5rkh8YYCnNISArY12FUKe+Ygps6A+6kO0vECtX2jh9Mq53FPhNuw8nu0si4RokECQQJOCmBAIA6Ga9pFKFsoHpPcAXVktVByk3UvyqLm9Q/vAM6IfSAQhIYCIiv2xZttm4xM8gXrM8kxLuQt1kobdfGvHBc02y+qUt4z5lpG0Zz+q7LY0Gmwcf68Oe0NdRB2kyLONembRTj1j0YHVm97WATQB/WPXUEXYdPBTtiU8BVlgRjwGjhGEA4A8274xmY97ZZvyHoxRU0rsA8sgbv4qAf0wuNQZgkULksBBAFWKVfnFhSW3M/mZKYtXpbMQ3RI4b7/Csfj0L77RHfKmtWbPGAH+0Wdoplqz0KUTaNRaBtFmsWn0IkoE93vlAd2Ya53fGnhUWgb0DC0uHfiXfn53f220PrZVFkmnrf3QltW8AP/pjFlJpHyzY/OxnP4s42R3dHEB8lAUa+h1ilu/OIjH8DBLILIHgAzCzqELClkkgAICeVShAF2BYHVuApdw0aTEHnygXKNCahADkoViwPTPPSZHiR4qsZ1XfQ67Ni88Kkq3YVVkvyAhLLgAde8Wa7SuAC2xzsWnpEfaQf+z31EaHvOLUY5/pL5t2+jqsG+hz7JOfsTajndTtg862/qPRzEcD9qZlaFXDAMD/+sVWk8uUBTpmyLbyJNSz7ZsK9w9YBbLdknqyT2TFUiVYoJRbJXa/Zn9z5Zbibm7iX+NuFkqRE5bCRLYI02YBA9neDlCibe30KbIeZIHX1aDxOY8MXOUlja6JzuIBIDzbN7c/LYm5xxZg6cRtlUVf5q0HtG/AayJzEsZNFmD4jXwABom0acBu2jhtvWj/UfQ9i+TwM0ggSCBIIEjgiAQCAOhZU2AbMJOeOgBAWcxJ2alLVCjbONpnEg6vCqyaA/qxap51e6be5SplrW5+bBrK+i1eyA95+aoc2XxoglGWjMiHPAGPaUu2JRdbNwF0im7htGm0Za+Jov3c9d8+02/TPoqc5VqAdsLknEDenOZLOynbssyme1CbsQ8AgaZD87NcCodhAOC9D/zO5M0WYFcD4xITSiKnsDLJBAy0T2TFAkUWVgAwtrxH4Ut1VVZ+o9BS57viu84yXSpL/Betd8Y5+hKiwBLAQBajiJs3bzanX7M1GP0mzYdx0/LQ+GyP2U3TVGb5E7EFICHWpqK9sZVfvxDbuUXzRywEpSP3Szsu99U2WEjFhytWrvTL8oeJCw0iBgwCu/Ms3hf97sZF/oHPIIEggSCBvBIIAGBeiTWcXn4AbTCjKpKk3NQFmKFEsDqetNKCZ/yyYdUxivIpfqSsVCW3OvK15QA/9v91lF9WGTbdZdZLGqADzSioADplbruqioeyZDwsH1u51kR32DsuPi9CO9vyAI9wLyDXAtQn4BF9Tr+Tn+vif1vscN0Ouw8fsv/N/Xt6br7vO8jvwSOHS03FVhw+BOpn7dq1ZksaW/upSyafjFn8JgLeYhXIxDPPpNMH/uui0f627P6urvKbLkdjk91XFqUJi6jV8bZ2ora1Yz3FwoN98jVgIWAglq9llFuUXr0nGbS1/mUBuDBxQrwRuH8/iTxmF/B7d4y3epfqtKyr2gY6tg5Qo+1inS1/gQDd+AzcsmWLiWwNZpylnRdZ0C+L9pBPkECQQJDAOEogAICe1bp83dVhAShFT4N7VaJispa27Q7LDcAaDvgoQwGui5+q5GTnK164V3X92OWW/duuV3uSWbQcJlGa+AvQoQyUzCosuaCzbB6K8l70PZ/pL0o77QRrP6xwtMDB5IVJC1bGrmzFsy0Aj47b8cOxT8BRwiALwLt2xrI4ssV4/2F3LQDT+Kcd6ERWvnusfukHsCRnsUyTThYARrGwUh9lt7s0etp2T3y3ja+s/Ih/e9zN+u6gdNpCyenXyZOvWZQgsgAKeE27bRLAlp5RtgwGyafWZ0csADsxABhFvQsvSTo6E2wRfuzYHAKS5D/5v3QtLbLrOe0VX5hEAEBtC8YKFutAYtJfYL/2NW59rmQYrhVLYGEi3kU1UXEhJWfvG70lsx+yK0cCAQAsR4615SILwDoAQA3mmiCXzWQaWEMZbH/B+qZMKy3yrZofyqgr2EqSFPO6yi6znLL4YPsfgA5AsuRRF6BjK6aaKJYpo6rz8pl+m/YscsIigXbCRETtBOsDQD+sxFasWJElm5HT2HT3azOd2CJv++493bJOPn5V9NuZ/r6pugkH/JiNF1v6hZt+tqX7iENAfA3Up06sxOIEkJf6ZtJpW1gJVCl7nPFVboPottuo3WcPeqdNz9RX2N9tmfwhU/QeInqRrKY4PRhdb+vWrSZiNQUQyG4I6TNl0jEoL8mgrfWvLcBz0aNjMQwGAOeiRQCwrbIY1A7SnqltDGqT+P8D6F63bp0B/uiTZa1NeyfqcBz65tAvp0k63AsSCBIIEihHAgEALEeOteUiANDnLcDQziQcKw0pDihSDPpMxKvadidlTWXWVmkVFCReyNpnfuwJlT3JzCoyJvhsGcfBut4HxJGvyDoAHbsuRENW+l1L5zP9g2inz6Gd2IcJMdlgoYF+x8UtSL+b2h8djLcoK5y48riRAcDpAQDg3b/6rYqKTwH2Ywtwl+A+P7CYZ9KJXyqsTbAKpK9g67cOYWCiCajCdjTaxKCgNmb3W4PSt+WZ+G4LP3n5EP92X583j6zpaYNYqxOTALaspgBatEW4DD+2WWirUwZZ6Ck1TefB2PPfQZPlTLRyaNadiQMmzSDAa2gmLUogI4Es8qDvxNclEctB+mPAwOThOLJ8BezWvKdFIgusBAkECQQJNCqBAAA2Kv78hde5BViDeVkAE6vZAH9YYSjUCdZIeUeRhSf9L1p8utq0l1U/TfBflA+29jGBZ0KkwHYTAB0m8mq7elbl1QYDfKwLm35N8qqUV5l5D6OdCTTOx5lkKDTVTlR+1qu9/Zd3jlsx+nA9aAvw/+7b1yXNty3AXcL7/KCfYfsvEQsrJpw6hAE/bMRNmzYZyyrAQCandtvqk+3Y3Lb7hXGUi/r1unkXgI3VFACJbTVF+yWyYEqbZSGj7MOK7AYuGdhjtv3c598T80snAE8vHD2Ulc5RB2LAcOlguaEvtDxBHgDQFgULb7Rbog7HoY3TH9uWr09/+tONdaz9bvgdJFCGBDrxl0z0KfhGr0+yHSdaR59RjJO0HOBVK2F1bAGWoqfBvQj7TBxQXAH+AAAVUFSx0mIVuy6wxi4HZVb8iSafrjbtUsx9ot+mFV7gYRgftCUAP4A/uy0xSQL4a/LkRCaG0GdPlG0eXf5tT2p9o9+m3ZYx7QPgzwaI6XOwqhn1MCG7nKK/bbr7yXzb7kd6sp84anQldXp+ridP/fOL3zwYzR3x/8c9l08BFs1Fr1hYrV692rQF+xAGrAKxECXiAxJQhagxl/L61VVRWnx5z+bbbru+0D8qneLfHndHzTPP+8hcALbaKUAJB9/g/mJyctJErAFps1UcrKDxuSkZ5JFX3rTa/st7h+JxfFgAAAQmtHXKYe+0+bnmCKPIwz4cx/YXSN5lnuTe5noIvAUJBAkECWSVQAAAs0rKkXSajNQBAGow1+CeRwS8w0QKJ9b2dmVOtAOsacLCwlZcpczm4cmltPYkrC28aJKVlDP84SsGENlu92UfEpMsN8//tC3afD8e8uRVd1q7LflIv+RFO9kdn5oLQMzEWAGAGMCHCbTNq567ep3c1euHaqYPeJeH/tn5TmryG+/b3HO/LVuAe5hK/ENbYDwisk0YK1Esqliwwhpl+/btJtqgirLwqR2J5lGudr8wbrwjN/HvAu/aNcECKroVbRYwECCQfo+ogxUAA/EbWAbd0jNsPWqUNuXUu50lC8ADA9wkiOaFoxcPY2qlLMRkjqvmCJoz5Hg1Nan8BZ5++ulG5ysr39TCws0ggSCBIIExlEAAAD2rdAGANqhWFQsadDW4ZymH1WmdwspvBSbfAH9MtpoK4ofy8/DUFL2DykWhR/lEKZdiPii9y88EniX5oI6Y2AD8MSFXADzGkqvJtiRadNUEK8mDnrt8hXYik1xNdF2m16ZNcucek14bIKZ90E7KmgDb5dbxO2kBuG926RsoWn6/LcB3/vI3PVm22QKwh9Ej/zA24DqAyEEx2l7JOCtQZePGjcExfSwv+5tLk2Ub76lfd413rJrxbwlQwuIH4yULr4ydOlgBlwfaZikXMkXqSDJoI+hlWwDu7xweLp4jAKCtUw5/qZ0p0BmkT5ctD763Kre1t7NGAldBAkECQQLDJRAAwOEyciqFFDh7olsVgVL0pPgNKofVZ6z9UECVnveZULFS7cIgLn7gQzQO4sn1Z/ADH77zonoRH2kgMoogWzcBdASCu1Q/mhj6BqAlZegT/dBq+/ZTn4jF1urY4s/lbUNqL8g/TeZz8Xe9wzoBmHS/j8GoUUO/LcC/2rPknoEy9h0eHWwcldam3gcwWbt2bXTaaacZ8A8wUKCKLEsBCbdt22a2W5K+7cFuo3bbbTvf4k9jk8Yq3XflSp2w0EHcsGGDsZhHF8MFAm1V1qwsimAViF6W94As12UwSl1MWBaA++aHn7QeLACXpK12wZ2yAUDyVH+jK/dCCBIoSwKdhdgHYBx9Cr7R65Nsx4nWAAB6VtsCPzTZrZJ8DeZa3UsrCx9KWGjZE3EUS5TMU089deipiml5VnXPVt5tpaWq8qrOV/z4zosUO5zzb9261YDIanPwSFsCRHZ5oi0e7Ily1fVfZv7QD+0+0E97B5Bhqy8LDwoAfljCcKqr7+HXe/dFszGfCo+JfdLtmctgmaIX+lzTLAB3/v6RaGZhqSxeHTcLwDRx8U2cdNJJJgKq0OYAUrAK5DuhryJikcxY16QP0jT6y7xn9wvq68rM3/W8xL/GXJfp5WAFxkwi/SNAICA27RbfqESsWVlQwzKQ9puFL+kZWdK6LJ9ltC3EY0jnd+b2QnwYwL75PcuSLLsRtgB3RSJdjRuaM3Qfhh9BAkECQQJBAk5KIACATlZLf6IEADa5BRhlmJVlgD9OY1UAoGniFFaVP+xqK6620jLsPVefix8p5q7SmZUuJtMKTGIA/ZhY57VUUB51Xn2vC03qNdGtU3ZZy+KbZTKb3BKu99sC/sHPtl1LPgz5/+TjV0Z79lcDAH7nno0U0RMCANgjjoj+iL6I8POf/9z0SXzzuCbAZyCRfgrLKtK1AYS2JWD3C+or7Odt/y3+feOdE4JlzYquRv/J1uC5uTkDaANqcygOQCCAIb7X+gXpGRrr+qXz7v78L2PYb/Hgj4WJx8YngqYflGTzJQvAAHj1utMJ8rBbSfgdJBAkECTgrgQCAOhu3aRSpi3AdQKAKH4owMS0wxiY7AD8ue5kH+Ud5RV+pMymCtmTm1LEfeUFSwSsuNiipMBJcLQlJiQ+KZOaGGqiKH58ubpMP5NVLFgA/uRXFHrlXuAnP/mJEbNPspe8+7WP5AEgx8eT9DLCzNz8smz+Z+cDy+4dnJmN2IZ8TNxf9gs+ybsfD0Xv00895znPMcAfbZNxkbZJGyUCpACo0I8BsPge7Loe1nZ95zWNfo2xvvIO3bhGIK5fv97s2AAM/P3vfx9heb8zPjGdiC5Hm01rt5KB9I40Ofl4z97+25nAR/Wu4Wwcs+iOwScdZThTxVLYi+lBHsVkGN4KEggSCBKoWwIBAKxb4iOWJwvA/fuH+ykZsaiebSEoh0x0UBYV8DcDWOOyry3RqmsAACWJZq5MJHVSq209CjVsSWKrnY8TDE0M7YlyMxIuVqqL9AOo4FeUQ4UAAQm0DSan9DvJLeG+yj6N7uQBICuO6Q/E5anxtC3AO3b3WhsqvwPTM9EJK9vv3078ZrnadcU3w6IXkXERyyra6tTUlImbNm2KNm/ebLYGYxXIVkt9Z1nKcimN+PaV/lFlKf59HJuSvAPSsHhCZPGNdotuh06JSxci7ZZ2TV+rre2tBQDnd3ZFNBs9Ov6dAQA8enHRcly/h67A4h9VAoD67igvyNqWevhdlgQWYv9/RJ+Cb/T6JNtxojUAgJ7VtgDAOiwANelGRDt27DCSYhB+whOeYCbgosUnEaL8wpettPhEv02rJiNSzO1nrv2GRqwNsPizwWssDqgP2jPWreLJNfqH0SO6faiLNF6kXNsKd1q6Ou6xrRIrKixU9J3y3QKisC08aVEF7dDtAu1lyWcysQV4LuGjr2g5SQDwd3v3R4fjrdXxHrhlYSoHAKj2syyTlt3o18ZokxxQRARAAQgEWAHExjqQiNUggArt2LexU3yPSz0nm6369bbxzyLKmjVrotXxoUm0W/pcIu0Wv85EbW3X4q/GuqSMfP1/Yn5Rt4X+mSjjgsdEJ5o4ZiaAUrHMNEbzbbStbfjapgPdQQJBAkECwyQQAMBhEnLsubYAs3LLwFuFyT0ADRNwJi0KmoAzeWEi42uQgiKF3lc+oNsHXmijOrDB3uprW4/ee++9BgD0uU40MdRE2bd25QL9OKwX8Cc5MvmUL0j8sI1DOBwD4hwCYoeDc7P2v4V/TwP2WcH4/0sB/0gyNcYnAVsiSv2p7yXtIYsaZ511VnerJWAgPgIBtllII2I1z1jKYpoP7Vrf4yC+02TRlnviX2NuW/gSH9QrJwQTzzzzTLNYh1Ugi3ba2q60nITNWJ60wNZz364TnZ1dkqcXju7+HvbjqEct7YYZlrbNzwUAVjEXabPcAm9BAkECQQJNSmA8ZlRNSrjksm3LAU4CZrJRRkDBRbFjAs4WzWQ499xzSysrmXed/0uB9xlskrxc5oVJAxMItnDyW4FtvmzftJ2Na1KpSZbS+nT1nYcm6acfwzKUBQe1gTy+IKGd9/SuD+1G8obWJN07H9kTdWJ+7LDr8NJpx/b9vL+TFoA/3PbLvlmEg0D6iiaT5Q/9MwAfEcCE/pCItTPuD4icxspz/AXin81uF/1Lr/+J2qir9FUtEekL48A/7ZZxmqit7bRbLAQJgNm33HKL2dKORSvpvAV/6GetLcCHervdgc1q4phy+uSBhXjwsGoAcBy+OQ+qOZAYJBAk0DIJBADQswq1AUAmEqMCgCi2bPMA+LO3ZmJpiOUNE5Q2BSmqUlp85s1FABArF0A/JgySMXTibwjgj1MJk8FFPpI0DvtfSqomisPSu/Zc9GuiXwd9+ErDtyhWJgpYlbCNkvaidqFn/a6ivd9z3+4nt/+uiL+fXQfjyWYfS708/CUBwG0Pp/v/I89gAbhcskW/D9q1fRorVoFYRtNHChhctWqVAQIBA12zrhLfbfvWltdw+h3xn7VPSs/Fv7v21vZbb701wkobi1Xcduj0a/4XiO2TP2hTGwsPxt3qEpB3cH74CcCqxYkV0/o51lfpedKtqxLGuPY9Vckz5LsogU4M+nc88wEIzSEECYwqgQAAjirBmt+3AUAsZ4oGFDh8FAH8AdoonHTSSQaokTUCjsxRfjXIK52vVynwvgI1ttxd4gUwmrZEm9JkiYkBk9k0v21pfOg9+5kvv1UXvvIg5boO+rF8AvizLY3p1wD+5HC+SL3XQXsRuvK+kzwA5JRHr4oeODyVN5vU9DYA+Mj+g9EBthb3ARaDBeByEY7axvjOdBorBx4BAgIAYn1PH7p161YTOTCEvpPvoeqJ9XIul98R3+onlqdo951x55/aVd2zRRiAmnbL4jG6JIA2ERAbq0Dabtpin2utZGJ+ew9J+ztLYGDPg7R/ggWgkYrmBlX2U2p7adUQ7gUJBAkECQQJ5JdAAADzy6zRN+QDECKKAICAfShqKG8obgQGVyYaya2ZPGNQJ50Gee75HATUtIEf8dIkmMm2IIA/JgIKWA3QlpgIZPFvJeWuST5Ee9GreNBEsWg+Tb1XNf3IBcAP4G/v3r1dNtkKvnr16pFOSK2a9i6xJf4QzWSZbDPbEgeAPDaebJcFALK1eC62+j4mtir8j/s29wX/oItDQEJIl4Bdf+kpht+lb8QPIJGxXJaAjNGyrtIBDAAqWPuXUe5wypanUBttqvzlFNV7R2PTuPKPtCUDdEKdfo17D4HYLOwAYk9OTprIYjLtli3CWfSAemt0sbQJa/svd/bN789MxsQxiycBZ36hpQmlS1cJALZUdIGtIIEggSCBxiQQAMDGRF+sYFZVUUJRyFG2sgYmGAA1KGtS5hmwAWmw0Oq35cgFkCkrj1nSSUmRMpvlHVfTNFU3tB8sVvDbxlWBtokVF9uBRJueDboqrc91Ih70bQ3i18VnmtiWTT/5AWYA/LHlV4GtYgB/sjTW/SJX0V7kXRffmdy99E1B37Ersjumz8LPdLygc0wM0v9g686BycMW4IHiKfUhFrDr1q2LzjjjDPO9AAbiE1MHMDB2A5YDqDBmJ0/CLpWYlMzUL7TtW0thNfWW+Fc/n5qo5TfTZKBDmtAhBWJzijA+L1nwId5///0GBKTtAgo61YY6O7q1thAdFQOAS4tT3Qd9fiwcnV3/7pNFK24HALAV1Ti2TCzE23+JPgXf6PVJtuNEawAAPattlCe2WaBsEQcFFDZWZZk87Nq1q5uUyQMKGxMJFLhBQYCZBvlBaX14JgXeZ7BJcq6bF9oT/toA/mwwh4kpwB9WAUWUe/GhCYb48+kqvn1tV6K/rDpADliF0lbsforTn2krAIBlh7JoL5uuPPntn5mJfjeV3QolT95KOzvfMT+3/H5pTNAz+xq2ANvSWPytNqbvZXmK0e6Qr6yrkgcw0OfikmPz5s3GYh9Aha3C6j9HK3nw21XzPbj05p+qX6+q3pvncDgFkkG/9maD2AB/ArHRHQEFiSw0o3fSdm13NsNLryaFbQG4MHFC1Imy+wBcODrHduFqyHciV80NNFcoiyj6HCLf3Dh/d2XJM+QTJBAkECRgSyAAgLY0PPnNNmAm1f0sABk0AWoA/nRyG6yhcLE1ky0Z/ZS4pAg0qGuQTz737X/x3QZ+xIsU86rqgvyxHKU92W0O6y3AnFGtuKTcVc1HVfIhX/GgiXKVZVWRd1n0U4f4gQT4wwpEAVBjdWzxZ5/+rGejXsuifVQ68rwvmnnHbjPJ7b88n+7Mcykt4AfwwOGZaF8MNvbz/0dhAQAsTeSFMrIPYGAcB1ABRMEqEOtAIqdlC1Cx3YMUKnDAS2qjdrsdkLx1j8S/xtzWMZiBIY3Pw2RAG2Ghh4j7GNopbRdQkDFh+/btJrIIBBDIjoFhC9EZyCuUZGJ+yQKwEwOAUTR4UaSnkAAAGnFIlx7WLnpkF/4JEggSCBIIEmhUAgEAbFT8xQrXyql9ai85MRDrYA978o2iBfCHQpZXgRcAKOWvGMXuvNUmfqRwVVU3KO9MOAH+sEZRkL/IUU+gVn5V86Fyqrzqu9JEscqyqsh7VPrpe5jk2W2FPFlsACRWn1UF7W3KM3kACLztmVkCUsvgdTo+6fKWjfHEd8iul7AFeLm09X3re1meopo79LVEDmDAspZvjUU+/AXu2LHDRAEqnKBdts+1pviuRpr5c9UYW3e956e0ujckA43XWUqiHQLyEVk8lCUgv9mdQty4cWPXohUdNU/+WWjom2Yh3sK7sOS7eDZ6dJw0OwDYOWrwDpy+5bbsgQDAsvsciWmcvznJIFyDBIIEggTKlkAAAMuWaA35aTItayyUKvysYA2AdYBCGUCNlDEN8srb16v4kTLrKx/QXRUvgH2//vWvzSTTPiiGiSVAMlvQywxS8DTJLDPvuvKqqi7qor9oHdDf6ARI9T3IQm2ljpMgi9Jel2zzlJNmAfjQEFcPefInLRaAN2/uPf0yLY9gAZgmlWbv8W1hMUVkkY+xn+/PBlTYJgzwDuhSls819c361pqVQv2li3/18/VT0GyJ8D+qDNAbTj/99Gjt2rUG+APEZmcBOgZXoixasWqtwlq8R4rxASCx96/urZnouO7vLD8Wjgo+AJGT5gZaXM8iu5AmSMAVCeBPrxN8ALpSHYGOGiUQAMAahV1WUdrqg8L05je/ObrxxhuNYvWRj3zEgEIoT/j4K2PyrUFdg3xZPDSVjxT4NvAjXsoCMw8dOmQsuLAiVZ7UPxNJ2hPKeRWhbD6qoHFYnpoYa5I0LL1rz/PSL5AY8EHfUh1tJU1ueWlPy6OJe9BtT6yhIXkAyIkrj4t2zZfrawoAcOODDw9leSq2LguhVwL6vtXmep/W+x/+1E477bRozZo1XUCFvpvvUZZW6ACywBpFH3CJ73qlvFiaxkMX6r1J/ilb43VROpAhbkOIGzZsMFuEaa8cFmVbtGLxSttlMamKQ28mOkvbf+FleiHfYUvzE8ECELlp/B+1XZBXvzCu310/eYT7QQJBAkECo0ogAICjSrCB91kxZSX1Yx/7WHdVltVUFKanPOUppfpTaRsAKH6k0DdQfaUVKYVrVF7YSo7PNraWaaKn0/1QwKv2z1MWH6UJtkBGUlAlvwJZNPpKVvqxOmKbLxM2tTu2/px66qkGJK66rTQqpBoK37Zrd08pj1u1Mto1VS4AyEEjj+CfcdgW4Omlbf89RIV/nJKADaisX7/eACoA85zQzqLO5OSkiWyv5DtlZ4DGwayMqF9TP5H1vbakE/8aq9rCV1Y+1NeTvkwZ0A5ZsCbKopWxBZ0Ev5dELFrxIYsuwrWs8u0DQODr0JIxIP8ODWEL8KKIBABWtQV4aEWEBEECQQJBAkECuSUQAMDcImvmBRSw7373u9HHP/7x6M477+wSgUL/1re+1URZBnYflvBDypYG+RKybDQL8WMrtI0SNELho/DChGbv3r0G+LNPiMaqhG2+rLrnnSQWZUWTSk2yiubT5Huj1EWTdKvsYXXAFkNAYqyOVU+AfbQVJmZNKv/DaBePrl93HzwU7T7U6+9v1aMGn9JehKcfTf5qKPhHvvvjg0JC6JWA2r7aXO/T5v/jO+R7JPLNsjBIBFyhnyeShv4d3YFFwyy8uM531ZIfd/5tfUljXdkyty1adegNFq24lmBxksiYI8Awa9vtR6d9AAhpDs4vuc/p9459vxMdisfC+fj7yWc5aOfRht+aG1TVLtogo8BDkECQQJCAaxIIAKBrNZKgB8X9q1/9anTttdealVA9RhF6xSteEX3uc5+rFKgRCGQrgKLBx6v4kdLiIw+iWQpXnrphIsMkEDAHAFAB8BgwB+sQ5atnVV9VXh4+qqYpb/6aRGuimPf9ptP3o1/WoZzkqMBWcA72qBMkVtlpV9Ge9szle9BNe1GbSTsA5JijjyqdhTt++ZtMeYYtwJnE5GwifK6dccYZZrcAfT5WgYAo7CDAxyuRfh8gEFBl0DZLtVFnma2QMPsb9bWvGVU89tis8XrUPPu9j4xPOOEEE7Fopc1iFcihN4CB6C5E2i5AN+MQ4GHeMNHZ2fPKVCenpfXEQjTTmYqOPfqxPfmM2z/SpaVbjxv/gV+/JYDhb6yGeRU8I9cr2Y4TsQEAdLS2d+/eHf3TP/1T9A//8A9mSw9kohgB+qGM/cd//IdZDa160FX+GuQdFVdmsqS82gpt5pcdS5iHF/gFxGH75gHrUAFOjgTMKctZfBER5eGjSP51vKOJoa8TZdEvWWGBsXPnTuOXSffwIUZb4QAC1ZmeuXD1VfaSXdoBILPxd1t2+OXuPZmynJ3vRNMxWHRsbDEWwqIE1MaS34vL8oHWk08+2UQAFKyqAAP5xgH42WK5efPm7kmspE1+3+I7ed9lvsuiTbyT3zjyD9+2vlSnDChLh97gH5C2i0Xr1NSUabu0WyJtFjAw8/Z2Zvzzv4S1btg3t7/7O+uP2c6+AADGPkcJmitkld2wdPZ351N/O4yv8DxIIEggSMAFCXil2bPq96lPfSq66aabzOo1liicKPba1742euc731nq6aTf//73jeXd7bffbpQOts1wst7ZZ58d/fEf/3H0hje8waxAVlWJV199tQH/yB8+3/SmN0V/9md/Fq1bt85cua9TgPldVdCgHgDAqiRcPF8p4rZynsyNemP1HOAPBVoBhRkwh5X2poOUO1vha5qmvOWLh0F1kTfPOtOLfnyG3XPPPcZ/mMrH0oK2wuRK6fTMhato8rn9IMfkASDcOzBX/jbcfXE/MDHMASCFx4FtwMc+2is1YZHw8DdVAtq2j7U3IApAIOMDwCALRET0DSwCAVT49gm+f1upwsh40+7T1ddkfLU1yWwZSO+omzna5erVq02k7QIE0nY5kIoDRIja3k77ZXGzb311fhv3gEvuFhaio6J9MZiXN8x0lnZR5H23Lek1N9BcoWy++tZh2QWF/IIEggSCBMZIAt5o9px0e+mll5pVa9UPANhdd91l4uc//3kDDHI4xigBx9mcrPvv//7vy7JhxXzr1q3Rt7/97ei8886LzjnnnGVpyrrxnve8J7rhhhuit7/97dG73vUuM/lW3scff7z5aVty6VnZVyl7tgJYdhl15iclRUpLnWWXXdagumFCx+SOyG8CihQgNmCO2lDZNBXJbxAfRfJr4h3x4ONEGZqZRBHYZqWAjyUmXBwe4LIS7jJtkmXaVXSrzSQPAOGdh2NAtuywEO8qnlg02hiaNduAH/foVUPTjUsC1ZXqzme+/+AP/sCcwnrmmWea756xInkSKwtEbBFm2zChDXznrTPVOe+pn8+bh+/pbf3PBRnQdtkezII4bRYgEPDa3t7OFnhAbMDA5AnYyROAFyZOiBaixTaep65mCoCGefL3Ia10aenWPtAcaAwSCBIIEhh3CXgBAGKRcskll5gT7ViRvuqqq6ILL7zQ/A9Ihh+8LVu2RC95yUsMGIhyUCTgE+2FL3xhdPfdd5vXX/WqV0WvfvWrjZUhgxs+c2699VYDABbJP887AJmscLJinwxalQ8WgEnJDP9fyqut0A5/y80UNi9MUpic4TOSdkrbEY+kQxF+4hOfWMhXTtXc23xUXVZV+WtibE8WqyqrrHyhFcBvZ7zVl62ACieeeKIBibmKLz1z+eqT7JNyhPakD8DjYqvzPRlO603mVeb/U+Ek4DLF6WRe9L8sDBF1EivjBwuM6ERE9QMALLRV/e8kQyUTpXGUbMeJb1uMrsqAtotlOpFFLA6pou3SZtGPWbAnsogFEEgbx0oweQLw/AR+/B62Wc70my3A4xzoC6oEAPW96TrOsg68VyOBTmwLTPQp+EavT7IdJ1q9AAAvv/xyA/YxcH/ve98z1neqpIsuusg4ur7yyisNCMhhGVdffbUe57q++93vNuAfWw3+9V//NXr5y1/e8/7Tnva0CFDw7//+77uDXk+Ckv9JA/8oQtZbdVgAalVPg3zJLNaencAmFBeUWv1fOyElFGjTDoAD8McqOLwR+F4A/bDg6NeWSiBj5Cyk3InukTNsIAPVhQ880O5pJ7hUSC4isDUcNwc+hTa0nwf3H4j2z/SeQnnK8auiXx4qf4tZHgfSU4eX3Ab41CaqolXft9pcVeU0la99EuuePXsMmILfNY3/7IL4wQ9+YBaUWFRKWlY1RXeV5arOKUP9fJXluZi3AEDavattn0Ns2NpORB/CKpBon4B9//33G5+C6//PxmiVNeefi9jqnh8AHPctwPa3obmCi+030BQkECQQJBAk0CsB5wHAO+64I7rtttsM1W95y1t6wD+xcsUVV0TXX399tHHjxui6666LPvjBD+YGPPD195WvfMVk+dd//dfLwD+VxRUFCHClqRAAwOKSt5WUNgGAd955Z1coANgowax42/x2Ezj2Q5MqTTIcIy8TOZoUucwDk3gm8/iDZFKkgPUEdLOVymWgWPS25ao2wyQqaf0Hj4857lFRVP4O4HgAyy7B/cECMLuwWpSStokFMJGtlvfee6/pH2ARP6GTk5MmYlkFEIhllQ9jTZEqsvt0fbNF8vH5HclAY7XrvLBLhu3BnILNCdgAgVgHMgZiIfjEP/hFtMpyfzyzEPe1BcK4A4ByDYDo2vr9F2gW4ZUggSCBIAHnJdAcipVRNN/5zne6KfHNlxZQSt74xjearcGsWt98883RxRdfnJa0771Pf/rT5hk+b/C553KocwuwFD4pgC7LJQtt4oe0vvIEYMAp0du3b+9hGWAY4I/JmM1nTyIH/9Gkyl5NdpDMgSS5zANKOpMeLETl6w96OV0Rf5D4SuIkRYKPdeCy7Ac2Guvh5K5HrP8Wfx57zNHL7tV9IwCAvRLX96E21/u0nf+x2IlbFRYIAPw4XIH+xLasIs0f/dEfGTAQHapN8lGdU7s+jatltkbpSr7xTzvEqp141llndbcIP3rlQz3i+f2+g3Hl9tzK9M+4bwFWu0BYAQDM1GRCIscksLAwEeu9OVZFHaDfN3odEFkgIUUCzgOAWOYRADee+tSnprCweOv888/vPvvhD3+YCwBkUqxDP/AByDYYAquFrBxyZbKs+92CGvrRlAUgirDvir2tpFCvPgWULXy2sXXT9tkGD1hpMAHzsX40qaB9+drGJHd7sth029JBMAB/WqlH1liGAhTb/ZmL9GeVn2jPmt7FdNtSAMBOVYTm0HXDFuCqKsGvfDXRB+jDqgofxVhWAQQmD19ggVKHL2CN7nsQ7/DRhr6mSH1IBhqri+TR9Du0XdyhnPp/ToxW7On13Tc1Gx8AUqCpjvshINIrqFtbty6jrm1daly/uzLkGPIIEggSCBJIk4DzACDbegkonAzg/QIAiILe0f/Drvfdd193S9yTn/xkc9Lwhz70oehLX/pShEUhAf8iz3/+88324gsuuMDca+pPEwAgvKIElj3I1y1DW4GVUls3DXnLA6hk+wrAn711k+1ZnFpNwCrDVyUpWSc+tjHx4EKbmo5PbtVBMAK5kSmTcoA/+rJkUNuxle5kGtf/9412yRy5TsYWvclweD7/qZTJPEb935VDQA5Nz0Yrj11+INao/OV9X23Mrru8eficXnxzlWUVCw24FgAM5PAFFqewKOZgtlNOOcWALqRVH+kb/6pz6PaVh1FlrnGtDfwnDwBBNp1H9Z9bDJLduG8Bln6BjHzU2wbVbXgWJBAkECTQZgkUG/VqkghgB9tOCKzcDQqAIQBjHIzB5DdPwDGwAooOh31wcpgdsBL8/ve/H/33f/93dM0110Tvf//77ce1/hYAyOobdKVN6MsiyFb4kI3vg3ySn7LkVEU+TKy0dZPfCmzxBcjBATsO2QlS0JXGp6tdJ/ZkyyceNDFukn58c+Hfj8m42gOLJlkOgnGB/qL17TPt8Dw334l27F5caLJl8Mj0kp9G+36dv13YAjw7Nx/d/rMd0Quftq5O1kNZlgTUr+lbsx4Zv6GMR8SpqSkzZrFzAt3koYceMhEdBctj9Di5MLHzcPm3eIfGNP5dpr0s2jSe2GN1WXnXns/8jmVFzh0bm0XPLLs99EbYArxkp96KtjG0xkOCIIEggSCBdkjAaQAQZVIhi9IoADC5PVJ59LviT03hox/9qLGyetGLXhT95V/+pTkRk5Pvvv3tb0d//ud/bla4uWJx+IpXvEKv1Xq1ZQGvJ510UmXl24Afq32+HxJgKyn26mVlAiyQcZoFF3SzxVfAH9lKKU/+LlBko6/Ykyqbp0aJylm4eLAnizmzKJycRQ+sQ+0ToJlw01aYdA+ynE4W2gT9SRry/t+k7PPSmpb+d3H9TSfcEbBL96GDB9KSj37Psy3A/9/W30S/f6QiWeSUpo/fR04WU5OLb31rqYnim/gKRDfiAAbcVbCAxRUwcOfOnSbiIxBrZMYzH/QJe0waxn8/ufh+X/Vv60++8jTRWQ4A7ptbmmvk4WvcLQC1BZh5QhXfhvLUNU/dhLRBAlkk0In9/xF9Cr7R65Nsx4lWpwFAe7tjFis3+ZvBEiZPYAKtQJn4Afzud7/btXZjG8vb3/726ElPelKEr0EUwquuusqcFNzEwCQLQGg+ePBgrQCg5OTrlfpCiaUObcXeBX6oS1lwSeEGvGGyhBVX8huw255rvOSRpz2p8JUP8aB6y8N/0bQskAD8YWWjgF8/DvbAZ6kN3ut5v6vaUp3096Nl3O7v3Nvrjwr+Tz5+VfTg7NK4VKZMFnJk5oIF4O0/3xmduHLRL28O0itNqu+l0kIcylz9Qla+6Q+xVCeyoIVF4AMPPGB2aLBNmLhp0ybzHKtAFjGz5l23WGzeXaWxaploXNY4V3V5VeY/kbAAXIhP/5jqLO+Ds9Dw/7P3JnCSFVW+f9TWO73IzgxazSqgIjBsArLoMPP+CH/GkeEziijq6DDqoI+/zOio4/h/jjoOMKjvKSAKuAyoLEq3AgpN00130/QC3dD7StN7d1V17VtmvfhG1cmKzLyZedfMe6tudN/KzHvjRpxzbtyIE78450TvYJsJGTR9+nQ32cdcHmkXXnSNMSeElKFUAqkEUgkkUAKxBgDtIPWsIFdKKJokXCO9JLse7sMK0GlAu/jii9X73/9+9etf/1oRZ3D16tXGQtBLXWHkLQQAwyizVBm2HOJqMVeK9lLnUWJRXER5KZWvWuexMAX4s4EcNxZcTEbixosfmdmTCpls+SmnlvfIxLAabYrJM9Y0BOGXxE6+AH9+d4AW+pMo/6TSLnRvLQhIzzMF8IoKAFQeFrs7eiuPu9IGo/ikPS7WAOAVZ54URfGey5T3Q56d5wISekMQvlmYnT17tmpubjbAH1aBhCnAeohPDnQwFro46MvilKRPH2/P3H4GIgN7rLavJ+l7XXZbHrlDdTPVkBoNsZJ3scKPobo+9cLiBWr6tJnG2h6LezFEqHDrmLgscwJ7nhA2Y+P5vQtblml5qQRSCaQSEAnEGgDEnUSSG7deseSzXWTl/nKfdj1Y+5111lkls//FX/yFAQDJ8NJLL9UEALT5E55LEhzwgj34ihIYsMia346ywuRDlJdaEMSEig08sOCSjTygg8kPrptYcLlRtscCADgW2pjwIBPlsNuUtBeAP9mYiDroC5hY028JDX7qlnujot8PTW7vEdrd5o9bvu3txe5nkyfUfsML5NQ5sqhWK5mt3r5H7d7XodgEJE21k4D0C0HeNe5lsyoO3IQJWYBVICFY8LzYvHmzObAGxCqQxYwogQW30hTe3YzHbstMWj7R/RIvgyEdsy7zep74M3Uz9O/hWON5F9z+aOjWsS8bchvfsOENQPZRRx3lSodzW00c84kOHYf3NI7ySWlKJZBKIJVAXCUQawCQVeHDDz/cWLqgKJZLgCgChuEu6SXZ+SttNmLnJbZNLRLWYbiGAmIJz1HRgdLO4M5AL4N9VHVVq1xRYkWprVa91MNkgnYD8GfHuASExoLLK5BTS17CkpvwQHm1eCZh8CETY5kshlEmZVAeGyHRXrAUlUQcLYC/sFznoqJf6K3GZ9iyrwbN1LHtUDEA2FDvwUwvQkJrvQvwC9r6D0n0DdR+R2TEnNQ2FrSJCN/STwQtD50CaykOQl9gFcgBEAggyIGOw0IYOhn9XVh1e6VdxqRa1e+V3ijyiwzssTqKeiIvM7tL9yfDnkJS16Capr/6BwDfec6pqm1Pk7FkxVMJ/Y6D+Ja0X8DAWrZf4TOKT5kTpABgFNJNy6yGBLSKrcf1atQUXh1Jozc8ztOSwpRArAFAGD399NPVggUL1KZNmwzgVSqgPfFkJJ122mny1dXnGWeckcsnA1ruRMEX+3opWgpuieQnbsC4AkYNAEJ8CgAGf4Qo0Hv27DGuvkx4JLF7NcAfn34mGKKQi4Iu5Sbp0+ZbJppJoh9a5TlAP4fNkx9eKAMLGYA/+x0H8JP24qfcUvcIvUmUf1Jph+7B7JDa2dFZ9FgGspmic6Gd8IAt1hoAfHHtsLVO3CwApc2F9kxiXpD0C1HwjdX7ySefrE466SQD/LHYS9/HAiffOdB3AAJr4WIpvEsfH/NHFQl5ol8kXQZOG4D0D00MJLMJUzLqtNPeoU499VSzWAeQTTiXgYEBtWPHDnPQfgECab+FIYcCVV7jm2U+FDYAyDvHQX8TRZ9TY7Gl1acSSCWQSqDmEog9AEjcPQBAJsHLly9X559/vqPQ5s+fnzt/0UUX5b67+cKEGrdL4rDhYicDj9O9uKlIYkCvVcL1DwDQBpOiokWUPhnso6qnWuWKslINfpjEEACdtmXHscQ9hDYXNHi0PBtR0KslwzDrsRW8pPJh81Cu/6gkN/gHKAb4szczwjKUfipoeylVv9AP7UlLQnvS6Ibe/f0DKuMg8/aBfCuVWvHWpV2AmcjWYrfWrfta1MbtB4wFYG9/vCwAk9zm/LQl6Rei5Juy8fjgoM0RGxAwRRY6169frzZs2GCs5NG96BNl/PPDk9t7ZEyKkne3tNQqn8igGvKOkse6zLai4vyLJa4AAEAASURBVHuHGorOeTnRP7KBCLJBr+NA16P9ovtJ+6XtctC+ab9xcXH3wmthXtGhRacuvJ7+TiWQSiCVQCqBeEog9gDgtddeq775zW8a6f3kJz9xBABRTh588EGTh/gyl19+uWdp//Vf/7W68847jZvdM888o9773vc6lvHoo4/mzgNO1irJRiC2dVBUtMjgLkpgVPVUq1xRYqPkBwWQ1V8mMICAJCYQxx57rAH+wgp0Xg1eon4uyAU+eB5RPpMo+bAnhzJZ9lIfirQAxbKZEWUymQD4s+N+einXbV6h3w/tbuuIOl8Sad/T57zJxkGPO9l7kq0HC0BtoKiefOZZNftP/6TqrpjPrNqs6kYMIeNmAehJ3mMgs7xb0k9EzRKAMwtkHMR/xgqQ/pFxFesqDkKhYFEFmGLHcQ6bNuFdxtqwy09CeTIuJ14GBTsAI/uegA9AAEC7GNom4zYH7Rc9kPbL2M7mXRzo1eiDtGG/HiB2nbX4HjUAWK3+phayS+tMJZBKIJVALSUQewDwvPPOU5dccomxArzvvvvURz7yEXXhhRfmyez22283u/Jy8pZbbimyVnjuuedyoCD333///Xn38+Nzn/uc+sEPfmBi0PzP//k/1cKFC4usbX72s58pyiJdddVVyo4HaE5W8U8tAEAZ7KvIZiRViRIbBT9YbWHtx+qvKM0oekxSaC9h7xAnvEhdkQisCoWKoieTrSpUGWoV8hwolGchoHmlSsTNDbAYqxcSsmBiwOTB647mleordT3J8k8q7dDtBABObmxQ7QCDHoC6Us/V6bxXG88ubaUorpgA0eKK6VR2mOdWbHgjV1zcLABzhI2TL9Ivy7tWTbZpc2wacsoppxgXS9oiMdYAA/HY4CDGGmMs/WbY1qq15L2aci5Xl+gX9jhXLn9crxXuAAyd3ZlgGwz1Zw+VZZf2i3uwtF+AQFzc0T+lX2Wcp/0CBoa1OFyWqJAuig7tVt8Jqdq0mFQC4UlgqE57/UWkbIVHZX5JSaM3n/r0V0wkEHsAEDndddddCrdewJUrr7xSfelLXzKAHr8feughdc899xhxMsDeeuutvkTLSvPXv/51ddttt6nVq1crgMd/+qd/Mrv8Enwfyz8AQhJueFgL1jIJAFgNF2AZ3GWwryXfYdQt/IhSG0aZrPLitollgkwYmIgA+qHYhT0pEZpFIQ+TFym7mp/wQftKKh/2xFiefzn5MXkV5V8sRJEBbYW+KGyguBwtXBP63dBeqaz0unsJ7OkttgCcpttBl/siIs/5J82zVWNXu3Flo58j3i7umMSjjCq1d/eq17bszRXfq0HIOCV5X+JEU5S0SL9QS77pH8XFEksqgBQsq2iTuFly0DZxraQfxdUyDHplTAqjrCifUZRliwxE34iyrijLrnOwAOzIBrMBHMiUBwCFH9oPbuscLPYR6oM2zAaGzGWIc86BNSDtlw1EahlnXOgu9ylzAtGpy+X1e208v3d+ZZbel0oglUAqgUoSSAQAeNZZZ6mHH35Y3XDDDcZFFwCwMAH+zZ07N5AryBe+8AUThPrb3/62meB87GMfK6zGKKCPP/64CVpddLGKJ8QlEOU36iRKnyiBUdcXdflh8cOkiEkHwB8uHZII8gyIgzVClIoR9YXFi9Beq8+k82ErqTJZdpIlE1csRJm4yvuEko/CD1iM61AtktBfjvZa0OWmziTT7mwBWK9UlCHvPC52T531JnX2O84wu5YDWjNpZQJr93nExqX9hhXg/jfL16rBrkzOCLK3L0qBuGllw3nk/ZA25/7OZOeMG98skMyePVs1NzcbnZB2idU9iyl8ctAW6Vc5glhVST8tY1Syn6Q/6seEDIY6Vd1Q8W6/7YPBdOj+oXbPQpXFYfpMwvgImM0u2ACCHGvXrjVgNlaBYYHZngmtcEM1AMAKJKSXUwmkEkglkErAhwQSAQDC19VXX61WrVplrAEB+lD4mCyzc9x1112nPvOZzwRS8kR2xBu85pprjLUfm4+IIgnAyPnPfvazxt1E8tfqM7UA9C95UeRFefFaEpMhJr8AfwCAkgBlAf6wUpA65FpUn1KPKOhR1RN1uTKhlolm1PWFXb48B8p1ehas8NNe6E+ER5kE4FJZ65X+JMtfaA/7mUZZHgBal7YCPThQDGwdruPYbjuwJ8rqPZXdqUFrEnHWTjvtNOPOhgsbrpfS/wEAcmDdQnsOukHDik07c+AfdcfNAhCaxlOSPitu7xr04P7LgZsw7ZLFFcZnwBRpl1irilWV10W5uPJezfYnY1rcnr8XGThZ/w2pBtUxsomHl7LsvP0Z7wCgfT+6vL0LNmAg1oHop3znAMwGCOSQxX+7jFp9Fx3a6ztVK3rTelMJFEoA99+kuQAnjd5Cmae/4yGBxACAiIuYWHfccYc5vIjvsssuy0263dxHjMHCOINu7qtmnhQA9C9tUVZEqXVbEvmZYGDBZW++wsYztE0mGdVWkAV48sqLW56rlS/pfNjPXSaMyM7JNRzrFYBilHlpi9WSc6l6hH6b9lJ543o+CbTj+i0WoDu7uh1FmfVooedYSLmTHsvvLNiohHcV62asqhYvXmxqok1j3UpsNg4W58T6yuuENZPJqle35AOgPTGzACwn3rF4Td4t6SfiyCN9qYAkLLgABHLwvaWlxRxr1qwxbZe2ybjthh/hXcaoOPIeNU2iXyRaBpltRWIaqpuphlRxGIaijGVODFSIAVjm1rxLtEUs/ThYaBEwm7YLmL1lyxZzRBnvMo8gFz+iAgDlnYMEN++oC1LTLKkEUgmkEkglYEkgUQCgRfe4/yqTKhuIikooAlLIYB9VPdUqV5RYUWor1SsrsUzcmeRKOuKIIwzwh0JWq+SVl1rRWanepPNhK6kor8QNxeIPMEQSgb4BiontI/zKtVp/Cv224l1rmtzWnwTanVy/9/Q5x7XrCRiU3q3c3ObrcIhTWHjvu9/9bgOwYJlPHFSAzq1bt5qDmFZYBRKbzY2l6+9WbVBdrX1KO0LnUp+2lMzqLYnr6z2il7kSwv0ibS7cUuNbmvQLSeGbvhbvkBNPPNG0S4BAsaqijXKwiAoQCGhYznVd9ISk8B5FKxIZxG3c8sJrXXZrUfZMHbrb6BhdlMHFCaddgF3cVjaLhAWhfQJgi4swMb+xuuYg3iXeJrRfdNFaPBuZE8gcoSxT6cVUAqkEUgmkEoiNBFIAMDaPwhshYgFYDQBQFAtRAr1RGr/cwo8oL6UoxE1PJgt8JzEJYCILkCPPoNT91TgvvCT92cjkSiaa1ZBdmHXIc6BMFHNxjeQ37YT2Uk3XcOr1kpIs/zjTjuUGCwdM4OQdxToOC9DfLVnu+IhaeoMFpXcs1D6pMbQh/a8uz8nWzpD/XVyA88/m/+IZSIB7wE74pe9kfLJjWmE5CBjIRlry3PJLUmrh2m2qPlN4VilAwMkTm4ovVPFMUvunoCISvks9s6DlR3U/9NpWVYCAtEv6Z9rmhg0b1MaNGw2AUsp1XXi3+/io6I1rudJ3JVkGTi7Ag2qqFnn8AEC7HQBmA2SfcMIJqq2tzfSthBIh3iXtmYMxBSAQwJBQDdVKokNHBQAmrb+pltzTelIJpBJIJRBUAikAGFSCNbpfwKd0F2DvD0CUFVFqC0tg0r5jx468jRpQfFGuwgx0X1ivn9+ikJfixU+ZtbgnyXwwQcRNR5KAfyjizc3NZnIZd0VW6JPJrvCSfvqTAFYbAvyJTJmkAQQzUaMP2v3coqLCGzRgcaA7YgCwqNbyJzoKXIDL51ZmB2vZoIEJK30pk1QmrHzn4N0AcEEWhTukr92+z7GKXm0xWWsAUAiT90V+j/VPacNJ5lNirjKGE5oBq0CAatt1nTy0SdqmACkyto63Z24/a5GBjNP2taR8r3NwAe5XEwOTPxAwhqBbAmh/WFNzEO8SS2va8IEDB4zFNTFZOVhcoQ1zMOZEmaIGAKOkPS07lQASyOoYgBxJSkmjN0myHU+0pgBgQp926gLs/8GJEitKrZSERQBum8RekQkPEwImAxyFE1W5r5afpXipJU1+6pbJVeEz8VNWte6hjeDiS5vp6OjIVcu7ifsZirrwlbsY0y9Cp7T7mJLpSFacaGdBhvYA4CWyJD4ewF/hruC7enqL+Dly6hS1qz/YrpRFhQY80dE7GvbAS1E8F5mwEtMKqxWsr3CP531hl8v169cbi2r6V2Korty+W09sO5STYtLTP6hmeSEgzRuaBKQty3gTWsE1Kog++tRTTzWbLwCgAKQAqGDpz/vLAZBCuwS4Jo0V3v2IXMblxMpgSJsUZ18vYr13qKHonNcTWR1DcDDboxrrJ3u91Xd+FpAYTzhYsKZvpQ0DbNO/ctC3YpUNEBiV90GUAKCM6/LpW1jpjakEUgmkEkglUCQBJz27KFN6In4SEAvA1AXY+7MRC0BRXrDYQuFnIiApjhs1CG32pyjkoqDb15L0XfiQiWacaUfWgMS0GScLXHb0A/hIUhIlOwnyL5Sr0F54vpq/aQdYX9iLB8QVE+BP2rfQdEhP2g4NFO8APGPypOoAgCx4Dwk15T8LNwEpn9v5KosnuD1zMDkFCMT6CnCFySsHrm6PbW1RDcW4qCk0DjsBy/sRhzbnLOlozgrf0ZReu1J5LwFHOLAEFJBagBQ2DZFEXEvkMN6ePfyLflHYj4lsYv+Z3aUDHhRv9tHtEGrADy/9eiOQagKANo2MM2JxTd8KEEg7BswG1Oag/wUsxIulXPgFu1w330WHFp3azT1pnlQCqQRSCaQSqL0EUgCw9s/AFwXVBABlcJfB3hfBMbpJlFgUpBUrVpi4KkIecmWSSpw/ySfX4vgpNIqCHkca3dCUBD5o/1h2Afyx6i6JySNAz/Lly81EKYmTZZnUJpF2eQ61oJ0FGAH+hA6ArGbt+l2uD9nc0ibZ8z6nNMVvSHazCUgeExV+MAE9/fTTjQUWgClgIC70uE2v37G/ZGTCXm0BWOtUizZWa56pX/iWfiIONIVNA4t+vLf05QKkCEhNXcSynD9/vgFRAFLYBXu8JHn+Mk4njW+nDUDg4fVeYgAGT8NuwMcELyhACbybbEjHgYsw3gmAgXyi6xKSggPrV3ERLrf5TSVS0DmlXcgcodI96fVUAqkEkiUB5jvf/e531dy5c034FsZJYpL+zd/8jfr0pz8daBzEC+SZZ55RL730klq9erVZrMAQh/4E/fncc89VH/zgB9U111xTduHtox/9qHrggQdcCZbN6Rjn06QcPW1SuSRAAgIAOlkghU2+DO5jAQBEaUG5J6EUEaOKxKSUToGA4Uma5IhCPlYAQFEozUOJyR+slJgIojxjBUKijRRuBiPtJo48VBJlSnslCeVfx0II4A/rCkkAAvQhbtytNh8cjRkp95tP3a7iltxsAuKHZsYVmYgyjq1Yu1G1dGxWpbb5IAZgXJK8L3GhJ2o6pE8bD3zDowApuAmzsCMxXln42bx5szmw8sZFmHHAze7WUT+jKMsX/UL0jSjriqJspw1AqGf1oenq2OnBa4xiJ+AgVPGcaJcc6CzoLxzovoxdbH7Dwe7BgNmMWaLnu63Xng94vddtHeQbD32OF3mkecOVgDbq1kB2uGVGXVq16H3iiSfUDTfckJszwxe62rJly8zxox/9yACDhDzyk77xjW+on//85463AtRx/PKXv1SXXnqpeuSRR8z83DFzetKXBOJnbuCLjfF3UxoD0NszR1nBLQIQx7beAvBjxR+FP4mKhijkoqB7k0p8covs48QHADHWSRx8JyFvXGmwEsXSy07Cg0yW7Wtx/y60x51OJ/qqSTux61BK7HABLMYI8OeWlk0trU6sqP5slazcwBldKr1eNwFxZKzCScDTF3YfUg2dEOUMgr6y+jV1zGHDE9soJ5wVSB2Xl6VPc9u+x4qQaGdiJXXMMceYjUEYD7BWld2tcRMW98qZM2cmUo+o9LxkXBZ9o1L+uF13AgD7ho5Qm7umhgQAHoobyzl62AiE8YmD8UtchHF5ZxzjAMCmfQMGum3DNgAYdruQ/ibHRPollUAqgapKYOXKler66683Yx14wxe/+EV1+eWXm98PPfSQuvfee80iwlVXXWXAQNk0ywuR9Dvnn3++uuiii9Tb3/520wcRt5Sxdd26deruu+9Wr776qrG8v/rqq9XChQvLeuaxoPzUU0+VJYE+Lk3DEkgBwIS2BNsCkMEySsVcBndRApMkMoAbFB52nhQQx6b/zDPPtH8m7nuSn40t7DjxwYo5QDHtRpRcJoIMHOwgiQm8U4oTD070lTsn/Qd9SdT9STk6glyLctJAnFAs/g4ePJgjEaWISRUKi8gvd7HCl80HnQHA9gF/G25UqC7Q5VIuwGHLe/H611V9md34Wg91qFWrVpnJKooe1ldYblczCc9en3c1aYyirvHKN7IU3gFScH064YQTzAQFIBAXdsYIWShCL2OcoH0KcBjF86h2maL7yRhX7foD15fdVlTE/sGj1Z7+UvbGRdnLnqjWTsBliXBxkUk67sGnnHKKGcvQcbBix8tB2jCLMbRf2nHhIqddhehGnBvrFrA23+n3VALjQQK33HKLAft4t59++ml14YUX5ti+4oorzAZat912mwEBb7/9dvW1r30td93tFywIS/Ud733ve9XNN99sXI0fffRRtXjxYjVnzhzjDlyqfGKdvu1tbyt1OT1fIIEUACwQSFJ+CgCIcspqdJTxaMTagrpQBJOgBLK6CehngzjQzUo9rjusKpCSwk+pdinPQhT0Uvnifl74kMlWLejFMhTgD1cZkSeDE6AfYAODS7kkoEAteShHX7lrQjt5oN/+Xe6+OFwTWqOQOyECAP7EBRB+mUQRdN1vuADoLAUA7tfuFdVI4Gz6v6vkxgVYnoGrAh0ydff1qz3728vGJJl62HQ99igzWeU95QAA5N2kX6/0fjpUm57yKIGgz9ljdbHILmOB8M4nO1ZzyCY26Bn0FcQEFfdKFgYAUdyEBIgFoyWIoL+SvlXG6RJZY3vayQLw9d5Zqm1gguatQY93wXYDYROQJCWeI+2Tg4VxvGPQe2jDuPht2rTJHLRx2rCTm7sNAMocIUkySGlNJZBKwFkCS5cuVQsWLDAXP/7xj+eBf3LHrbfeqn7yk58o4vjddddd6l/+5V8862ClwD+pg37lC1/4ggIAJEET8QDTFI4EUgAwHDlWvRRxAaZiBuxqAIDUxaAfZyUQWTAxRKERpZVOBiUGIIdVfGKgSEoBQJFEbT9lciWTrWpSQ5sh0C0bfEiboZ3QXmg3lQYpoVV4kDLkfBI+hXZoTSL9YcoY/pkI4erLpyTAJoA/JkW2vOS6288D3T3qkF6gKEzTJ05UbYOjm8sUXq/V756BQTWoF34aQd8iSg8vWa3qurK69NKw5BFHHaMuv+g007djrUI8Kw5cMHEXwYUNMJAFniDPpxyL8m5EVX65umt5bbzyjcyFdye9RxaIGCvQKwACAVJYgGTzBQ6AabGoqrbFahhtxh6TnWQQRh2RlpFtV3VDLUVVbOo5TLXq/ra7b7qaOsnZIrvophInkgYA2mzQPmWHdgBsacMsiLLwxUEfCwiIPiTjnw0ARtEupI+VT5vm9HsqgbAkoNU93ceX1jvCqifMcqA5yvT444/nir/pppty3+0vvPM33nijcQ1GT543b5668sor7SyhfLddi+3wXaEUPs4LSQHAhDYAsQCEfBRPgvlGlezB3VYGo6rPT7lMBAH+7KD8gDgoNijfNoiTBH7cykB4ietziTMfvDdYd9ltBvdeYkJiUeR1VTvJzyLJSrbQLhN1t22uMB/3E3sE4A+XX0nERMLVNyxgqdQGIEdMnazaOuMHACKHzt5+NXPKJBFJ6J9PvbxBNQyWV8J7+gYNmCKTVZ6RTFZlox7AFxbDJk6Zpd759lNKuuuHzsAYL1DeLXnXxji7eezJ2FqJdxZl2TTk5JNPNnHVaJuMLVhYscDEAQAIiML4gn6ShCT8Q6uMcUmgW2gstQPwpt7DVE9GbwTXMy0wADiQ7ZDqEv3JvAL3YNowwB9tWNzc6Vs5cG2nDYt7MHpSpXcj0UJJiU8lMM4kQKw9Ev3BOeecU5J7NueQ9MILL0QCABJvUBLhC9IUngRSADA8WVa1JBsAxIIpymQDIfaqX5R1uilbJuwo1kzcJTEBZJKIRYiTwhpXfoR+L5/Cn62ke7k/LnlFgZSJZpR0ARzQZuyNHGgzAH+scotMvdJQTR680lYpv9BOvmo8g0r0eLkutPulm/uI7QcYzEKCJAA/Af7kXBifpTYAmVpNQKA81lbEJm7AUQGAyH/HvrYytn/D5PT25+8CbO/UyiSVkA+MAwD7v5q3RbUd3GVc3LAKZIHM73ttC0PamLQ5+9pY/j5e+eaZCu9u2w/5cPvlkB1YAVLYgEGsVrFYFYsq2mac25OtW7iVQazehcy2InKy2u23JXOUPn9QHeieov5kVlEWTyeSbAHoxCjtkRAXHCyu4B0B+AcoiBUOO2HbCZA7ihAMcX4vbP7T76kExpIEcOslsbuvbTxTyKMNyMk9hXn8/GZutnHjRkWMQNyMSYyTH/rQh8oWhx4PKEmIL/RArJXf8Y53KDYQ+djHPhapp2RZwmJ6MQUAY/pgKpFlA4CY7UeZ4gaYoZDjWgOIg1ItCVNhQJxKQfltJdZWbqWcJH0KL/CBXJKqMNl8RCF/ZAM4UAgWB9nIoZBOkb1MGAuvx/m30A6NSaTfj2zhE0UD4M/uR1AacPUFYIoilYr/19QYnYttUD6i3An46dUbVaYzo+P/lUcle/udd0hmfMLKm4Ox8P5fLVD7ugdNO8YCiwPLXqxWAAOjDJcRVM5xvV/6BLufiCutYdMlOoIf3rHyYxEBvQTwDyCQ8CQAJoAqHFhUiYuwrdeFzYff8oR/7pdx2m9ZtbjPyQKwZfBwla0btsDs6J8WmKyxBgDaAgEAoN/kwNhALAHF8ACjANz/ALzpY+MOaNu8pd9TCehZk9YVyusecZMSNEtiPKmUeHfdJgB+MY6odB+L5IxZ6F0swAZJl112mdnt16kM+pTHHnvM7FDudF3OAfo9//zz8jM3xrKJybe+9S31y1/+Ur3rXe/KXR/vX1IAMKEtgEkPiiMva9QAoK301dICEEUUhRlXX1E+eHx0QijYbl30bH5s5TaJTcHmhUman0lKHPgWPsJ+HsjEyboLVywmZn43cnCSmfAgk2WnPHE9Z7ebpNEvtLulm3wsIAD8oTBIQsmgTUQdp2tzy6i1stTN5+AQMfCqlEb1R1cVltoJ2NXNFTL9fMHLqoGQiBVo6u3LtwB0KhZldPn6g2ry1GnqrLPOMjtb8qyJybZlyxZzAPCi2GKBZS9uOZVXeE7amLS5wutj9fd45ZvnKbxL/+7nGdNebItVQGnAQCZa6HDSNtFhAFHwXihneeGHBr/32GNyEBn4rT/ofU4bgGzvm6X6tT5J6ssEX+hJyi7AQWXJ4glWQeyGvX79ejOGUqbo5ujnLLbIgowdv8tt3fK+uc2f5kslMJ4lcN5551Vk38s7ZS+G23sNlKpEAEBbly6V18/5f/zHf1Rf+cpXyoY5Y3y94IILjKXf2WefbXQ7xtXVq1er++67T7GpCeMtMQrZSATdME169/ZUCMmVAC8ejdwGw6LghpeLiRLgn60MRlGXU5kS3wngD5caSaw44urrdcJuK7G1BDSFjyCfNi88G/t3kHKrfa9MqL0MVOVopBwmWVj82QMTE6xmDfIQ103qLFeOl2tSXi3eES90OuUV2rkW1jNwqieKc0J7Jbq5jqsobcJeNMFimDbhZ7LilZ+spqEUANg9WBng8lpfWPnd7ATst65te1pdvYs9JSwA7XrbDnWrrXtb1bF/OssogYB8jJEof2wc0tPTkwtsj8sasdjYwKEaz96mM2nf5d2Sdy1p9AehN2ze0aVodxy0RyyqaJ/ocVipc+BOJZvaRDFWeZGHPZ4lUb+oc3ABXt8zWXUNDFsUD6iZXsThmLdfbzQynhL9AAYIJPpOLOZpxwDaLLYQR5fDb8xL3jnqGI/9zXhqRymv8ZMA+pIkN3FqAfxJjGVBEq6+6OW8+2wqsmzZMvWDH/xAff/73zcLZLgDo885pTvvvNPROvDCCy9Uf/d3f6e+/OUvq3//93835X/iE58wZad9SwoAOrWlxJwDAMS6yZ7MRkU8ih9gWTUBM8A+zIpRjgEBSby0KM4Af35duSgDflBsbeU2KtlFWa6tkCeZF+EjKA/c72QlWg3rLhlQZMIY5XMPu2yhnXKTSH85edAmBPizF0tYQGjWwJ+bVc5y5Xu5trujU7GrrlNq6Q2mQDmVWeqc103konIBXrNzr+rr1PGjKpn/aUbcWAA+8MslaqB+SPVY8QKZqGKxcsIJJxjwDyCQ9oAbJotKHFhnYXmF5Uo5yyt5N+z3pZSMx9L58co3z1DGpCieORspSNsE+EPXYfxCz+I7B3oObZNDQJdqti3hnzplnK5m/YHqGtJ9bbbYNW1NzwTVPjC8mDxYHzAAoCaQTUB4R6JoI4H4j/BmmQvQX4rFH+CBANrMS+yYl4y35GPBLXHtKEI5pkWnEggiAazbmBOHlewxxja4KVU+gD9JNgUqla/SeRYR7HTJJZeom2++WV133XVqzpw56txzz1WLFi0y3ht2Pr6zSFYq0Sd/4xvfUC+++KJ65pln1IoVK0w5F110Ualbxs351AIwwY9aJq7VAABZtWbCJIN+lGJjJYFJGbENRPmkfhRgrDVkxSEIDSgglC3lBymrlvfailSSeRE+/PJAu6S9YN0lAxLPhRUj3MPlXYnyWYnyL5PlKOsKu2yhnXKTRr/QXkg3bYnJNG3CXp2kTQD8sYBS7bTpYItjlU26PzrYrQHAOsfL4Z/0WE9n77CSFzYh3527SDWOLjiXLb5UDED7pkUrtullzXrV7eAuTDuRwPYotvQXgIG4vLAxEAdubXGxvLL5qvV3ebfkXas1PdWsX3iXMSqKupErrukcp512mum3aJtYQrBoQUB0DhaycF8HTImSHptHe0xO3PPP7tRdarFl9Ra9A/CBvuEFlwEdC7B3oElNairOZ8uh3PchldEhHDpVU91h5bKNqWsyF0A3lwR4wEILk/nCmJcsunBgeS2AIRaCpdpUqfNSV/qZSiAMCbAY6nVBNIx6g5Rh0wv4VylWn5e6bG8I23uqVBmCP0Qxx6I/wTKQORzGQLfddpv6xS9+UYqUsuc/9alPGQCQTPPnz1cpAJhaAJZtMHG/KBNYeQGjpFcGeRn0o6iLzobJOq6bonSjLAD6Af6FucsY/GBVGCU/UciosEx7EmAr6oX54v5blD157m7p5RliJcHgIKtVlMUknkHDr5Wo2/rtfPIskvgcRP7w4/UZ2DKoxXebdupH/gIGizsDeQT4q2abKJRHqQ1Ajpw2Vb3RG183sqgsADfsPKAn6O7QyMJdgAtlu2GLnmBqC0s1rUF19/arjG4HDRpYdUq4ttA/YEnORBWwBcsVxgOxvGJ8RbFmsiqLTvJuFLY5pzrGyjnhGX7GE9/y/KQ/rxbv9qYL6HbSHlnYwsWSQ9zXaZ9eQ6AIX24/bf6rJQO3tFXK5xT/ry87QXVk36R6M8OW2H11A6qla5o6bqZzbNZKdch1NgJpqh/fAKDIgnYiMS/ZKdSOeYkhAXo+B6ABur3dx0oZ6WcqgVQC1ZcAoBsLpXgXoheVS1itC/7APD2KxKIXYN0f/vAH9Zvf/MYYIvnBAk4//fQceYypaUoBwES3AQEAbbe2qBiKCtxgcoHlBcoAHY4kOiEmaAA5Aj7KtTA+o+InDNq8lCF8cI8o6l7uj0te4cMtDyiRgH4MUOIeThkokkzqaT/VTjI5sifM1abBb31CO/cnkX7opu3QHuhLxAoUvlghpS8J6qJAHUFTqfh/MyZN1ABg0NI93O8Oc8sVGAUAeKC9U3V19rly/4WQnj5n12kh8v6Hlqhs4zBjelhRXT39avrU8v2APVE99dRTjYUKbUgUWywCN2zYYCyuAFuS+m6IjPx82jzb/YSfspJ4j/AvY1Q1eUDHO+WUU8zGCzIhA0yx3dex2BAQxU3MJq/0y5hcC/690lqYvy67rfCU2qE3AJnehPX3sFVzZ6ZXtXSHAQC2q+rblBexV7UTsnheST+n3aDHczAuszjHBBzLaxb9pY9loo/+houwpPHY3wjv6WcqgVpJALCMzTI2bdpk5lelwqKsW7cuRyKW61El6RPAOlgA8+PynPYlxU8ndQEulklizojJbTUAQBnkZdAPKiSUal5kJutYYEiCJybrUccJEWVWlFupP2mfwgd0J5kX4aMSDyiQEhdS2iJtk8k5K1BRTIDctgkZYGTC6Pa+OOQT2qElafQLvaxEAtaQ4IfJBH1JLcBgQ4TDn00Hna1MJjWNulE53FbzU1FsAvLtx59XDR7CHpazAKQNrFrzhspOHhVVe1dvRQBwNLdeDdWxrABSOJiYiuUVlsXivibvCQDMeEnyfsGv8D9eeIdPGZNqyTvjIzoRB+1R4qwBonAwEQNIwTWYsRAwJSx6hX8Zo5P07J0sADf1TlVT6ukohgHAQwPdqiU7LTBb42UnYBGUtAuZG8j5cp9YUjc3N5uDdksfS1umTbNbOwf9MNb6uBFjRZimVAKpBKorgYsvvtgAgOjUy5cvV+eff74jAbjSSorSpda22BPcQ+p1+7lmzZpcVuYGaUotABPdBsQC0I2fflBGZZAX0MVveSgNTKaI8Semw5RFEE8m68TACUtxLUdjWPyUq6Ma12xZiUJWjXrDrkP4sCebdh1OcSExA2eyw+HHJNwuP4zvMkFK8nNADqWeQRgyCrMMcf/etm2bKRa6eQYC/InbZph1BilrIJNV21rbghRRs3s7tEtt2GnVlt2qfsi9KWK5GIDPLdqouge0heC00TXN9i7/cQtRMrEIPPnkk437GlaBLFjJu7F582YTn42+h8mqvPthyygO5QnP0CL9dBzoqhYNwn9cnjGLXAKiiPs6VlWA0gJU0/cJmC16ol95yXgWF/498eFgAbixd4p21W3KFdOV6VeH+oMDgLgAj6ckcwHRpb3yjuUq7sFYuNK3MsnHulXG9ahcCr3SmeYf2xIY0joIR5JS1PRee+216pvf/KYRCTH4nABAxoUHH3zQ5GH+fvnll0ciQnSvxYsXm7LBCOwYhV4qvPvuu3PZL7300tz38fxlVFsez1JIKO+i2FXTAlCUQa8iQ1lgpQ/gT9zzKIOVal7qaq/0iTLrlx+v/EeVnwkZvMBHknkp9TwAibESZWIjEzEmQLj5MsHxq3xG8Txkcix0RlFHVGUK7ZQfd/qZIKAUYAlqW2IBAp933nm5eG1RycpvuTt0qIMB/Z46pb5sxul0dOc86rudfeECgAN6PGjr7NXuv+5TOQvAX/5mucpOyGeqvTu4TzX9krivsQiBIiqxRnHJ5KDdAToDBvpVTt1Lofo57f7A7ieqT0ltahT+48g78f9w15I4awJUo2Nt2bLFHLNmzTJjJe24lCtXOckK/zJGl8sbt2tOFoBbetl4It/iui9TehdJtzz1Z0c9Wdzek+R8EnolqA5Gu8JylYO+FTCbEAzlNghJstxS2lMJxF0C6NHswosb8H333ac+8pGPqAsvvDCP7Ntvv12tXbvWnLvllluKjDCee+65HCjI/ffff3/e/XjrMF5dccUVeeftH4QH++AHP5jTuW688Ub7svm+ZMkSgyGUcgtm/PrKV76i/vjHP5r8Z555ZroByIgUUwCwqDkl54SYwtqWdFFRL8qfrPq5rYcJOi85h0zWUaSxmgD4ExDTbXlh5fPLT1j1h1kOvCQdAJTJlUw2cA/BsguXEEkSF5KOXp6fXIvDZyEPcaDJLQ1CO/nlGbi9t1r5pC8B+LMnH6w+ChATN6s/Wzal3H/J09YfHKyy66r03d5FrlJeroftAnz74wtUfQ9U5IN25WjpLREDsF9b/m3Zsl9lD8svK4gFoBMdxJCkfTFJZQGCTxYmaJcsUnCwkAUQSB/lB2xxqrfW5+z+wO4nak1XteqXhbU48854KEA1Gx+J+zqLw4ApHEzWyMPCGaCgW36E/ziOuWXbgLbIqxsqDrmwtW+6OmZi/p3Zuln5J3z86s+MLwtAaRdBAUBb1CzuyqZ/btunfX/6PZVAKoFwJHDXXXcZoIyFzyuvvFJ96UtfMoAevx966CF1zz33mIqw4L311ls9V4pB0Hve8x4FIIfF4TnnnGPGJ/SmPXv2qBdeeMGAj3wnve1tb1P//M//XFTPk08+qb71rW+pv/zLv1R//ud/bhbEmBOwCLZq1Sr14x//WL344ovmPjYAvPfee12PfUWVjbETKQCY4Acq4Fk1AEAZ5N0CgCihEqtNFAUUSJRPBvhax+USfoS2BDeDHBiWZF5kcsFk8+WXX1YtLS25R0I7ByxmhVjy5S7G6IvQlsTngLLNgfztCX8cxAvAguUwiwjS/6AkALTQlwASAwDGPZXaAAS692lL16qmfKysYtVhuwA/v2arash6I6J/MOO4s+/Djy9Xg2pIZZvyy+vQMQDDTvJuYOlH2wMERJGlbRKKgxVrDmKyAQIy3qGMJnkyKzwjyyTz4bctSH8u/bvfcqp1H7rViSeeqE444QTjpk7bZBJF3ynAIBMh2iZHJV0safyLnOuyW+Vr7rN1YLJqz0xQM3U4BjvVNR6usno9oj6/C7GzVPw+3mIAylgsunRFAXnMMB77Go8iSrOnEohMAmeddZZ6+OGH1Q033GDi9AMAFibAv7lz5wbyfHjllVcUR7l01VVXKVyRGbecEmAfOwRzlEos2v7iF79Q5557bqks4+58CgAm+JELAFhNF2AZ9EuJzcllM26x2qBdlHlRbkvxk4TzSeeFCSYWf5IE/GOS3dzcHGpAc6kjik9RWO0JcxT1RF1mXOgHXAH4Y9Iq/Q7AH8AL4J/EfUyK3DcdHAW17Wc4Y0KTas2G62Jrlx/G97B3AW491KPynfDcUYkV4NTJE/IyP/nMa8Pgn15gslN7t/8YgHY55b5LPDYWKAD+AFtwYaO98p0DS30BW8iftGT3B/KuJY2HIPQK/0njHXqx9ONgh0ZAQPpSrAHRGTdu3GgOwrDQPllgcwJzREcSPSOILKt6b6YYANyirf9InXpRyU4TdB/c0TtZzZjsYVciuwD9fby5AMuY7NRmCkST/kwlEF8J4IjAkaRUJXqvvvpqY0WHNSBAH/oMOsxJJ52krrvuOvWZz3ymJChXSZxsGvLUU08Z19xly5aZsvGoYGzC/Z9NgC644AL1t3/7t2Vddm+66SbjUUh4Fiz+iCOKQQBzBca2s88+W8EHrsSVFrsq0TzWrqcAYIKfqACA1bAAFOVPlMFCsTH5wQWKYL6ScJcCdSc+UtyUBOFHlBihOYmfwkupZxNXnphY0V5w9bUBQOn8q7UhTFjykQmiTBjDKrda5UA/tNeaflbzBPiTNg3YJ8AfA7udkiL3zSV2AJ4xcYJqDTnGni0fx+8eLV1wAaZdiKwdy3R58idPL9MbcGKB45EIfQdxAG0AcN+BDrV/X4camlpcVkeATUBKsSLvRqEc+I2lHwfx2ABbUJbb2tqMZSA7tBLzhtAXgNeHH354KLIsRWeY54VnyizkO8x64liWzbuMs3GksxJNYjFN20NfFEtA+lrGYA76WLFateOvSR+cNP6d4v9t1jsAk1oKQi7UNQ6plq5pAQHA8eUCLLpzFLq99DPyWal9p9dTCaQSiEYCLG7ecccd5vBSw2WXXVZ2LsF4g2sxR5AEfTfffLM5gpQzHu/Nn0mNRwkkmOdqxgCUQV4GfcSGcoy1FsAfEx1JAJMAf3HeHVH4EeVWaE/ipyjmSeEFOlmlod04gdfveMc7zCpT0p6FPAd70pgkHkTZrhX9hA0A+MOlUtoyq430JVioyDubJJkKrb1645Idh5yDxE8G0IzeWE1I8fXJDsZ92gV3UlNwleGxpa+pBgxwijG7irQV7gT8wMNLFJ7EmQnFhYWxCUhFghwy2GALbsEAgQAuuLIDDHKwEg0YQ7smtmCck90fSB8RZ3rDpE36IcocK7yjn+G6xQ7XAH+0TYllSf/LgfU9bZPFW5GBjG9hyjfKsuqy24qKJ/5fvf7X0p9v6Zepz6iW7mlqthqNOVx0c4UTA+kuwBUklF5OJZBKIJVAKoG4SCC4Nh8XTsYhHWIBWG0XYAFwUBSZ4Ehi1bi5uTkR1g2izIpyKzwk8TMpvCBr3ONoNwSSlXTkkUcaywPMt0lJfSYyQUw6/faEX55RlJ+0BdoEwJ/UDfDHyp4b62GRu9wbJa1+y97a0lbSy6TAc9VvFZHfx07AYQCABw91+QZTevryXfdeeHGzGtK+xENN+e6/CCPsTUAo02sbY5EOi0AAFxY9AAMBXQC7N23aZA7cVAAD4xrj1OZZ3jVkMR6SzbuMs2OFb54lYy8H4RYYmwED29vbjUU+cSyxXJW4S0l79k4WgOwAPKNpqto/lB8DsE/1q1ZtARgkpS7AQaSX3ptKoEYSGMLzpXgBsUbUuKs2afS64yrNVWUJpABglQUeZnUCADpZUYVZD2WJ9Q0uI2y7zQRGEu5MTNbZATEpSqIo87ZFo/CTtE/hJa7AEzIG3AHkof2QaCf2TtBMQCTZky45l4RPafsp/e6eFgsXWIFiESUyI2wAfQmuaNLnVCotCXIvtwFIRuVPRivxG8Z1P/pjR2+fOmKacxBmtzQ9uXSdGuzPqiY/5n+6EtsCcPXanaq7s09lJzkr71FsAiJ8SpuT35U+6aNll1YAb7EKZBwVF0xAbwBvwECx7q9UbjWuy7tJXV75rgZ9UdZhj6ljmXdZcKHvBQAECGTMxmpV9Eu8PQAEsQyMU/t0fP5DeqEg+0bepYzu9Lb3HaaOnoTFbf6mS12ZPlWfAoB58ir3gz5B3g2343S58kpdG8vvXCme0/OpBFIJpBKohgRSALAaUo6oDlHCorYARAmUXTb5zsHAjMUCCqPQERGbkRQrSosoMZFUUqVC4woA0k6YSLAbNN9JtBsmubh12q5vtqKX1GciPNgT5io1gVCqqRb9TCgB/nA7E1nhEtmsrYcBSaQ9h8JUTAoptQEI5HVnBmNCZXkywtgI5J4/vqQaWQNwxuzKE6CvEgNQ0oO/fNFYVWYbnQuLYhMQaa/yrggtXj7p93C/JJA24B9gINaBLIIQD5WDWIIAgbwPhTEvvdQVRl7hmbKC8B0GLdUuw+Z9LPZLTvLEk4Pj1FNPNe0SS0CAasblrVu3miNO7dOJB8C/Or03uJ129U9V/dpceHL9RH06HwA8NNitBrQLcJA0ONSlx7OMfkf8bG8UpObq32svnIsuHRYV9jsXVplpOakEUgmkEkglkC+BFADMl0eifokFIMoZA3LYAzHWWoA3gDj2gM8KcCGAkyjBaWJFmU8q2GTLO268MJGl3TCxlXYDjdJusPIqTMID55P6TISHpNIvk/uoFHDCBQjwJ88fMATgL0i80KjpFlqDfJazAGyzQK0gdXi61xkzK1sEG4EETftaOwOBSOwCTKKNvrpmpxrSnr+Z4u7E5GnvGrVSNydi9od2a7tgMs7SZwKQE1OXY+3atcYaFjCwVhb2dn8g71rMRBkZOXZfPt54ZzwDgBaQGlBwUMcyZcHZbp+ysQ27DcdFRs7uv4eZdtJQ11TUXjoH+1RmZIfgoouuTwyZnYAnNsxyfUdSM4peB/1hzzuSKpOU7lQCqQRSCSRJAikAmKSnVUCrAICcZtKAghZGQsHDXZOYMKL8M8jLoM/KcNKTKC3CU5L5iQvwBBAtsdxk4mQHxMfNqFSyJw7S5krljet54SGlP/8JscMzVk37948GWCeuFMBfGHHPRO75tcbrV6kdgJs0CNSOdawPQK7aHHb0jrrp+6l7+TosgTO+3X+ps2cELH1y3hqVGciqITb/aCiO/0deYgDyLobZPqJ6t+kbZ8+ebd4JwBWAQFzjGZ/4zoGlPUAgFtTl+lJ4DzPZPIcpyzBpjKosm3cZZ6OqK67lylgOAH366acb8A+wGv2Q9omrMAeLOdI+bev+WvBVagMQaBli1cAhTWiapQYyDaqpIeNw1d2pfr0RSAoAupNVmiuVQBwkoFUE3SfEgRL3NCSNXvecpTmrKYEUAKymtEOuy3a9BbQLCgAS+wUAB3ckSUw0sPZjV7iVK1ea0yiESVeGhX5RboXfJH7WmhcnwJgt3o8//ngzIXDjwmZPLJP6TIQHe9KYpPYUNv30JwB/WJBIYtGieQT4k/rkWtDPuMod19k9nfkuZ8Lr9KYGve9k9WMASv1ePtkEJEi6/fEFqjFYEUosAB+ds3LY/beMt11Gj1NsGjJlUumFB7/8hN12hQ7KxZKK47TTTjMgC+DfoUOHzIZbsjGDWF0RfzcqWoSmuL5XQl+Un/ZYFLWco+QjSNkiA/QMu32yuQ1hHGifra2tZmOvjRs3Kg7aJWAgCzyy2BqEBq/3OlkAbu0dtgAcyDrP9mdMnaBau6eqow5z3q3dDQ0D2Q432RKfx144j+L5yrsmn4kXWMpAKoFUAqkEYiaBFACM2QPxQo5tAWjvxuulDJR7lDdc8/iUhIUOwJ/E5LLLRyEU0EnyJ+1T6BflNmn02/TWihcnl07ce2k3bnZvtXlA0YMPnkdSn0mtnoMtxyDfRdkOOuEHrAD4k7ih0MRiBRZO7Hgq9QSh1b5XygtKt11mmN+3tIz2q4XlTtGW1dqUrfB09L99WBwGdQF+42C7qvOz+4glDWIAdvf06/HqoI7joEWHBWCZhBtwmACgtDFpc2WqDnyJhRMWUTiwogVokY0ZsA7kEKsrwisQRzOKJDxTdjX4joIHv2XavEv/7respN4n43Hhs6d90u44WATEKpADTwD6fg7yoAuQhwXqwjIik0lmW1HRW0ZcfLu1G7NTmjKpQbXojUCCAIBYAI6HFDUAOB5kmPKYSiCVQCqBWkogBQBrKf2AddsAIAqYl4Rii0sewB+TC0lY+rGxB7GJbGXNXuVj8Hdj1SVlxvFT+LEVmTjS6YYmmZiIou7mniB5nCy7mIjSbgQw9lO+tDd70uWnnFrdM97px3WRIPH2QgL9CcBfNSyVavXcK9W76WBpALARbzT/HmeVqg71ehAX4A3b9SYXeuLdGNDXmV2Af/HoUu2zo2OF1tepbHE4rzyeO7r71DGH551K5A/eIywCTznlFGOhDxgIwMKOwmJ1xZiN1RWfMiaEzaz0cWGXG9fy7LFovPEuz0T0inJtigVj2diGdkn7xJOEmIF4lXBU04W9LrtVyDefvdkGxSYgpPYBZzPkCXoxAQvAIGm8AYC0iajei6jKDfJ803tTCaQSSCUwViSQAoAJfpKALgySKKluAUCUOSwHAP6YPEjC5QgAp1QgZwHMyJ+CZiK1eHyKYi6KehRU0cYAeLDssgGeMF064YO2FSUfUchGyhSF1Z40yrUkfPqhX9oFwB/tQxLxopq1q++b3vSmyCYIUpcfuuXeanxubmkpWU1Ag7iS5Va8oA3nhvS/Og+AXJBdgP/j1/NVvfO8uyKpdgZcep95bt3wKdx/dZ9RLhEHcCwlxuFjjz3WHGJ1BdjChl0s6HEQtkMss+wwIX7lIP2xvGd+y0nifcI7tI9H/uFbZCB6BudKJWSElTfHgI5tSpxA2ieLhngMiAs7rsG0UfK5KbdUfY7ns63a0jjfEm973zTT25H/YN+o3mvfX984pC0Ah92E7fNevg9k/bsPe6mn1nllDmDPC2pNU1p/KgE/EhjSShhHklLS6E2SbMcTrSkAmOCnjbIFAINiZbvoOrHESizuQ6zEskurJBQxXDYrxQ+0lTRRCKWMJH4KPwAY8CO/k8xLFM8F+bCiD2CMa6ck2guAcZgunfIMkgqgCf1RPAeRe5SfMsF1I3/yAAQD/NntYubMmQb4k4WELg1MTNGAhJQdBf1Sthu6o6i/UpnlLAAHhpIR/w8eOwLsArxx70FVnw2uZB9o6VStB7sU+nrWhfYS9k7A0sakzVV69lFet62uZKdWrK4Y33kvOXgPsQrEMjvoZD0OPEcpT6ey5XlzbTzyD98ynsn4xjk3iTjA6JYchS7sxA7kIGSIuAiHAVZDV6H1H+e29g5vkDepvkkdHHQOuZDVm38c0i7AQdJ4swAM2qcUytp+3wqvpb9TCaQSSCWQSiA8CbhQocOrLC0pfAkIAFjKApDJwI4dO0xsFkBAEoosVgQoZkwi3CR7oJfVPzf3xTWPzU8KABY/JRQxrEmw+LPB5UKAp/hO/2dkgiUTDv8l1eZOoT+pSqwb+uENQJh2gVWHJICG5uZmAzjIOT4fen61qp9Yr25699n26XH1fbOOAdjQNqQyM4sBsC69+JCU5HcTkO27W1Sfdt0N6v6LnF5bvdOICwBwcGKxPAtl2a5dgMNM8m7LuxJm2X7LghbcfjmwBGShjzEfnQCQnmPt2rVmzAcM9BqLTfrjOPHsV1Ze77N5H4/8Iy+RgVcA0Ja1uLCfeuqpxjWYWIGA1rRXAauxGqd9opsGCjFTJv7fjKZpaqcaXQC3aexTA6olsAvw6Jholz3WvsscwNajw+KRPpZ3bby+b2HJMS0nlUAqgVQC5SSQAoDlpJOAaxIHsKsrf5dJ3Hux9sMFQxQ4BmvcLggqzsqrl8RgjAJIWTL4e7k/bnltZVbkEzca3dIjvITBB2WwMo/Fnw0qE8OtWQM8KOlRpTD5iIrGcuWKwiogQbm8cbxWjn54YsIG8GfHDK3ULpasf0Ot6zyobnjXmaqpscyWrQEEUo7uAMWGcmtLd49q1RtRTNI4VL0GAQcsEBD4qo1Fmco4Vii0FBVCvc4bYhZl5URnr/PE2TGzdfK7jywMLc7hgV3DE+whHf9vqKm8+y8khG0BaLEVy6+M67NnzzZ9NS75AIGE/GDxj+8cgDEALVheYaXlNsl75jb/WMgnfbmMTWOBJ688iF4RhgwoA2tUDjYLAawGDER/xZKcA7Ca6+iqfkJIlNsBeEoDG+U492Pd2b7UBdhl45A5QBQAoEsS0mypBFIJpBJIJRBAAikAGEB4cbhV3CYErFm6dKmZpGMNIMorSj6gHwqVF4W/kD8Ge5RBUQgLryfpt624iDKTJPptWkUxD/JckAFgMaAxirkkXMRx9WXSGHUKg4+oaSxXvtAv7125vHG8JhN8m36+O1mC4voNIFwpdMC2/W2qf+KQ+tbcheor/++lkbAtdFM49Nq/I6nQQ6GbtPVfPa+TBtomH1AaABy9ecaEJtVSYjI6mis+3/y6AK/Yvls14nUXEOisG9RRC/uzBrMcqoz9GcF1jLEYgG5bA+8AVrkcbB5ix2IDwAdkWb9+vTr66KMNGFgOaJH+IE7vlVs5BM0nY+p45F1kJzKQ8U3OB/1k1+oTTjjBANaA1QCBtFN0EYBBDuJco7dy8N1NqstuK8omOwBPrJ9QdE1OtA10q77Biaqrf4KaOsEZJJS8pT5TF+BSkvF2fjy/b94kleYOLAHcCRIWAzBx9AZ+SGkBUUggBQCjkGoVyxQLwM2bN6srr7xSLVmyxOwU+O1vf9soTLIzqw14+SVPFMCkA2bwL7zwXRRcvicxCS9++HCKDYnyxQo8bceti3gYchOlTyacYZRZzTKEfj/PoZp0lqpL6Ef+HGIJalsXs7AA8OcGEF63Y7/qyWgLN+3l+rs1m9Q/vOdcdfg0dyEHStGYtPObD7aoRj2XrNMAYIP+bOjWrsBThpGwwwAArXisVecNMrxYAPZ5nxTv3t+uevoHVKPuU4Kmxt5hd2nAv4zesdNNau8eXcxwk79SHumb5F2plD8O1+1YbLjtsykD4Ap9P4DdKeD6AABAAElEQVQLB/28AC0AM3ZKIs82/UG+C+8yxq7bule9dfbRQYpM3L0ynokMwmaAd0nA6re+9a1m3AEMbNGbJ+HJsmnTJnNgbU4bBbQup88WWgC2DU5QrYPDbbpOByIolToGe9WEhsnaCnCaBgBLb9xU6n7O96ebgJQTT3otlUAqgVQCqQRiIoHSo2FMCEzJKC0BFDMs/7Du+/73v5/LCECHonTKKafkAV25DD6/iNKVAoA+BRjRbaKYi6Luphp26GMiiEuYxIakHIkN6Xa13U1dbvP44cNt2dXIJ6CATBqrUWeYdQj9WAi9+OKLeS7gWIIC/InFsZt6n1iy1mQD/MrqP199dJ763zde5eZWT3mEbm5C9vzu6enXCyClrT08VRAg86JNOwwACtAGZDVl75DqmD0MXk1u1C7R3jG1ANQEu9XPLsB3P77IE8hYkkL9XBt6RtBK/XwzLiNYhL0LcFLfbZErFrunn366IhYbAD9jAEALesTGjRsN0IJ1LzqF7NAqPNvvmZQ31j8LeX960bpxBwCKDGR8jvKZE/tPgGjaJEAgB14JxJ7lIA96imM8yyFtapwdjhMqdMoGIPzWKnPZ9KbDJhoA8PhZ/gDAdBfgsuL1dHE89jeeBJRmTiWQSiCVQAAJpABgAOHV6lbAm//+7/9WWPmtWbMmR8Y73/lO9bnPfU5de+21oQJ/UsFYBQCTDmiKYu4GAGRTGNx8UaqFb54rSref2JDSNsL4FIXPDR9h1Bd2GfIcKJdJk/ATdj1RlIfMCchOItafJKwtmjXwJ5bGct7N57Itu4azjeA2y3buVhv3HFQnH3O4m9td5ymU84pNu9SP5i5V/+eWa12XEVXGVVv2GuQPEJTU2KP/DOgfTQQ5N6dq9geSvJAwoaFevbxxl3rnyce5pnnBhu3G8tFTRQ6l1+t5ff3I5N1462ha3KSoYgAWtjk3tMQpD30+MQA5AFoAAhkT6ANw+eeQHVolXnDSefYjfxmLhPfl6/LBJT9lJu0ekYE9vlWDB3uXa4A/2iegtR3PkgUpdBfasWmn2R26qxne7E5o3NI3Gr6ktwICOH1qk2rt9r8TcOoCLFJPP1MJJEMCWlXX+noyaBUqk0av0J1+xksCKQAYr+dRlhpc8X70ox+p22+/3VhuSWZcfD7wgQ+ou+++W05F8ikKoABHkVRSpUJR6OEH5VYU3CpVHXo18lzK8cEKOht74O4l+VhJB/RjJT1IbMiwGBI+xOIgrHKrVY5MEqkPGQtgXq36/dQDnbQJ2oYd+xELiyAu4DzD3e2dw+CXBm6MfqXRpi8/+qx6+B+u80Oqq3uo94GnV6hXd+1XcxauUe+7+HRX90WRqaVTx5TqHczDvjTsp6btGlKdb2GaWsEcJQqi7DK9oH/6vlm9E9Sn/+sxde3FZ6h/uPZdBuS2iyv8vr+1U3X3DaiGEJDOxp5hWWU17pdtdE94R8i7ABfyOBZ+A7TgLXDSSScZ8B8wEABQdmgVHhn3OZLQrwnNQT9lrGRs2rLzoNrb2hG0yMTdb8ugFsQzrmKNysHiN+MVYCAbhnR2dppYlhs2bDC7YJ90/DZ1eMGsxrYA7BrIBwcL+Zk2qdFYABaed/s7M9SrMkP9us+rvfW5W5r95JM5gOhsfspI70klkEoglUAqgdpJoGCorB0hac2lJcDqJy6+3/ve94wLBDkBbz784Q8bBehXv/pV6ZtDvCKKvyiEIRZdk6JQXuAl6fyIEubEB9YdgDvsBCnA2oQJE9Sb3/xms2pOO4pLKsdHXGgsR4cNAIqsy+Wv5TUUeOKAYQ0qln9CD+AfGwcESYvWvq7dfodLEOs3fm3Tk7bn1m5Vl502O0jxeffach/QE7xXX9+jgcc6ddecJeovLjg1st2H84hw+PGdJ7X7qyRWmfV3RNLUrf9kh1S3fgZJScdNnqp2b2oz9D++8DW16NXt6uarz8mRbz8DOfnAnJfCiVWtQd2GvuEl+qEG9+6/0BE2ACjvtRO/wndSP+l/cfXnYDGA/gEwkDGEBPgyb948M26I+2VSeXVLt/28fzv/Nf3eur1z7OQTvULG51pyZsezJFQFQCDtFM+Gffv2qekNS9Thb86nUDYA4Wxb/7CVe36O0V8TJtapA/v9WwBS0kD2kAbJjxwtdAx+EwBQ5gRhsSjvG+WNxT42LDml5aQSSCWQSiCoBNz50QStJYL7ATVuvfVWRdBg3NPYxe7cc89V3/nOd3IKq99q77//fjP4MABVOsgbdbrzzjvV1772NQP+wevnP/95tWXLFvXjH//YWG9RvyjpUdIig70M/lHWVY2yxwo/opijqIsChXL86quvmk1hWDHnPMHdsfS48MILDQAYJ/CP5y0Kn/BQjTYQZh1CP2XGlQfeXUC/xYsXm5hfgH/QjRsVQdZJYbSLJ5dtNGWZP8PYzfBvjYB96/cLR6+F8M2W+yML1ig2nCX16dn6v973h+EfVf7L81+48fVcrXXab1XEwPfJ+/RkdKDGAQBHANockSW+YMHXsb0rz5JxX1un+refzlePrtiruvqcgcynX9k07P5boly3pwH/ciCypjnb5PZOpYK6AB86NAx+SY1xfa+FvrA+ZYfWSy65xOzSKuXifkn/sWjRInPwHWBwrCZ53oyxS/WiRq4djlWGC/iCf1sGBZdr+pONqND/L7vsMnXWWWcZ4Hra5P15NOl1FrVtxAUY6+uDffnvc15m/aOucUi1BHABprzxsBGIzAHC0BUKn0H6O5VAKoFUAqkEopdAfMx/PPD6xBNPqBtuuEGxo50kALBly5aZAzfZuXPnGpcWuZ7kz89+9rPqgQceUJ/85CfVpz/9aQN2Cj8SlB9XiKiTAGayIhx1fVGXbwNnUdcVZfnCB3W0tbWZCRpWo5Jw8cKdk3hudl65HpdPoS2p7UvoR54yaYqLbJm4Yy1hT9ihF+APa1DiJ732mrZwCYn21a/r2HclUqu2wnhw4cvqxovfWSKH/9Nzl23Iu3nhljfUuu371FvfclTe+ah//HblejUwmM2BZgZr488ICji5VcdaPF4DZy5BuKjpLVf+YX0NarAEyPPazk7V1t6vrvkfA6pJ72osqa2jW3VqcLk+BAYbRzb/IPYfOwDrTkyqqfjZ2z+o+rRV6MQm76pOVqMHd9z+lPq3r/9VUT026Fx0cQydgE/GDxKLj4wjbBzFAhP6FzGI161bZ3aNxyqQ3VzHkmxkLIKnnS3tajI+6OMoCf+wbI9vcRIBdKHbGP3mkLbws6w09wxMUb3Z4Xd/ipqgDmhAs1waasiotu6pKqPN1xvqy+ctVc54AgCjahNjqQ8p1U7S8zGRAK+5v1e9dgwkjd7aSSqtuYwEvGvFZQqrxqWVK1eq66+/Xu/y2GN2pPziF7+oLr/8cvP7oYceUvfee68iHshVV11lwEBWCYOkp556yri8lCoDpTfqhGKzbds2x9g7Epy/GhaAMtjL6l/UfEddvvBjK7lR1xlF+bayxPshCXC4ubnZxMax88j1uH0m/XnYMo5LmwL4w42PSbtY6iBn+q3CTV+E/qDgJYDLQd0/C/bjZDXzoxdWqr+94O2huOcK3S2d/Wpnq14UqrdQNf31iz9+Wj32bzdUtbk/sOiV/PoKFTb9e+LBIdV3hEVr/h2x+DVRNaiBAwPyKItomjqhQc3a06MG+zN5AODP5i5XYCWB4ZLMkKrvHxYesf+yevMUrwk34IkzvKs637/7GbVtg3YnH+dJ+gP6DRYLOIi/Rr+CdTl9DG6YHICF9C0sLMjmIUkWn/C+bU+njtmpreF0exxPyR7HZHyOM//1Q9vzyLPj/zVlKvdG/dp+PKtXGdp7pqhZU7vyynL7YzxsBCJzADEKcCubNF8qgVQCqQRSCcRDAt614hrTfcsttxiwD9Pzp59+2rgzCklXXHGFOvnkk9Vtt91mQEA2y8B1NkjCZRIQpdap1EBbTQBQaJDBv9YyCVp/0vlhcsKOrVu3bs0TxYwZM0ybxS1ewJG8DDH9IRMMmXTFlMySZNmyrjUPgH2AfkzSmaCTaO8C/BEHsjAJ/fakrzCPm99/WLEpP/abw5y5byij/uN3L6h/uebdbop0lef5Ta3aQqwYIDrY16vufWKp+rurz3NVTtBM2w60qV1tBZsFIANIs2Qxdbd2Uz4iaG0B7i8WVVFhdQf1JiYWzXaGCY0a/GvJqK4NB1Rvd7+aPG1S7vLvl69X9eXj7efylvvS2KvdfyWD/pKZkPslZyt+4gZ8xIypFfPZGdq06++T89aqGV2WOZHOIO+1vCv2PWP1uxPPjDEcuGASX5Z+prW11YQiYQF248aNZuGJ/obNG6RvT5qMpC98aeOwRf1QJr89JI0fr/QK/9wX+2eYPaj7qlGvIGjeau0APLmB/ql82IXu7HCMQNyA/QKAA5lDVD2mk8wBRIcOk1npW+UzzLLTslIJpBJIJZBKYFgClZfEYiSppUuXqgULFhiKPv7xj+eBf0ImcQElgP1dd92Vs3qR62PtU1yA2SE46iSDvQz+UdcXdfmi0NpKbtR1hlE+E7K9e/cq3ofVq1ebjWCk3DPOOEOdffbZJp5b0hQooTdpz0NkL+2J37XigWDomzdvNvG5sBoG/GOxhEWMd73rXerEE09UTuAfNIv8+R4kPbtqi6vb57y2UbV2aUvBgEno3rCvdHynXyxcpQ4eir6PhJW7nl6SB/QB+gFbFeJoAGQNnYVnAwrDw+2Vap6g58KNvaULPHXSNDWxP6s6d3Xo3Y5HJ9YAbq16E4l62QWmdBEVrzT2DgMuQiubgHhNHV3lA/87lff//a9HVF+3tgYaZxZfTrJwAgAlHzoB1n7nn3++kniB9C/cw6YMK1asUPPnzzcLstXwUhC6wvoU3jftHQb0M4PSEsOqId7l2OOYPb7Fkeq6zNYisrb0Ts+dmzFtZu57qS9tA8NjSEuX/41AxpMLsMwJSskzPZ9KIJVAKoFUAvGUQKIAwMcffzwnxZtuuin33f6CknLjjTeaU8RDY9e6sZzEArAaAKAogGMNAEwKPyjjuFktWbLExGuTZ46lnyRc3gUQkXNJ+ZT2ZU86kkI7dNpyl4ljtegH+Nu0aZPZ3IMNkmjTAH+zZ882CyUnnHCCYgfFcknoD0r7ul0H8qopBdlktWnZVx8N3j9D95qdHWV31c1oIv753ifz6Irix4CW+9KtO03RRXwXntB0H7YjCipclllIj3UbVn+NrfpPifRnxx2tXn95r5o0sqsmFoCSHnpyZW4HaDnn57NuQLv/jlgRZhs0qK5dgP0krxuBrNu4W63VukPvnzbpOI6jG5zY74W8K37oSdo9wnclntFFTj31VHXZZcObMhx55JGGVTYaYtOy559/3ixaMYbFYczddaCypRZjEUdr7/BGJ7gAizyS9hz90GuPxTI++ymnKvdktxVVs7VvFACscxGQoGOgV7HGkAKARaLMOyHvbwoA5okl/ZFACQyxQVsCjwSKOiU5ZhJIlAvwwoULjfhQNM8555ySorz00ktz11544QV15ZVX5n6PtS8CAFZjdV0Ge1spTLI8k8IPyhaTJjZwYDIlidiQBGVnx0YmV6QkPxuZYCZ1giX08xyqxQPtgXbBBh/y7AH6iNOFZY6XXfqE/iC0H9LWX+0ajBz120QYSMQ5LX1jl9q056A66ZjhHYidc5U/C91Lt2vXr4by61nr97eop5duVFeed3L5AgNcvV9vbpIpDDRfhn8s7Op0jLshH66tAciseGtTuwbfRrGvvPxnHn+02jBvu5o6pUm1vDaMYPZZAODvl6xTdSF4Sor1n6lcz8r9xP/j3nYdA9BL+tp3fqvrGt6+ZP9byoPmXspNal7pD6R/qMQHQJFsytCrLUHpm3ARJm5zS0uLOeijjj32WBOHNGic5kr0lLr+myVr1c3vu6DUZXMe3jfs6h4OaaC/A0FntBtwo3Z/Hw9JxhR4jTsAWGgB2K+DkO7oG3X9HyjRn9nPkY2Gpum+uEVvBOI3DWQrA8t+y47LfdUAAN32N3GRSUpHKoFUAqkEkiSB8jOmmHGydu1aQ9FJJ51UdmJLXBpJco/89vqJpeFxxx1n3OaIZXPBBReoL3/5y0ap9VpWFPltF2BR1KOohzIFMJPBP6p6qlWuKLS2klutut3Ug/smbpyLFi0yMZUAe1CKaI+0Q9x9ef7CB2XGlRc3/AofSeXBVlijfheZWBNra/HixSbWHzLD9Y6+EVdfgGEv4B/PR+gPQvsTL67LB/8qPXg94frKY/Mq5Sp7vadvQO1k98dKSb87//nYQtWvNymJKj26XI9RhYCf/AY9KEy1tgIspEf/rtfGTk0lNpU/6Zg3qR0LdhggpPm46WqwZ1iWfT3DFlJdPf1qX0+XaggqYg22NOj4f7mkv2Z8YnFsAuI2/e7p1WrngAawNb5jwJ5JDerrD/7R3G6/F/KuuC03yfmEbz88szhF2IF3v/vd6txzzzWgH/08MUpZuGCBVvowiVVaDVkB4s1/tXKoAvrVV18fiec50hwzenfo8ZLssVjG57jyXpfdlkfa633T9IbAo1OcHr2Q6ibNnDZBWwD63zxwrLsA0x9Iu5A5gRu5uskjfY2bvGmeVAKpBFIJpBLwL4HEWAAy4WXDAxKBpculWbNmKSzjcJEkEH6Q9Nxzz+VuP3jwoOJ48cUXFRuM/Nd//Zf61Kc+lbteiy9iAYjyjBtglDvvyWA/1gDAuPHDc8Riwt7AAeUbiy52bmVSZSd7YiaKmX09Kd9lgpFUHqrxHLCiwcWX3TdFWQb4A/ADGJZ31M8zF/qlXD9lLHhtm+fbtmh3y4Xrt6uLT32L53u54VfzX1UDo/O8smX06s1HPvy9R9Q//I/z1KWnzS6b1+vFFdt2q9ZubdKnk4315b7nvuSXPIG49YAKDhuY5OcM+VcpetqsjTesKo+eNU11rWpRmcFh877eN1pyV3t7hkG2X/3hZeP+26CBzSCJnX/rR6wI9aacwwL1KR+3LsBZ/Qx+8ODzKnOYrnCEfFyhf//SenXicYerv73izCAsJfZe6Q+kf/DDCPcefvjh5mB8o/9CN+vs7DQ7CrOr8Lp169Qxxxxj9LuZM2fmFiT81Ffpnt+tWK9a9UYvlRK8v9E28k6PtMdB3f4nFu+hVKmoRF63x+Igz78azBdaANruv9Tf0T+8SFGJllkzpqiNbwSJATi2LQBtfTmIvlHpOaTXUwmkEkglkEogOgkkBgDs6BhZhdWyEKu3cmIRABAF008iZtb73/9+Ez8L0IVEHJtHHnlE/frXv1YAkn//939vlNRPfvKTfqoI5R4BACkMwDNKADDpAE2hwEV5sZXcwjzV/C3unHaMJGgst3Mr9KGY82zgIy68+JGbTDBkwumnjFreA/0c0B82D7j4A/yx46aUzbsO8IcrnbTlMPiX8v2UtXl/q/fbNNjyjd8tUL/3CQDOX709B9i4qXxrT4f61m8Xhg4Afv+ZF52rHzEYwr3MMek2M3XnkOoaHmYcs1TrZGP3kGocDeeXq3bapAlq2p4BdaB9GAx58/HaEvAPqwQnU/0jFoBPvrCuyAAyV4iHL+z+Kymr3X/97P4r97e73ATk+/c8qzqyGR1rsMHwBQU8Mlyhv/fEInWMBkAlSV8lv8fyp/QHYfEsCxaEKWhvbzcLXTLm4S7MgV7DuMeiRhQ6zW+WrVWZvsoWYVgLdwB464Ygu2Fn9Dg7XpLoEzK2xZbvId1pZXflkbelN9+Kr6Xf3YZTEyfVq67+SapvsFFNbPRuyjyQZUVn7CZpE3AYpt4xdiWWchZ7CYyqG7EnNSUwlUBYEkgMAAjgJgkFslISpRGLGa/pr/7qr9RHPvKRohVoXFiuv/56NWfOHAMO4sby+c9/Xl1zzTVm5dprPWHktwFAQAJ7Q4gwyrfLkMHeXgG0ryfte1wATdoo7lBMgmSyRYwkgGcmQW5cOccCABiX5xGkHcMD74c8xyBlcS+gvgB/UhYWoM3NzabPEZnJtSCfMsH3S/sb+w+pXtysCoEufjNnLmOl19LXq37+wir1oYve4YmFDa/vV2+06sUhlyG59JxODemjTbuFPrBgpfrIJWd5qq9U5vaeXrV2d/7mJ7m8LpTLSQf1s642AFj4nLQF3AQH45V6DVAeP9Ckdu3QRI6kIyY2qD3yQ3+yCUivdsXe0dGuGnjWhWVbeSt+1XTkuf/qsvzG/6OujhGrzHL1HmrvVnPmvaYGp2sQf8TSUMP4OTZwi/7Kz/6gPn7ecepPZuVbYJcrdyxck/5A+oeweKK8GTNmmIPNQ1jcAPxrbW01/d769etNmIOjjjrKjIOEYAmLho17D6qh/uF+ulyZi1/bPdqWR95j3IfHSxKwJ8xxJhLZZV/X72o+oGtbADbVNaqDA+5CATQ0Dj/o1q6p6pgZDh1iBQbGuguw7aovc4IKIvF0Wd5H+fR0c5o5lUAqgVQCqQRcSaDMlMzV/VXLZLs94kJSKclmCZMnT66Uteg6Smm5wed973uf+upXv2ruA3S77777isqo1gnbGlJ2hY2qbhnsUQplUhBVXdUoV5TaWgGaPK81a9aYXX2Z+CBTgOuTTz7ZxHED5HED/iEr4UUU9mrIL+w6hIckty3pN4I+ByyXX3vtNRNugIkxib7stNNOM/EfsYwReYX1HIR2v/InqH4OMSkkygUIds/C5cqrdc1/P/uKYodYtymjsRuNOxg6fzxvpWLX3jDS/37mpWHLNwc+xXKopGwMOXVq0gGHm8MgrkQZhbXhiixut/YtzdoKedf6UfBv4sRG9cbyrXYW1adj/z367Cql4+6XHTvzbirxo6FvFHiDRjYUyQZYqnRjAfj1/5hr4IPMRGeVyOCZelfiHy/dqQ5pa0d5V0qwMKZOS38QJc+Mcyx2nX/++eriiy9WjH0s9FL33r171fLly9X8+fNNLNygG56t3LJLdfX2675mSA1WAPNWbNyfe5byHle6J3fDGPgizz7ssSZs0RS6/1L+lt7RHYBnNLnf1GNopBNs6R61+PVCb1+mzQDZtdIrvdDqJ6/Nl8wJ/JST3pNKIJVAKoFUArWTgLO2Wzt6StZs7xTnxq1XwDAbICtZuI8LuP2KQoxiWqtkWwAKz1HRYg/2thIQVX1Rlyv8BAVrvNKJO/vq1atz4A5KNgA3VhAXXnihsfwT2tyWLQp6tXlxS5+bfPI+jQUeZOLkhm87j7SNpUuXmokv16ZMmaJOP/10MznG3VeetX1fGN9F/n5pf3HDGyXJcLMrLPH5vjP3hZJlFF6AzpVbtIWOy5TVKA7Wf5IG9OT/X3/9rPwM9Pn0a5tz9xuwKPfL/Zcp7llxX6jLnMTca+oqzvz2ow9XB9flh9E4YkaDatvdlpcZAPD3C9YqE68v74r3H3nuv41691+emUFtvZfFHZViAK5dv0u9vGG3GtBrheXoN0Ckxot/+ILe0VZbO46XJP2B9A9R843OxkZul112mXrnO9+psPwj4QWyefNms+P9Sy+9ZOII+hkrfrHwldwO17395V08tx+02v6I4V+2AmgYtXyqWb7IN6oxJyxe6jLb8orq0DsGHRgcXfyf1jj6PS+jw4/+uuF3u6XLHwCo6jJq1WvL1bPPPqteffVV1aZj3Mo75FBd4k5Jm4Bwr3qqW2ar1de4pSfNN3YlMKRjsyTxGLtPJOWsWhKwpkPVqtJfPQAkBJFmEw42RyiXxIWEPBK/r1x+P9dwS4EeNibBeqtWiVVyVs8xyw+6Ml6JB1sJtJWASvfF9brwUy1eUAS3bdumWlpGg+cD4BLHjfYk9PiRl9xbLV780FjpnrHAgyiuXhV+YmHRNmSjI2RF28AShrYh5VaSYZDrUodX2qXOHa1l3KUKzc3kpoLP3766Qd38nnPVjCmV3SznLFqrWrTrrdIx4tykwSka4NFZTe6RW55bs10d7OxWh0/TF32m36/aqHp0rLCSSXivQGa9LqKxU1slTauQsWRFHi9INVgeazxPfkopp//pker1+cWbaNW1FSOFbdoNe2tLm9tHIVUUfdZl9OYfGozMJb1EGcT9l3LaK+wC/I07fm/wxUEd+0veAVM/ArFI4VydfkY92kLoU997Qj36tRvz85ubxt4fv/1BUEkwHrApCAdhMtCz0P0AAmVDNkJlYA2N9aC9SFyu7mXbd5rnSB4AwMOmTHTMfqijR3Vpvco0Dp2jTr8npPFkASj6hIzNRgAx/FOX3ZpH1daC+H8T63nGo3HE8zIX/OjKDrsK+wYAdXn1Tb06xuQE015ps4zlbOJGW7W9mQqqTsRPcQGmr4x7u0iEQFMiUwmkEkglUAMJJMYCENlgBUPatGmTAbzMD4c/7CYnCZe5qFLeZCGqSlyUK1aOqQWgC2FZWWT1MkprRiZPTFZwYVqxYkUO/GOy8va3v12dd955ocRyE0VMFHaLzcR8HY88sPvlK6+8opYtW5YD/3ifpW0cffTRVQMZpD/zM+FfvW2vGihES6yWJ+5z1inHrxmd8auPznO8VnjyyZc2ugb/MN7JNuXm8gYIlPL+4b458tXX548Xrhy+rwAsksKE9xKXJZshblox3jZ6PaJvjXoz1IYCg7Y3HzlD7VuyuwgAO+64merAulG3SCFp4ebtw+6/RTCi5HD3Sew/G4jE6i7IBiDU2tE1Gj+4kIq5T61WezSgCXyb1daGlRI5iAe4u71Tfeq/Hq2UfUxcl/5A+odaMEX4g5NOOkldeuml6s/+7M/MmAk9xGEmRuoLL7ygFi9ebHYWFoDCic6dLYfUoYH+nAVgX5mdYecsXDPaYejC5D1OYwA6SbbG5wosAO34f1BW7zZIrM7bMTgcN7zVpwsw9Z1z/hnqjDPOMPEt+Y1uvmHDBuPGji6IW3tSdTWhW/Rn+AsjST8TRllpGakEUgmkEkglUF4CibEAhA1iwyxYsMAMpgyixItxSrZL7kUXXeSUJfC5/fv35ybsrOrVMrG6iHVZCgB6ewpRAk4oM7QRJif2DtYzZ840Vl2zZs0KFdiJkhdvUvWfWyaYSVYE3fLA+7p161YT8F4kNn36dNM2sCyWcuRaNT6lTj/yn/Pi6KKLI60V0a/Ru5bs2Km27GtRJxz1ptGTBd9a9KYNm/eMWtIWXC76Sey/Uun1lnb1yrbd6szmY0tlKXl+V1u72qHvl+QIIQnvjhflzuHPRo1V1WkLuKEJLjLn3+r9F1XoOGgTR8k3Zcw6TLvLbepUA73AYvnp2BmT1QG9eUJh2j2oXXV1mC3AMd9J95mNPSN+lroQ4gmShlxaeA7nLv7bqd2TiS3ZoC3K7JTVvP/wp8+bU2z+UbRJDfKRZ2fdCBCEJeCqHXvVvz7wtPq3j1xpXR27X6V/qCWH0IBLMAexoNk4CwsrwsKwmMLBAjChErAKLIzn/IvnX9Hka5B5pGn3lHEBXvDylnxWR9pCagGYL5Y4/KrLbssjo9ACcKicb3/enUoDxD3akrlJHfTrAqzLG6rv1t5HZxgPJNom1qsctFn0Qg4/1qsFpNbkpyyYhw0Awgy6B+94HPqamgg3rTSVQCqBVAJVkkCiAMBrr71WffOb3zSi+clPfuIIALI69eCDD5o8gC2XX355JKK85557cnE9WJWuZQIAJKUuwN6eQhSgGe1v3759BvizAVlAHVx9aZNRpCh4iYLOcmWOBR5EcXUC0TgnwB+fkpikNjc3mx285X65Vs1PqduJ9kp0rNi6q2wWsZ4pm0kuauDly48+q37x9x+QM0WfDz61QnVn9CzeJTiU0ZgW83cwHZPki/6Ets//9En17FdukquuP+98aonrvK4y6snPYTuGVPuJrnIHztSovXnt+IwTmxrU0R31avf+YjffJn3t4NricBdAdl1N9QoX5iCJ++stbBHgL6j7r9DTqXcpnjEtHwX+3j3Patft4QozEwrcf+XGEp86zJcGJofUUy9vUn96+Az1d+9zXowscXuiTovFj/QPcSGe8Cf0m4yrAH8Agbt37za7sPOdA2tqgEAWack/f/02QrTl+oG+MgDgpt35CwzynrB5yHhJ8uxlbI4l39kDug/Pd+/d0jc9j9QD2p3bbdJb3KlZuq8IYgHYnx0Nh0EbJL4zm7tJyCB0RLFeZZGYxT/aKcA1wGCcU5QAYJz5TmkboxKgO09al540esdo00k6W/lL4jHnBnfJSy65xFDJzru4fBSm22+/Xa1du9acvuWWW4oG0+eeey63wvTRj3608HYTh2vlyhGXrqKrwyfmzJmjvv71r5sfuKbcdJP3iWOJon2drpYLMEqgTAJECfBFcExukhXMMHhBUWaFd8mSJWZnXwH/jjzySHXuueeqM888MzLwD3GKgi4Ke0xE7IkMaVt+AChPFUWY2ek5wA9u4LiA07cI+AcYTJD7s88+28QTFf4jJK9s0VK/V/mTf29HMWCUV5lHhWVza5tavPH1vCLsH8s2aCDKLfg3Mp/S2Nposr/rsz2DGXXfH5eNXnfxDauyxZtH4tGW4S8HfhbUWaoKduPFMq8aqaHAO/a0KTPV7k2jO/7aNJxy4lFq57pioLf/uOlqUMu4ngCLAVKDZf0nxYQFABZuBNJ2qEvNmfeaqQbaHQ2EyrDDpWFrxyF13zPL1Vx2wB5DaeGm4ndP+oe4sQld9KVve9vbzIIvn7LQhvUVFoHz5s1TS15apnZ3dRoAUHjoLeECvHv/Id0nFJizjrySGd1XjJck+oSMa3Hk22kH4K3WDsDQvKezwvhUwNisaRP0DvGNqqM3f9GgIFvJnzYAKJmQIfF8zzrrLHXZZZeZjW5EdycG8Jo1a0w7ffnllw1Q6HUclnqi/hR9WfTnsOuLaz8TNp9peakEUgmkEqilBBJlAYig7rrrLoVbL0Ghr7zySvWlL33JKH38fuihhxSWeaRTTjlF3Xrrrea7lz8E4sdqkN1Yr776agPcMGiTtmzZon7961+bQwbn//zP/zTBfb3UEXZesQAU0Cns8u3yGPSJsSNKgH0tad9FqRUl1w/9yAHg7/XXXzfuHZSBAkPsNiwT5Nn4KdvLPWHw4qW+KPIKD7xbHElUBIVm4QHgjz4FBV8S7t/Nzc2Kzzglm3YvdD2/eptih91yKQeClctkX9Pl/a85C9Tcz3/IPmu+L1uvLX0O6d0564suOZ4wm3/oybt+LXMpD17T56HvxwtfVjddcXYOTM9lLvHlZ4tWqUENAkqyipdTw595leVfcvylCZ26c0h1He94NbyTmi5bhH923NFq4/PbS5Y/sbcvZzllZ+o/7jBtDafPBMFF9Ptu7/6LyLAG1Jt5hpLau/ORzv//P36XW/TvPwyXM+/V0GbYMHRogm6rv5ynjnnTYeqcU/7Ue0ExugNQ+/+566eqQ5tjLrrl44YyGR+lf4gRuUWksCEallQchN7AChA3Yaytnlix0cT+bLAsVUvtAvzb+RocLmgU0oelFoBFYq/picINQPb2T1ZdBHy1UqexFrdOVPg6dTIdmlItOg7gYZPy+44Kt5rLA9nR8d4p/8SJE1Vz87D1KroBOiTtFN16z5495mCzEDYO4Zgyxf8mVU71Bzknun9UAGAQ2tJ7UwmkEkglkErAnQQSBwCyevbwww+rG264wUyqAQALE+Df3LlzXe8KV3g/v7EudLIwlLwMyHfeeaf65Cc/Kadq9ikgUzUBQJkU1IzpECq2ASf4kd9uimZCIW5GfCdxPy4cb37zmxWWodVMQnuSn4vwgNzgI4kKpkySmXyysYcd/xE3cJR+XH7jmIR2WdxwS+PTemJdMXkFwXSBB/p61MNLXlXXX/C2vOJ/9dxqNSCz8bwrxT+II5dllCus3wHw0RiU+tTdv1X33nxtcUEOZ3750rAFmcMl51N12qVVE2KDbs4ZlZqkjfAiBwCpfEQuZx5/tNowb7sjwEe2o46arrYs2cTXvMTtg4dNlGLyrnn50aDjHtqP1Fj+8YzqHR6Ul4JH8rZ3De/syc+163erlzfuNvgO8G22cdSyfSS76w+9IbDK6p2LcVf+3N1z1M9vu169+eh4AftumWE37Gu+/3PVmRlQuMxv11a4b5k1GrJC+ge35dU6HxttsQkc7pe4XP5w5e8NSbgAS1qzboM66ajJxjLLHn8WvbZdsgx/0jmMvCtpDMB80dT8V2ZrHglb+w7L+82Pwcas7tvc9yUTdEgAEjsBv+VNB8x3L3/6KwCAUhbvFPoAh7RT9EoWDtntevPmzeZgsRBQm4VlQO5apmoAgEnra2r5PNK6UwmkEkgl4EcCtR1J/FCs78Eyb9WqVcYaEKCPAZP4LuwSd91116nPfOYzvlfMzjnnHPWzn/3MgH9M4IkpQ9wOVuYYhNnZ6z3veY/6xCc+YZRGnyyEepsAgFHHAIRoUZJFCQiVkSoXZgNMbgFAgjjv2LHDtDmRAeWwSnv88ccrVnZrkeS5JBkAtJU+ryBULWReWCc0yw6UrOJLImA9wB9xfuKcRP5eZb96x77I2PrB88vUB847PbeBw4B2v9vwhp6QuZzLYf1nYncV5nf6rSf4r+45oLbtaVXNx5QHcV59Y68OEu8yrtQIcOBFSExWJ+0fUr1HFhLqpZQKeSla03bSMW9SOxbsKCvSNx81Ta1q09sFF6S+Y6apzKQ61YhVVQBSG3oKhKTLCsv9F5LtnYC/ccfvcsZdA9OC0U3Zxgqwfkjvgp1VH739V+qxr35Yxxus7gIQdARJq3ftUR+773Hdf2VVZgQ/eX7rdvVhDQDKmCL9Q5B6anEvYyPAya4edtjROoxlAdjSdkjhcon+SJxAQBbcMl/fPxqj1dCsm6c073QX4Fo8xdJ11hXuAFzg/tugXXnrPC4kNDYN90cAgH6SkwtwpXLQI1lA5sCjCatADr63traaAzdhrqNv4uZei3fS1nsr8ZReTyUQfwnQs0vvHn9qhylMGr1Jkev4ojORACCPCPfKO+64wxxeHhmxN8pNcFk1/tCHPmQOL+XWMq/EEamWBSC8ihJQS76D1i2gGeXIJKdUmazG4uaLm4bkFXcjgL9aB24WXoS2UnzE+bzwAI1J4oP+ZO/evWbjFxuEJ/4jwB99ShKSTCbK9Y+FfBBEv0W/G1HpTz3ZQXXH7xerL1x1kan6oWdeGa7PRfw/rLvM7r96wl/nUl8i2yfv/Y16+isfNfWV+vPdPy4dvVSAXY1eGP5mW7YZOVXIL/dP0Rhy75HyK4JPzSzB7rtWtaiMBn5KpYaGetW5zdkKpv9PZhj337pKPuClCue8jnfY0JcvFNx/B6a6fGjlyh651t49bAE49+lVak/baDywwUllrP9cVk824gFmNWjQrRcKb/j2w+rRf/2wamocdiN0QV5Nszz+8lr19cfnGTA4y/rViInq/C0aADz7zBxt0j/kTiToy7OrNqtBXkTdV9sWgJOmTDMgCgt7hGrg6BxoUv1D+e+D/Q6nAGC8HnyhC3DhBiCDfS5fZIutbMPw8/e7EUglF2CrKseveJBg0HDiiSeqlpYWAwSiY6B3i+cJC/8AgQDXuAtXK4nuby+gV6vutJ5UAqkEUgmkEghHAokFAMNhf2yUIhaAKQDo7XnaCowoNYUlAOiwSxsWXQKMAPbh5ovyVWt3DKFXwLMkAWdCu3wKD/xOAh/Q6AT8QT9K+Vvf+la+JibJBF/auRvCf79sgyvwz55AuynXzvPY6nXqU5efo6ZPmaSeX7XNuFva10t9x43RuO05+NyW26+iY3BA/fSPK9WH33uWY9Fdff1qtbYAtFPZKWY+tmXfVvY7lkqNndqqdFrZ0suWUe4iZE1pyaju9vIxrk4+8Ui1dc6KoqK4PzNtosLNGldYv6mhV4My1s1ZjZvx27huW+eDfGUTkKwGGn/44IJcMRiC4bpr15276PELVqZmZ2CtUe3v6lYf+89fqQf/6fqaWOh4If3bTy1QDy1aPYxLa0FkLAP2ZW/sUp26rUt/IP2Dl/LjkveRpcPu+vYOwNB25FHHqMsuPcMs7AGsoEMtWLWzmGzrHU5dgIvFU7MzQxrYz+7Oq35rb/6C20C/vpwfEjAvv9OPAcx6daqmBaATHbxzhA7hwLsAjySsAtlIjLa6YcMGc7DYiD5KvHJbj3IqM+g50ZVt/Tlomdwv/Qzfk9zXQH+aUgmkEkglEHcJpABg3J+QC/oEALStj1zc5iuLKBeiBPgqJCY3CS+QUwg4sXsgwB8AjyTce7E8xQUjbOVH6vD7KbwU8uG3vFrcJzxQt60M1oKWcnUiYwBhrEWwDJWEm1lfX59RzmttESo0efkUpduL7Oet3uqlCl95MxrF+9fHnlO3Xnmh2tVSPri6XQEAoKP7L5mcUB/O6Yk+kNAPn1+uPnDJ29TkicUzx//zzFKDK9p1uf4+Uoer/HryN23HkGo7zVVu75k0Le27OyvOjfUWH2rIYVfi/qOm6lhxdaqBubKTPF1S1Nibjx4CygEqujbbdFEPMQC/+8NnVc/AaAC4/pkBiHaos04jikPaFZi4hRv3tajb7vmd+s6nrnLIWftTvOMf/+nj6uXNOhbiCDkG/JMf+hwb3CzavkMdofOSpH8wPxL2Z9Wu4XHcdv+FBTYBYVyfPXu2am5uNn33Pc88UsSd6UdGzo5HC8DYPvvsdt1+R/uPQb2y83p/PgCYcdziu+gR553oyYIaAgBOzTvv9kd/tsNtVtf5WGzG24QD/VQ2DkHn2L9/vznQO1h8BAz8v+y9CZQkR3kuGt3V+zL7om2kHmkEGiGQ/dgMti+Iy+Fem2c/42Obhy9w4WDwMWCDDwd42Dxb188cY7ZruF7YZcxy2SWQhIwMQvsyo2U0I2n27p597W16rf39X1T9WZFZkZmRWZnVmT0ZUk9lZUb88ccfUZl/fPkvcYUcYd0/aTqwsSCzipkEMglkEsgkIDIAcAUsAnYBbgcAyA/9NANNPOUq4MTjQUY2ADuI+8gF7hgA/i655JLY365yn0E/eSw8jqDtk1Bf3WQkcRzgCW/gsT6gdKOAZ6wLrA8kBtqzZ488n0T+JWMe/7D8gwCA+081ficepFu+9MjR4+J/3fawgHWeMHD/RfbYKp5utI+jKQpc4NH6/s/fIb7wvjdYbWGBgU3XHU+T1SOXGjbC35o/JaDYOO1ledio1TjqIny5kxJkVHpCDKJBRn9EJFVgQ1dp/fpBcfypI7pLIn/FagFrvc6gg1KodZTI/bdmbGOdhbVouTva8Z6fnBOP3XfQ6gMHFQr0z2vedoG/BGQB1aUrcA8mvUM8sG9c/M/vPyD+/Pd+nSkm4nOGYuG98YvfFWdJJlwwhRXKZuws942Oi9/dWEsE4ikrZ8MEfd93/JxYoMQmmBPV/RcsqlmAMT7EVTu/UAN/bEOgKeVy+PCo2H71GrFu3Trv9cMNUvzJzzHWL5I2lA5HApDj+SFRcgB+5Q6NCbjPQC6UEN+1S8wsDhIQ3im6Apo4FyszPj20dhk6P5KGXHvttVJXxXMJiW6QkA4vrvEHABBAIF5YI8ZlVCVuADCt95mo5JvRabMEcG9X7u9t7j1cd2njN9wos1YxSyADAGMWcDvIswUg3grGXRgAZCUg7v7ipK8qtXCpOHTokAy0zH1CyQKwA7eKpCslPBZW2HkMafrkMYDnJI0Dax2xHxEDUgX+oFhjfagZn3mdBAHRkjJHQXmfnF2geFnYWMc/AgAUj5w6aQT+gRsk/xBk7OW693PjGedJucLHnskJ8eT+E+LGbZfIuEuY/6dOTYl8uWFxQtVk3QKhVj06IKxVRY1AiaGjVXFhG3qKuGCQGss+tZetl60Vux/ar56yjisUP1ACgPClDVng/qsWiBAgTTliwHP3rqM2Hb+E/TDG71HsnHlUVC4BvJSWgDAcpbn7zkN7xOXrV4k/uKkRS0+p3vbD/ZTk5u23/JAsl+2T5rT+Y8YeGDsq/i/iH4XvD3wtLZ/ffOApORfgt8kCsGiXw87njtJto3nm1RAGk5QdeefOnfK+j6QhAFnaGYOtnXLn57D6bG5n/359OROAjGoyAFd092UfwjOlBTLkXUW3xw4xvTAoNgwFs+iDBSB0gLh/M5gX6Kf4QxxL6CkAA2dnZwVeZuNv3759MgkO1ikSkrXKE+v+vBfwEWWgy63yFqizrHImgUwCmQQuYglkAOAKmHwGANtpAchKQNrFB4UDitrBgw3rELw5HRkZkXFX0qKQsILOCnsa50WVdRIANKxxKNMAfqBco0DODPzpNn08hiTwH3QNBOX99kf30cbasBfUA24W3BjD6iBPSRa6DeLNwX0U1kwyjJNbf8QPPBvp5+9aAAN88Os/FX/9um1WduefHZvS1i+TkVSZLvmmffDoT0uYTvZg7wmgLmAmSzd66nkV2FDP47iT+iuc1VuyFDb0i1Ivuf/a8RMnCe/vNAFO999KF1lp0VDhBhxZISvDudm8benlV7ktjNZ7RQKTMlyBMQb6/3/++CFx2YZV4tdeuLV14i1QuOvZg+KjP/iZqJaxshsFBlM66z/UmKQMpIcvzIm1dMz3h0bLdBw9OnrcYhTgrFqW8vQCQyl3PUT3NE3pwM2iXvoHam6hyM4KvQF/iMEGMBCf/Czm+mn+ZH0iqWNyJgAZc2QAhuztr2vMZqNM8712sFdMzOWlG3BQABC9FquzoodAxHYVWPlBb8VLSQB/0F3gsQCrQIQswR90FnYR5n1DUP5Y948DAGRe0nqvYf6zz0wCmQQyCSRdAhkAmPQZMuCPXYDbkQSEFUFWDA3YS1wVgDNwl4CbhArUrF27VipP+EybArIS5kWV+XKuL3b1BPAH5RkF8sUbdCR/Qcwot5LmeWD5q78Jt3Hi/IN7j3hdbr7W2EM3XzM5Q2AKsvp2LnhXLsL6j+qiO/oIXdB2gbJB/ujxE+K3X0yZFtdtEGcXRxv06uMpEzBZ7arW4uHBc0wpXgCbUs37kFDKweNVMX+ld7WgV/vPVDwBvGuu3ihGf/6Mlmx+yxpRJSs3guu0101Owl0WYJmtELmo3X+7yMpQhfsACFQBNLbAu41nzRfpCgwQkOYOBkgfvuXfxS1//nvieVviTOusYaR+6h/ueUT8231kCacpbtZ/XHXXxJS4adVA6p6J4H/iwryYyJMfPZYpPfedLu+qCzDq7zpMVsa6oqBIV101Iv7Ty0YkwILEIWoMNoAweE4ADAwLsOi6X65z/Bzm59py8eHab3ncdmks3wy4Vek3GKasHuqpAYALQ2Gai0LlgujpbOYnFLEAjfAcX716tfyDmzB0XYCBCGuDmMWjo6PyD3ou1ipCmARJZtcOADDAcLOqmQRakwD0uLou1xqhNrZOG79tFE3WlbkEMgDQXFaJrcmKZjsAQH7rx0pAYoWiYQzKrFvW1q1btwr8pbWwgs4KexrHAcUV48AYlmMcAP6OHTsm/3CMAn6wmQPwZxJHJyiIlqR5Csr72LnpYOxjE+1rIudNEpZKFQq/2AQc1ZuhiwqBhJ7uv9yF275QggW1Sjh8dGJevHfb9eJLjzzNLa1PXJcx5+mgQrhwlQBAnLOKU1GzXbRq+R70TYpIAcBOAsU27tTEOlM4WdvTJY4s6uuUh2vuvzkngKe09zvMOZN/UAPMa4kSi0RVEGOwq2ifhCL28zgVXTdN7IK0BAEJHAYICLfSd33uh+IHH32zWL86XGKBpk4MTgDMf/f/vkM8uv+YdrjSWrY5z42N8tOTM6kFAL/14G5rnmH955zyPCUB4YLkHucorEFTJaqgAvnIAox4r4i/tm3bNgmsAAgE0AJL8bGxMfkHgAXPDgAsrDdxX2n55Ocw6xdJ47vJBdiRARj8VgDE6ybVZzDD/bXt0dR8OACwWCbr6a4rfHqJ9zLWHTwW8AeLVXYRhrfQ1NSU/Nu7d69co1iriIHJeoAbZ6z7p3VNu40rO59JIJNAJoGLSQIZALgCZpsBwMwFWD+ZUFjgCgGLP47hhpqImzIzMyPP6dw59dSSeZYVdFbYk8mlP1esfJpaoflT9K8BKz8Af9jEMfAH5RYKMTLumQB/3Mty8M99t/oZhPfx01NiqRoM/ZGuna0ySe1LlN23m8KdOjfzII3MvzD36gBupZp94aKzgIAdG3LWkN8r9JT8y6/+TBzrcpj3yeZk/UdJHyRAQP2VqW5XA1Nooh8iHJXkAdZqfeeqYmmjbtRatt1PEkq68fEiJe9wr7J6db+Y2K+3hiqs6ycrPUqgAbQ1bCFgCpZ5aoGcEWsfCVwiKehjoZnJ4mCLKLQhc5AP4hnKZDTUZomeQ//t778tbvvrt4o+TXZpQ7LG1ebzBfHGL31XnDx3QftbASFY1LperPd0fGFRTBVLvsBAvXqiPu557rDFj+6lwVLdwhuV7tl5UFprWg3UA2WpqlmAcc+E2y/+oFvA0grPESfAAgAGzxOEF+H7rEo+qcesT7B+kSg+K2dp6c5bLC2Uc+K0NP+2TskwD3BxD3PX7O2tPUAmQwKAsABMUkGs4muuuUZcffXVEvjDWoVbMPRjHOMPwDasAvHnphO3AwBM028kSXOc8ZJJIJNAJgFTCWQAoKmkElxPBQChsMWprPFbP1YCEiwWCeZAqQG4wzHcoFioWVsfe+wxqbizopvk8XjxxnO+EsaBtdWOcWBNMPDH6xmuMAz8dXcHRyJ4HtoJYHqtiyDXVKUb/KvfnXR+9Nhe5ynf71EBgABUKjQ1zuyx2KNLAJAOcOy36fOso4CDOBwrzRE40EwRyUYA6nXIf+rWh0oupuYWvmJyrTBwikCkCDxIh0dLon+qGRhTO756yzrxzPcf08owv4Wy/5L8W8n+m8vXQVO1U4o5CIu0qGIdInuy00IR2CymiuBLtefYjmVCECCt9fiN00t58ZZPfEd896P/zfP31SpDh89Piv/+5R+IhUV7jDuVLhK4wI3bpOwly7hXaNa/SdvlqlOk58gxioXGU+2M/we+VBfgn+046MqqagGoAoBqA4SGALiydetWCbAACATAolqWDw8Py+cL4rCFeb6o/bXjmJ/D/FxrR5+mfTgzANfcf+2/63LRJ9O3R2ddsN6lMhnaBVgfP9Wjy7ZcwnMdGazxt337drlGsVaRCA/ANce1RMIQ6EJ4Ua7OP+tKvBeIiuk06kxRjT2jk0kgk0AmgXZLIAMA2y3xGPrjGIB4gMLMnwHBGLqyFAFWDOPoo1WaOosuKDBQuuHKqb7ZZCWGlZpW+16u9qygJXleTGTD44hTGQTwh/h+AId53gH8wdoPf0Hi4TjHxKBZGueBeceY/ADAHQePO4fu/722n/KvZ1ADVoBwsVS3ewClpLUVoTyu2X9V2mhszBNVRl3usN6uNEhAFmjiMt1/kcSiQseWdZ2TPrdHm4AFFkzdF6qiuCo8ke6Zqli3VzVR1DPRMbtoDVWtUcmRiz7Fg6uZPIbnI7foFAyRhPUfJRaJpFDSlC5NH4W1cUb+a+Yco5GuwGQlSoifrHBs6oJ4z+duE//8vjc0N4jgzD37R8WHvvNTUXEk+3CSltZ/zpMu35+bm48VsHTptqXTP3rsOflbZCLODMA4r7oAP3vkNFdt+lStXd0AQG7kBFjgfQCABYkZkJ0VLpf79++XmVkBsACIUe+9TCcJn/wc4+dyEnhiHjoq43woP7UJQErhrX2rFP8VJawFYLGSTABQDqr+D7/wxDpECCGsU7gJw5oVMQPxB6CaE4cAwGadiXVnlV52nEkgdRKAKhLWNWO5BtusPi0XJ1m/KZZABgCmePKYdRXwwxs89TvXieqTH/qsBERFNwo6UFpg0aUCO+AX7gxuMdxYsWVFNwo+loPGShkHb4TimA+sDwb+mD6UW6wNrJFWgD+ec+Y/TgCT+4r6k3kHXT/+T0wjNW2wolrRBGupqQ13W4q510XxALnAGg9FbtZr3lu1E27/+mFNuF5XtGRV2g9W/4ySUgAAQABJREFUlf0kOf8S4Ef9qfTBF8Up7GS+olTUCEBCMpDp69UOzY8RD2/TjrydX01zJP84uvOQ7UqFXH4LV20QJXINrlB2W1iP1ffHtnpGXwicy5F1nlpg+Qf330KPTZpqlUDHsDC0QFilZQVufeg6mm4Uyu6HWPfSElCxtnvyyCnxt9/4ufjom/+ze8MQVz5//07xxXt2WuvWjQTWLbsmu9VRzx+aXxJ5sqhLU7n9yf02dnUuwIuFmoXkIrlLTy3Sj9ZtXSjLtUxeFqaFny94xgAAZIAFVoEABvFn4nZp2l/U9fg5yfpF1PRbolceszUfyw/bvuNLuUwTavIsaGopRLFuMrpY7BWLhW7R3+NuTatpTklAgj8jdXTadQ77BiQNQWzLiYkJuVYR1xIv1BE+B38AALnwXoC/R/Gp6iBR0MtoZBLIJJBJIJOAXgIZAKiXS6rOqoAf3uIhHk1chR/6SQIAYfUIYAfKNCusbNGFN5terjas2CZpPGHmjseB8ftZb4Wh36426jii6hOZ76C8qusDcf0Y+OM1HUV/rMDyOoyCZrtoMO/ozwsAfIoyZZYYGQvCnLKJDtLMrS4smHIU6w8Ai0xmQMAb2Kp747o1C38e+35sJusgQZETPKrjomtIBiITleh6cgMYdHU15wB4dhK4VQlqKUc8rttTEt2UENWvrB/oEcfO1Tavld4uCfxVhmhQBEAie26FrNly0lfXj5L+OmL/OcUAuggpCWCq5UJAZ84RXxA0S7Q+MFWddUs8v35gK0h307D4gY28jAUIRJLAUy53PLFfXLFhtXjbf30Jnwr9id/rn3/vLnHfs+NNstURDWL9h/Ylov/0uQnxPHJxTUvZf+58g1UCnVUrPr7ALsA/eZBCGjSmhi/XPmns6qVSyRwAVAkh/t/1118vQRYkIwMYODk5abldHjp0SMDtElbo+ORnoUqj3cf8HEgCL86xNycA4Rtyo2ZFvllofA9ytFjltzhCTJEbcH/PVJDmBAAm3wJQNyDMNce1hLcEJw6B9Sr+uBw4cEBcddVVcq2qugNfb+Uzanqt8JK1zSSQSSCTwEqUQBTq9kqUS6rGxC7AYDruRCCsCCYB4ADYCWAHyjQrqgzswGXBxKKLwZ8kjKeVRcfzAhqQRVoVKB4Hz2crMgEwzMAf00OcJgB/WB8896304WwbJf9O2nF/N10zd+44EDcrZvRpVw4gA7k5ZOx37NLh/qvu1j0oBfX6kGQJpGIrwHJ/DflzOpXiuuWKXKticeH4ap03PqDBDR+tiplrjVvIiv2nK2L4uL8F1xABfXPHJ0SFQMD8letFlT5VgUqgzlC+bhzmFjUACglGJv8wnTw34nQeAKPO6Ce/muKBYQJa5N+ja9dL6FK6AgME5DHSyc//dIe4bP0q8bqXPs+1rd+FRdqk/+FXvi+OnJ42Gpq0/lMsWf3o8/Udp86K3+cvCf98eP8RUSD4lovO+g/X2AX43icPc9XmT8ePNogFYDMxwoDJKwHPH/xBh4HHAv5goX7u3Dn5h+cUJ2NQX/Dq6MV5jvUifq7F2VdQ2h2VMVuTWgxA2ylRCXqTV5rPlvC2pPZDgRvwZWuCAYBpcAFWhqs9hD49MjIi/2DBCn0KaxWFXYSjXqumeoiW4exkJoFMApkEMgkYSSADAI3ElOxKcCHhMjenRKDnkxF+MmgCi7nlAprwFhKKCNwTuCCuH95GIsEH88jXvD5ZsWVF16tukq/xOMAjxqJ+TzLfTt5Y+WtlPgCCY30gALsK/EGRRTbGOGXD/HO/zvEl+TvzDh69+N81TtkoQpRIXYDr/cPdVpChRgXZTKkYu//KyrKJ9z9AbhQAAGMgfJ2SYJADsA5lqtMFXzmE2lPayo5Ar8WCDMi0s4UpmxGlTgLENj6BtMj+ZeOmYbFn/KyoXndpA6iqN8NeWrr/ktagc6/1p04kYZ3nCEEIEQGgKfaZjcerH0m/6BQ6gYvUqNLdIfvxah/nNYyug7wIq0gu0FEfK33c/L9/JjavGxI3XnNZ4O6PT82IP/zS98TcvNn8QjKl+m8laGc7TtVetKn3iaA02lX/ew8/Y+tKlwAEFdgC8MBxxVrQ1pKmqoEjyithLQAdZOVXgHvPe97zxLZt2ySgAqtAgIAAA0dHR+UfYgTCk2Hz5s2BdBtdf0HP8XM4zmdmUJ5k/SqBc5XGc2iC3HQvyIeBnVorAOBUcYEA9SG6hXeESgSStCzAdskE/wYL1q1bt1oAINYlLFjVtbp27VoJXEMPN3kBH5yLrEUmgWglAH0Of2kqaeM3TbK9mHjNAMAVMNsAvACAwdUxbgtAFVyDcqh+j1uUMzMzYnx8XMYn4b4AfgL4g3IcRknlNivFBRhyYaWdZZSmT56PMGOANQXWByxCueB3AeAPCinT5mtxfPLm2AtAi6PfKGgy76Dlxj8C4J+hhAC0Jwpe4lCyiI/yILECMA6KHH0Pw1qQwQAQgPtvzfJPGRR3TJ/IropkIDnlcpA+POsSeDREsQDnrvSsVbtIvG7aWfAF7Gou1J3i4GmycukHqtpcAP6Ve+FKSQPjsTZX8zzTpbH+Q+IUxBMs94Qkyj0i8cdCRctacYhYpj4s4I3b+H2CpQjnEHEOEUKOrUjRPdh67z//WHz7I28Sl5NLsGl56PAR8f5v3SXKAVxSkSinbtRk2o1Vb2JxSewjt9rtm+ILMWJ11uLBk8dO2ijoEoCgQonuZ+emZsUcxTnTLhyq43xxUQb4HnHBswnZVvEHPQ5ulwADoc8BZMEfABVYDQIMBBjTjsLP4XY8OwONp3yEpqsxDzrrP9DT3w3MeipXK2LtYC8lASmESgSy0gBASE3Vk1/ykpcIuAizBSvW6tTUlPxDohvoXLBiBSio6hZm0s9qZRLIJJBJIJNAnBLIAMA4pdtG2nADbjcACGUgbgAQQASUCgA709PTlkQxXgA7iFXSinLB/LOia3WQsgNVQU/zWHgug4wBVq9YH6pFaH9/v1wfYYHhsNPP8xCE/7B9Rd2OZQ+6bgDgvbvHEpUwDVvAah2vklY+QTAk07pOEIjuSYiDJxGDxh4UYrMKrAMRC7CDsFJbMe3T1qj5S++kEHNb6LwPvdWHSqJv2oVJao5kHpUe+sfAmpDdf9V10syZxxmSmy42H8BbgGCuFpUeJNVLnWT5l4Opn6aUhmiM9SnTXG7rKWkFCFRJkXmRwIb//snviltvfqsY7qeF41NueeRJ8b9++mhtTD51+TKGHzT2H7fFJ1jeMXY88QDgkXNTYrZkB/QQg9Gt3PoLshb0+h1BcErxywKsVA11iJdWV1Osxa1bt0rgDwALrNmROASxjvEHABBAICzavWIch2Kg3gjPAH4O8HOtFXpRtnW6/44uNZJTqP204AEsyawe6gkNABYrF1RWVsQxA4B4BuAPa/Waa66R6xX6OUBrrFXUY2DQJMkNr7XQz5YVId1sEJkEMglkEmifBDIAsH2yjrUnuJIgJke7XIAxmDhBDigEGA9cORF7hMvq1aslsAP3gyiUBVZs4xwL8x7nJ48DfaR5LDwO3nh4yQyu4AD+4DLFBcrmyMiItAiNYn0wXdNP7tOEf1Oa7arHvKM/N/7/4yl7dtggvHntsYPQsdXF5rwWpikwwBN2cyizDdPmB1mAXcdEF8r0dJWeujaGA7PpaF37CuvDvnNVsbTJlQPRPV0Ra/Y7/G2pOUQGCzRT4A89QlYA6AACuveImu4FAB0s4JwFlnnS+o9kGrrA+m/RgdTUiUlPbOK9E5aLCSjgQsYDBIisjBlWaG/++LfFD//qLfRijRh2KR++9W5x965DgeehAqDcnay2tw09fWJdnuacLCtn5ipiaoySALxcWzUxJ79+364mQM/NAhBMP7xn3JP3JgtAshpsR8H9eP369fJv+/btllUgnnvQiZ577jmxb98+aWkFMDBqSytVj+DncjvGbdJHR1MGYL1FZFXGKgj/ux8egMmsCOkCnM4kIF7yZwAQ60HVF3CM9Yc/rFWAgAAA8fIeloEHDx6Uf0huA6tArxezKl0vXrJrmQQyCWQSyCQQXgIZABhedolqyYGi43YBVhVBVgaiFASUTlhyAfiDSycXAH5w9YWCEWXh8cQxlij59KPF40A9VXH3a5e06zwOrzFg8wPgDwAxF6x/WEy0ahHK9MJ+svLqBqCFpduOdsw7+nLj/9kTjbibgXnS4zOBydgaMKBBtGN1/8Uess5/cRUOaptKGxym1JGXCWQDWNjVSJwoAR9P4NA2OO8vA6cphtkmfR3Ewtv8WMEGEoFrAHiVbqBhwTbF0v1X7xmsZ0BzVgfQySSdhKcUKT5fKyVHmZHd4hIW1tIshcVs1DlthUFHW4BKMjOwQwM7Mzsv/ugz3xe3fPAPHC0o1CVZgL3lqz8Qh05M2Oa1qaLmBOa+7G9YKLppI//84fViFWVkmTq/II4fnRGn6vQ2DPeLPXsoAcD/rekgQaceOnjEzg2Bw04QT61w9FzDs0A9bx071g7chttdYOUH/Qd/CIUCSytktYdVINyF8YeXXwACAbAgMUOrRX0G83O5VZpRtXcCgKNLzQBgpUw/XiXrdpi++3prD5iphcEgYVdlV6XqPLUp0a3W8SMPw0hC2rCe7BXfD9ewDvHnTHLDiUOwnmG9ijqqO7uqgyRkyBkbK10CUjFK2SDBc1YyCbQogZXzZGpREGlvzgCgCprFMSZ2mQVtVgai6AfKJt4aAvhD9lYuAHSg9KpKAl+L4pPHoyq7UdBtNw1VQU/zWFgB1AFQuhiQcAUH8Ic3y9y23bJX++N5SOMcqPLTyX9+qSCmlijjRmtYjSqulo7JSFjG2gORwO6/slHw7iuEAiKOmokIYDEn4xOqACD3G4ECh8QZ3ReqFI/QwQ3RXr+rKLrqeSEkXAHgr4cYUizOgozecv81GrmGMtx/CaRzFkmXTPRkBmDnRcPvMvHHUjNtbl7uI2sVCMEhJr6+XJ9Ys9JCyQHG7j11Xnzky3eJv/uj37BYO31hVrzpi98TM7PITBq8wB1da/1HYuukn3Q3vWvrK3aKbkLRjxQbL1bUni6llyxHD03KlwPqvUKts9zHs4t5cWZxwTbXXtZ/QHXySBHj8bvowI1GKXG7ACtdaQ/hBYG/6667TupMAAPZ0urAgQPS0gp6E8AVPBf5maQl5nFSfYaFpeFBvqVLHZVxq32ZpudIvtkFuEzrudWSQ8IeKmWKlTC71C9W9zd0UxPacAPuza0zqZqKOqzzm64H7EuQ5Obaa6+VL2xhFYgYzUWydmZ39uHhYQlaI/4lQrdkJZNAJoFMApkE4pdABgDGL+O29AAgBCVuABCKP/4AELAy0MoAQQNvr6EMIJsYCuhDGQDwx+NqpQ+vtqzIqMquV/2kXlM3ZGkei24+EFtmbGxMbnJY/gCER0ZGpHuUOna+vlyfzIsOQFsunkz7Zd5RX8f/XTsP2DbWpnRjqwdAh0Ed7NP42LTDoPWJboEMkNnqz7c5VUCMvRI9ZbuaPXFNuXSvR/dJJAOZut5eZeBkRQydqtTi6pG1XxXWdVQ3bIFopftvC9Y0iP2ntcIi4hVoIQ4QLAivoO221afkoHJdqGs7CO3AayoAccyIzhUYJH7x7Kj4x1sfFu99wyvFjvHj4r3fuEOUiuEsz2AZa7P+o7XYQ4BfjrCMHELl1TAOWi8VJNTWFkzPqaPTIl8oifNT82IjZS1OYvnOQ7vlfKu8uWUARh0Aw1Wfdc3yYZplZHFJQMHLS1j74Q96H4BAACxIzAAvCvzBEhDXAQbCQjBIUfUIfi4HaR9bXQCy5XGL/MnCoCioWXXqVyplt7uC1dT/QMniNDk/FBgARCKQlQgA8otzfwHWauD+C1Aaf1ifsF7FWoU3B1za4cq+f/9+ccMNN4grr7zSlGxWL5NAJoFMApkEQkogAwBDCi5pzdgCMG4XYDzIoQwCuFMVxKDygOsKFNZjx47Jt4FoD9pwCwDw1643gazIRAFmBpVBlPV5XjAnrcxLlDyFocUbDcwHMh+Oj9uTv0QdAzIMj15tMA8oOgDNq10SrjHvbvzf98xY62wCbKiJqCVa2AMi064s2MQTzTBkQac+Zf78UAflAWUAOFQLGFDP4TvtQYuU2LVrQqnorKdcCnqYI8SmkyzrKr0gClCnKtY9VaCYegT8kdWf2+DyhN/kV3eK4WMl0UH3c68i3YYRM5C0BTc3W6/2uNZFIJ2zYM5gxVjqr/HuvG7yvQOJP+jPrRRW1a3/3Cos83mAS9ISkNcy80OL8hv37xLH87Pi7n2H7euK6xh+lsl1G2AfrPywXgB6BZX4ZUO94vxEzfrpxJnpxAKAd+851CQVrLGWigPvC5J1uaV+AzSG/vf85z9fWlohJi50K3zipero6Kj8QzxBgIGIv8Z6j1cXqh7Bz2Wv+m27Vj1D67dhiadz/wUvZQCAdN9qpZQU9Hhyflhs3dCIN2xCt1BZWXEAWU82WT9u8unp6ZE6PvR8AIAAAmEEAKtA6HdZySTQVglAEcFfmkra+E2TbC8iXjMAcIVMNgOAcVsAQlx4+EMRYGUgiAjx9g+gHx76AAFRoFxCMcWbvyhi1wThhxVbVdkN0j5JdTGWtAOADEJNTExI9yaW75o1a6SrLz65Dl9L0ievpzQCgKocdfwfOK2iWGptw2PoWMBqotK16riVzPAZhmbANmVyB4MlXG0YmgQgGnrSDZis0IAhMMzmDlcZylGtRkDR8NGqmLmW+CpUKO4f3VP7c6TPapihdgD+Fi7tkpaJSMpRGiC3Tx+vUsT/q4F/epoqO7rjDvLR6yw0j7pCACWy9soEILqGfudk4o+K63IC7lPpIXtNPGbCse7HQSTXAVCVgaw6LNEKNFc/3Xu4NdaJbD+F7eS1F5phSgLC5fjpGfFL26/gr6E/YU3Y2xOdCop71tgUxfNzzLWC4TTxit+nX0mqBaCObzx/APDhb2lpSepZAAMRVgXPVPwh/tpll10mrQLhfulWVJ2In2tuddt53hn/byzfHP8P/FSQ9KdFAHCx2rCJnaQ4gEHLSssEzDp/KwCgKkN4cuAPbsJYm7yPUetkx5kEMglkEsgkEL0EotO+ouctoxhAAuwqG7cFIFjihz8rAyZs4k003HwB/LFiiWDBAP62bNki8FZwOQortszTcvAQVZ9pHgs2b1AAOaMv3gajIOnL1q1bBYC/NBQGJ9O4nph3yNkJAJ6bmRPzAOwdm+vAc6IiYYEb1xoASqoqT65AVnzOPgOMp0DGCez+CzLqsZOs9Z3oA2QoEZjTM1c/G6BPi47HQTfRHT5cEauOEhjGCKWjPkJkLVxSA/4k38QDQLnSYE505ckK0OWNspQ1baJNgBJHl9ZX6f5rfVMOIBv6ky7AymnTQwCYABDdShHYBg1AXddudZf7vHQFBghIwC1+IuAdywR/rZQuAncNMC7PLlb35cS5k3mLlxNnW7dquuf+feKhZ8bF//vu/+rZd5CLdz15QJSdaB0RcLUAJADZCbpq+8OPQCnLkQRE6d74sK+vT1xzzTXi6quvltb0AAIRZxnPVsRaxh8sruAeDM8LZ2IH9RnGuoVx5y1WPHDknHjeVRu1VDoU919UGFvSg5gVl3ualqjLydkS3o7UfkFwAQ5aMgtAM4lhfSHRX1YyCWQSyCSQSaA9ElC2Ue3pMOslHgnwm7N2WQBiFKqC6DYqvHmGoomYHwwq4A00QD8onk6l041OXOfDgJlx8dIqXVbSTeal1b6iao81gcxwiPE3N8cICbmr0Rp50YtelDqXEAYbeK1HJad20GHe0ZeT/x8/sq91NILoYn/u2E8HHxoIsGVHq4AiEBYDhpC5t9KLih6QjO4SztFfUQUA6VSkhUCjgdMA/5qpAvibJ+APIbIA/DGLSGQiXWdp41Uc7CRwUtOYyMkkHRQATloANpP3P0O/766lhvWY2kBavsH11cVaUa3bdCyt//Q8c12Am6GtQ5kIC4y/x/SJbgAClnpqSV10cxm4axJ7Zz0RTOC2SoN19KLulKi9kMHpE6d9suYqbXWHZ85dEJ/50j2israbvADKpAPwj1lX2/zcrTufa6oM61M3Wdbi/zU1aTrhbF9G1okUFdzX4f6Lv+3bt0t3S4CBeN4isRb+EIPtkksukToZW9mreoT6bGjH0L9852PiE+/+P/VdVcZs50ddLAA1Ntq2diZfposLdM8cpLt/h5hcCAMAXjDpJjV1+KU/681RMa7qG+1ea1GNIaOTSSCTQCaBNEkgAwDTNFsevLYTAGSgiZUBHVtQLgH8IRA1P9zh3gs3X7ifRK1A6HgwOcdjUZVdk3ZJrJOmsWBNYG0gxp8KWiP2I0BjuIWkMR4MK6+85pO4Ttx4Au/4A+9O/h/ed9StWbDzUeydFZOmlgEeQ+5LiJ+vAFVuuBCAwgbMViMO6zkAcCUCu7qAo7g1NuRFV61CmW6r+bJF2g34Q1tMAQC9HFxj6VuZXIbLS6X6d5xrlJr7L5JsKEJvXPY9gvulLgtrmeSB+PoVJCgJUZBR2CseYYnIStCygVuF6KW9TZAwpkjejE7AKSwXsP4LJ91GjzkCf6fONNwgceU4xQAMW3Bf+bO/+K6Yo8y7FYpXuePZY+KVN46EJWdr9+xp8nV2FC/3X0dV/VfiV/5glKslCn+S1gJPi5GRERmDDfHXEI4FL2ehy8E7A3/QJfFyluMw83OhXWPOF4ri0bHj8hnEz1O1b9UCcJF+NEgCoivwAG61FKtlMdiTE3MUXiGMBeBKcwFmPTkp+nur85u1zySA20RUz9x2STOCW1u7WM36SbAEMgAwwZMThLWkuABDqQTwx66cGAMUSQT8xRtmBqmCjC3OuswPNiZQbvh7nH3GRZt5ZyUtrn5aoQs5nzlzRgJ/qrs6ssNhY8LWgEkeg9f4eQ4wTvzpNjBe7ZNyDbyrZWwi/KZfpSOtbtQTAY+rZNVURWbXegGbHS1oQ/ASM1H+isOQh9KRXTw1bnDZUU1eoPPoB+COTAaikKk1bP1fZDKFVV+pT2/xp/YgE2YSnifdTmGBR6U4RBDfFAGIijAxFNRtxf3XzfpPul4SOBgm/l9HiVx/NUlF5EDq/xTWEAxLOI06HvV60o4LBDDDTdzNFTsovxg75rfVsv3yjeLQ06dtZE6cmaFnJQGwITI33/zxO8TEzLyorK6pnnc/tj8SAHD3+CmxRGCNs9RiT2p+cLg/09r2LfQjcLZOmwWgboz4XeAFG/6uu+466RoMq8Dp6Wn5Qg4ZWbmgbjufZV//yRMiTzd6ZFvuonjTzqLGADxKQU3dLP2iAADR9xC9XAEAOLvUL4r05qLbK+6Ag9mV5gLMcbvjAgDTcr92THP2NZNAJoFMAqmTQAYApm7K9AyzBaDqRqmv2fpZfvirFoBTU1MS+EPmVi7gCcDfpk2bEgus8VjAcwYA8sxF/wnZMvAHCz8uWBsjIyOCAWxeP2kFAFUFtp2bJpZnq5+82VMBwNGTE6IA5C2CYgK2eXYDQzTekWO/H84wrdEF02qcaTqq0BYT4BpX1Vn5NTVST6Ah/ZUJnIMUaT8deSkSKLqwkVx9iTYsEL26wFhQJAAIDYAqV7sps3sfueuSZR0XgIpIJlKm+qHETCCLLvsv6GMdVIhoGHAR4J8fP+V+yv6L9eElCB6o12er7b1o168tUWzJCoXAjQr8A9lcBNZ/oFOelWaiOLRKoVgW56fmxKb1+thrVkXHwZ0/3SMefHqMLDMbQn1w15iYXyyIwf7WYgB/68GnHb3RV1p/kIO24Ieo8KGtQyd196tyOZp7oVuf7T6PMCyw+MMf9EcAgcjKioRtKHgW33fffTJeM2I2DwzAHDq+8uPH9krihZIGAKyS7kBZgLmMLdFbFU3BiyEAvI2VpqlkeKq/h6mQNSwlAtk0bO7Wu9IAQNb5Vb3ZUIy+1Vh34k/fBlmFTAKZBDIJZBIILYEMAAwtumQ1ZABQtaqKi0O2coIygMQNcONEHBkucN8E8Ldhw4bEW2DwWMB7WkEnljuPJUnjAC8IPI41gqyEXGANijXC65bPs/KnAlB8LQ2fzD94TeMYmH+V9x89ui860TfwpcA05aauDl6hsQR4/JCgwL00Nyg6kn/Ivpur1c7wXtFxHUAXwARYAeYmHBdb+ArgD/zxVtele6sHAITS8omECddc8MRTgliAOSQEqW+bAdRUKPOxBKb8CFs9NA5gfQWLT2eBLBD/r9hHRAOioR1I/EF/XgUykfJuNgjzaqa95t2TtonxSYgmv57+IUu6EOJ17QdurzX3btcqRhc2rxkSo6PntbzBCjAIAHj85JT47L/eSw9ZAhXJ2pQLwMT7nzwsfuNXt/OpUJ87xk80teskz2WCgZvO44S67rUV+KRmAaw0AJCHik+8iINFILKyHjhwQD63cR7P7sOHD8s/xBIEWIhMw6xzoE4UZZxeNp0pLMj7QlG6Wis3fHRACUDotYTVlVv8v0qJZj7gvcUi6jhQ89PBDTgIAJi5ADuEmX3NJJBJIJNAJoFESCADABMxDa0zwUBKOwFAWHQhZgwXBI8eGRmRmVujUr6Ydlyf6ptMfrsZV19x02VlPAkAIHiAFQHcwZEBGgVrgoE/NyuCJI0hzHwx/2gLGajrKwy9drfh360KAO481PiNt5ufpv4a2IHcBuq3902t3E8YECgNYsNpUBG9uFWl5rgkYwk2jKTRIlQpkjVhgcDEoNCRzLhLvMD6T46osZcm1KiD+OsU3QuwcSR+4f5Ln7wmgjLaRTHedAVxBeFFFzj+n0z8QclOdESVc4VVNeu/sHwrpGI5BPBXJVyjnKOjzmYXx1Y7Rey/KMoVq4bFjNBbOyEO4C9ff4VRNwDM3vfR71HUPypA3hzAzE8f3d8SAHhmelZMF+gZ41gYtfXnOGnEcaOSDsBOSxbgxiiCH+E5hhe5KIjdjKRtsAwEEIiXvvhDoi7EcwYYODwczBrUjaPP3/qotT6KlCDGWToqY7ZTbhmAywQARlW6uhr3saBxAAsV/e8nKt7aTSduF+B2jyfrL5NATdlJmRwat6SUMZ6xmyQJZABgkmajBV7YhTJOF2AAGgD9EKcNhZUBvBEG8JfGpA1OwKaFKVj2pjyW5QQAAaIC+Dt69KgN+Lv00kulxR8HFncTFo9BBaDc6ibxvAo6pHEMzD/zjs8TF2YjEzX2/2EKmlXVpxXtDTsi2OPBIs6rlMkCTlqTKZU8m7hdxHn6wxhKZKHWVcPEFapmhyrw59aVFyXV/Rf1nJZQAABzlBBEgjQE1Kiuz150m64RWIdEHdoCOdAFuBYHKZ2w/mvGBGwkcBlxBWXikTACslGL9guSfACArf0GCAQlENZnOIEZkElXIiDalesURw/XnvM6Jk6cblj8666r5/7i/7tNTC/Sgqc1AStTZ3lq/wlxdpJcitcFz7IKWt+4f5f8bdno1i1cbef4C10ziv+H+polvJItAFlE+GQ9Ai7C27ZtE9dcc40E/gAEQg8sFovyBR9e8kH3A0iIF3yoH6bgWfPI4WMw25SloAMAyQJQLa4WgGUiEtHvvwpz5XoJmgl4pQGAvCbS9mKT5y/7zCSQSSCTQCaBmgTCPakz6SVOAnFaAALUgRsnFD3VjRMZ5W688cbI3v4uh1AZcELfrNwsBx9R9MljWY5xYI3AGhTAH8cOAj+wEEDm574+MlkyKAxALccYDNjzrcL8oyKDaL6NElSB+Wfedx44QSCFZhccluewpNBOMZaSljkRbfC8hlLQuP+GFQe7pRbWCNEX0DCk2E8Wf2RkA4u/sMNGzD3eXFsJIpzzQaBfcShHIGBFVJCml76HKQD/dC3RHfbT0hIxSBIJaf3nZLaZM7hYo+j6rl1p779IRCDj+4EnQps5c3GZNK8eCmdWi7IWHU9RWf9tv2yjOPj0KVfGTDMB/+DHT4rH9x2vrSMCeMgkuokmTv985wHxpv/yfzRdMzlx776xpmpdJFvXXwpMMA3i/4Go7oUFklNcDIWfwaxX4NmAsC74wzMeL/oABuKlM0LA4G/v3r0CL/tgFQhQkJ8nJvL66cP7xFJnQ7Y6C0BRbsz1dKlHTCHrkaaUAQBGtLuRlrr1O8oUuQAHKcWKOVAehO5y1WUvmTgBwCBrZrnkkPWbSSCTQCaBtEsgokdk2sWQfv4ZAIS7JR7SUTygYeHH1lwM6uDhjL6g9OEzKteP5ZoBVm7RPys3y8VLq/3yWFhxb5WeSXusEQb+YBGAAj4QLBzAH9yHgpTlGEMQ/vzqMv+o18558OPL9Dor3wwA3vX4AdOm8darW4VwJwANQmJTTKL26YEUwRG20ksdKXASzrkCC3bKzd+oL1CTyUBoPMpet7lu/UwUwB8TZ+s/fO+kTLooOoCj0ksutHRdJhQJKeSuxcZGXnZU/wfAHzxfg7r/AlBk8Eyl5zwuDeQkwBjN4nBSN/uOkVcpp4UEfOlLp8bMVFpW+sQyNOutUQugrmKs1LgQ4qg8V7uXuzVFDEC/cnjsnPj8tx6q/VDpBwvrUrdy96PhAMAleuacJF1E+YnKLmpZovU/bqfVqxtPON+BG42jXAwuwBgyP7/UZxqLAi9/R0ZGpFU/gD8AgadOnZI6FI7xB68UAIF4CYj6fuWbP3/KVkVnAbhUOCAG69PqlgAERMpFApoj2t0UYFYraubKQV2Ay1XSx+kv1xFMD7IJIkFfWEeOYn+hDov1DfVcdpxJIJNAJoFMAvFJIKJHZHwMZpTNJMAuwKg9Pz9vxW8xa22vBSAHCtyxY8csN18ogXizC1Dn3Llz4tChQ6kHzDBqAB4YG5RdVnjt0kjPN1bU2zEOAH9YH+oagVLIwJ+Jwq+TLI8hrQohA2gYWxrHwPwz708fOa2bptDn9Ftyb3LYgyNmmlXgoeqOJVjVjA48GJLx+jQAmEeTGhDRjBnUWEFD8E1mYYvrq2LwnDuHUQJ/6AUsqS7UrhaAdZYqfXRPlK3qJwJ8dJSRqMOlAaz+CBSDm65pARhZA3S8W5RAmtZJJ8zqzMl7EzW8yvKVLr40PmmpCSRQU4AHVgmFiiJRh0WeGHDNeGtVMju4ZO0QJXvQJ/9gCifPztDzkkBZFyvOQqEkPnDzD7DUa4XqVrubrf+Y3uHjE+Lw8fPimis28Cmjzx888qwEqm2VqS+J2XDftov0xe28sx6+a+awTOv7YiisR/AzWTdmPC8Q+xl/SB4CTxHoBAAF8ZJ43759Yv/+/TJhCMBAhIvhZ4xKb3Z+SRycnq7dH+sXmoBWehD0VI9b8zead487mJ/sFZ2Xh4yzoDJGx4sKADixEMwCEKTgBtyf2+igms6vcQGA6ZRGxvWKkEDtgZyuoWheKqZrABm3SZBABgAmYRYi4IEtAEEqLAAIKz+4cMKiS33QA9RBfBe25uK3f1wnAvaXlUQGAJqLH+AwFHwAxBwDEusByj3WSFjgjzngzQFvPvh8Wj6Zf/DLIFpaeAefzD94hwvWuXlkZIxwBGH2zuhf4UG6/0YEAHqxUxzGVaXjCMQAvQ0UpauqBgCMGvhjlqXLrTKUBgCoH2OVEnV0ktWdajXItPw+cy7JP9AOcwcZSH78CNWvwx3ZZLoLa8lyEeCbBrQ17CpwNbhVS2s+aildfDWAkZMoYh/CUi8qaz3Ql9Z/Bn07edF9v4wst6Zdkn9wfWTvPUdx+zZv0IMwH7z5h2K20ECByz3+MwgrwD/5vWAA4E92NVsod8kksspiZ6bxSfc14/h/VF1nIXuxxQD0AgBV0SL2H/QA/M3OzkodAR4k0BkADOIPoUBwHTqlGg/4Kz96TFrMqvSaLACrp0W3RPdrtdwsACv0JqB0tld0X5ane4FKMdzxvMA6phgMVAqlbjGf7xWDvebgYqE8syIAQOgErPPzHkAKJaJ/2nnfjojljEwmgUwCmQRSK4EMAEzt1NkZdwKA9qve3xDXD8AflDUGXqDMAdCBsoZsb2phhZDrqtfSeAxlBmAWKzdpHAN4jnNeAA4z8MdyYoUf68S5RsLKMM4xhOUpSDvmH23SDgDe8/ThZuuaIMKIoC7tOaQ7pUUK32lTF8G+rkbShRCs3xjYsfqmA5fqahXvYyIAyA0xyAoDZNFCYAVKXMBfjTr1ab+Fk4We5EILcFhtyEpPugIbxkuT7WjCugiw0xWAZXB7ptBdJEgzSXYg8UcDR9KRtc6V+4jfRrx+63yrB5CUyi1GJ1186SQAIp2Lr1ufoIV11U0gaWS8EtGoYv/Vkn9MuLFvO484gDoA8BvfeVQ8M3bGqttBWYArg+7Wf1zxZ48dEO/63V8RObLINym4vx48T7yqk0MNa8lnHCfrBCHzarf+mrZPTJijZACgQyCarwgNs337dvH85z9fnD17VuoOyBwMXRPeI/hDLEHol5s2bRI/3XWwiYozBmCHEv8Pld0SgORnyfqv2CmqeXoh0Ke/FzV15nGiTD/yftopLcITmArcgIMAgCslDiB+b6zTxAEA1qSLR0OA3yc3yj4zCWQSyCSQSSCQBDIAMJC4kltZdQFeWKjvKn3YRT0k9sCbWX6ww4ILgA7e0Lplc+OHPwNBPt0k/jKDNmkHNOMYB1uFwuKP5YN1AVdwKO9uayTspLPyx+sxLJ3lasf8o3+W13LxEqZf5h/y/9muw2FIRNsGewF1P0Ab+Ej3BypthfOiLvkHrmsAAaVZjVevOugP+AbtS5fI2KmDrABbTe5h61/zBbHo8KcWmSUXJzx5JSvAYkWUAwCAoOtm2VYFHbpuHP9PJv6o2KZfHYN6XKAQW7DskqCay5yq9U2P4cpL215ZHS7UyOQL0E+6+HrJzqUDaX1GcwGLPWnJ6lIvyGkYReks1YLQ4Lp+yT+4Hj4RB/DFL9iinhJ7950U/3rrTgjIKhWyJjX50Z6bnhe79p8UL95+hdXW6+D+Z8dEyTFwANZRrgEHeclOk2uqF5MpvsbPL9YrwgwFbZEZGH/QN+Fdgj8AgefPn5d/Z2ZKYrJMFnWOG7vTArBaGrVYoFuDGHdxAc7P0M0AQO8C/VgjAADR6VAvhW2gtYUyuTAotqwzA8lRf6VkAlb1fd4DYHxRFtY/oqSZ0cok4CkB/KxDPMs9acZ9MW38xi2PjH4oCWQAYCixJa8RXCvw8MTGHS7AXgWxWcbHx+WbWa4H996rrrpKxvnze7jzdVUhYDpp/GQFlxXeNI4BPEc5DiSTATisWoXCyg/Anxc43KrseAxYx/hLm0Ko8gv+01aYf/D+3AmNj2obByT1MofhUJTuvzwU9KPgFfJ0abD5LIAgsinhZqE/2YIRyUDywwSyhaZk1tDp/otW7AKsAzhUqhVy3ezMkwUXJQYxKTmX5B+ybf3nYBr/rxPWf7SRNynFVTQzEhxufX7U/jBXVcITQFXN4qvWCXLMLtWdBVpL0pSQhOIAPoLQw8aly9wb0Zd0cRYBFM3KCbIAVMviUkF86GM/slsNE1JTGjKfk/94bL8xAPjdR59Ru5fHMvuvlzzNWZH0dCDtxWYByM+EJmEHPDEwMCCuvfZasW3bNgn84aUirAP/Yw9Zi2rmrEjJ7NRydm63uLy+YzldHBBLLnEE8hfIApAaVuep8jpD82G1I81xv7Qard3AgiYCKayQTMCqvs97AI2oslOZBDIJZBLIJJACCWQAYAomyYRFKGmcndcNAERgZoA6ePPKBUoZgL/NmzdbABJfc/tkkAaAWRpBGue4WJlRFRxnnTR8V+clLL94M481gox+DIjCKpSBP5ZVWPp+7XgMqIf+4+7Pj5+g1/E7xB9+F2kGAE+fmxAzeZgWBZWAT33QA/Bhgidhv6UCgPSdwTOfXswvgx/0o4yz3EVzR/wpp4LR86tNtKt1sKpCIGDnkl+D8NcxNAadLCoEylhWeqjgV7A5h8mNS8IHqzmt+a4lPUGcRZ9wA3ZaI1rt1QPqr8sjlqBaFTABQEVrTOrFEMfglePEAaQD8BdFARnGLNitWbqktqCF5WA4pRd5YJZX93WJsdEJ43V//LQ9E/Cff/T7YoFCadgLMdep/ojtV53f7n3isHj/m/6T6Ot1+Kw7K9L33SeaExQhbqXrLxf3ZHNWaj1qZHuxAYDqM1kzDYFP4fm4ceNG+bewsCj+5t9vsd/n6xRHx46IX9qyXia0Q5tCkdyE678Vt/h/ZXL9LS10CUQZqM4GnWz3oahJjCcX9HEv3VoXKQnISiiqfpw2vWwlyD8bQyaBTAKZBKKUQAuqZ5RsZLSikAADgKoLMECIqakpCergkwtchkdGRqQSBuUqSOGHP4McQdsH6asddVnBZcCrHX3G0Ucr41hcXLSAPwauYBUK4O+yyy5rGxCnriXmIw5ZxUkTYwDvaVtP4JkTu/w7bcSNkYCgwtRsqrUknCChBM20NVs76bj9FdZg6I6T1EPzmRa6xdgIXCrTTjWq+G06bmTmXwfjlvsvxmQwF3DdzRXIFZhi7HkVxF5zo1cBDYwXljQGzxvQ6jTgDfzIhCqQJ7AnbxZRXVvQlQQmwR7xKa3ztDXDn1QtMWHdiNISAEgkAABGVdZ15cSpAMRUC8Av/uv94uAJh1sk3U+KA84fsXcHC0tF8djuI+JVL93mWfHQyfNivgw/6saEQ6Zy3hqnbDSkrAPF/6P1bKNQ+1KmF1MXQ+HnF+sVcYz5jof2iZILTneWXlQ/8sgjAroqwo2s72+szjE399/ZHguw7yAAkJagukRCDyFHL4W4TM0P8qHRZ+YC7C0m6B34U3U/7xbZ1UwCmQQyCWQSaFUCGQDYqgQT1B6K0pkzZwRcfKG8fe973xNw5YTyxGXVqlViZGRErF+/PvQDlwFA0MRbwTgVROY7zk/mX33DGWd/cdHmcbDibtKPLg4kgD+skUsvvbTtc8tjAO9BxmEy1nbVYUUWSm0aCvjEfWOcwgIACEbZe9Y7jEAr4wLA4meJA9E5E1fE4f7rHAdtRcjdFfOm2/o7a2u+GzazLBlp81ump3DOaTilIR3mFFucqW3Z/Vc953cMAK+jDAsq9wHmXKz/JG0L8HRvzzwgjpsnLa5Y/ywN5EK7/2IepLUnTbkEHGP8yaqWmDwHAKzCwknS+s8hi7Bfu3OdYuZ8sEV48uyMABi2+5nj4jv//nTzTwZWowQqBimXrh4U58boReVLvVt9/YFdTchO1wL11+G/vrwpK1dd1kJmAajIqMXD79+/x5VCdy+ZR1OBPnvwwNPi2pc2nkmjS6u07Qrk/istuulqR4VexC1RaID+sL+wRhcdXaBRW1uZCzDdKw0T9TQkaHbEupNZ7axWJoEIJID7vMu9PgLq8ZBIG7/xSCGj2qIEMgCwRQEmqTksAPEAffzxx8UnPvEJMTo6Kl7wgheIj33sY2Lt2rUS1FmzZk1o4I/H6gQAo8oAy/Tb/cnjSSvgxPJipcxkHHATB+AD4IcL4kiOjIzIgN1Mi6+161Pt12Qc7eIrSD+sxCYdAFSBP9VqGGM9sxhN7CSt3EyUFwBG6l6e2ligmZZoCyfRT52n0gD6VTtW6JrwTU0hV14DSmv7IY1PugHTWcQCzM3ZL0fxTYJbGvyFwSfZB8YEtNVtzMwIuf92wgrQDQAksKeWfZUb2D8l6Eunyv6enQT+VYw8xNFDieQNYA2JMGzrBRddihwy5EIHAP0ksOxSN6rT0vWZ54LkzVaYAFWNGVeZITwiSuu/6y7fKA7ualhYqV25HRdLFTF+bEL85d/frh1C2TBupEp/eLYiFvL+955HDh1Tm8njDr9mwYwRXddFlgSkSfShTpybnBNH5mfpR6i/3265akT86otGBGIFTk89bOtjLK8HAGUCEPyk6qVK7sCi3zyuJbdzflZgvlyPRzG9OEjAN1k0G5oor5QswPyCHPqy7/PNKUCD73HQNOg2q5JJIJNAJoGLUgIZALhCph3ZWgHqbNiwQXz5y1+2RrVu3ToJAiLGX1SFATPQSytIo8qCQae0j8VkHLoEMP39/RL4CxIHUpVflMc8BtBMOoDmNm4eQ1LXE+SK4OtjY2MyMyOPA/MPC8CjZyYowDptePT7Mq4e+hNuosoerYkO8Kgm6z8YJ8XEjwQW6wwVh3HQ3BEsA6NIAGIbbN0qTmaXpQsB8QkbKd0XaXHWPBSyNmxIX3NZR0qeQ/beDgIBq5QYxFkQ+8+NFjLnwtINlo5um32m14HEH35ADlemz8Ja4qUxHOWK/RBV2OoUgF8cLr72Hu3fnNZ/LCu4pYYpUVr/of/iTDiQ5KN/d7vIa1xiO8qUOGYomHp59epV4tQzZ8Xcjd5BMWfmF8X5/AL9TFmKNJ/SZbzxvUmmBFB7Wa821acTbu7suD9ViF6nC3Clo5XGc/z84udZ1GP4wm2PeN4PALQODw+L7du3i2dP3G91XyA0/Vi+2Q0X8f/Ki9124HaObj7rraahD4oKAFghk+GZxQGxbrBhkehFuFAhkHMFFBUAXAHDyYaQSSCTQCaBi1oCwTS0i1pUyRw8NuwA/D75yU+KY8dqb8WhsL32ta8VH/7wh8VLX+rjSxNiWKpCyEpBCDKJacLjSftYeBysuKsCnp2dlRZ/5841MrsiAczIyIhMAJOUt68qH7pxqGNK6jGPIWkAJgN/sPxUEwUB+MM6gAXxrl27xONHKGi5x166Vbn7Wlyhb2f/QHCc51plxNGeoC1pTRZzN1avlkUjdVjup71wzfvaut7KAcSlc/8FTZsFIE6YyraDAECqWyXwwwnkwWrPrUjghQBcAIieheh2URZhn1oWCbCNuIQA0fg3Z12sH5ChDvFK15cB9GNewKcdAMSZWgkFANJYcuHwOu7W9rl5zVCg5B9WY5qvszN6EETGerQq+h/09nSJxUPTlCSmU8zOegOAX/j3HZhwG9Fawhj7ObUC5p8BYPW853FjmpqqAZzqCZDcpIlACk7w85f1iqhZvve5MU+ShVIDHZ9ZelYIWGdTOZofIrf55pcQ+dleeV39TVUBAEZQljrwBqphvgw3YHMA0J4sJwJ2loUE68eqAUDUjLjdx6PuJ6OXSSCTQCaBi10CGQCY0hVw4cIF8S//8i/iM5/5jLTmwTDw8IQ77tve9jbxqU99KraRqQohKwWxddYGwqzQsMLbhi5j6YLnRR0H1gkAHzXzcysJYGJhXCHKY8ApdRxKlcQfshKbFADQBPhjoYL3AxNkXbNMBXvupo06Nu+0t3ff3rfIbJ1wcTX60PeiP9tivzBeA3hFZCqUDKRKAGBU/UgZNu+RJcNOANDPIlMdZZVAPLgCV5SEIDJmn4fVHltSIVOvV0ESh1xjz+9VVV4rkus0xul0/5WuzwD9aDFJLz1aP8tZ1OQf4EO1cAxjiYikMd6SDDbayyl28IwImKmUzOBUoMXWI7k1lwddFp+tYuPLSFe/OAV30O6cuOADAP5ox3NC1MLD1QgQL8413aAc/sjrRYWMA0i8ruTCz1/1mRzVeB/dPS5mZdYed4pFBQDs7ThqVfR0/6Va6rzJRCD0++8IthytvvhgQQKA/E2IqYUh+tIIn9K40ny00rIAs77cPNLsTCaBFEpAKp0p4xs8ZyWTQIsSyADAFgXY7uYTExPic5/7nPybnp6W3cOF813vepc4evSouPXWW2Vijjj5AkgAJQDg30oAAFnBZYU3TtnFSVsdB9YGgL/JyUmrS7jTjIyMSDdxBqmsiwk5UPlKCoAWVDTqPARtG2V9yA8Wn3D1dbP4c/YH+Z+nTJyRIgzOTryUF1xz7Ksl0BAl4uHkp067NIjO4+zI2TF9x8YUABV9wkpMBYc0tY1PqRZnzkadiguwvOY1H87G9F1a9ClWgF1e1n8kTswfQDk3i0TZhbT+C8ZIcRUJrd4EHwwcL6e1n0ZcNus/XFfBqloMQF0r/TnIUm2vrxXs7NHD54M1APhHBlGuvxQZWNH1alNfm9YOibNPniUX8VobLwvAux7cK5aUhAwglpOAqEd/xK/M8NzUs88Jj+V4MSQCYX2In2c+0gp0+as/2elbX7UA3NjbWKOjS8PatoWZntrtQAH8O+jGU81TzLr+AG8WNNSLdJMe6u0Sc3lYAgoxQRaApmWlZQHOAEDTmc/qZRLIJJBJILkSaPG9WDIGduTIEfGBD3xAXHfdddKNDXHv4PoKt1hncPuoOAbdq6++WlrdYdM8MjISFWlPOp/97GfF3/zN3wgAPMjo+5GPfEQCPf/wD/8gNm3aJNvGNWaVMVYKWUlUr6XtmMeSdjCTxwH5P/nkkxb4h3Xyohe9SLzkJS8RGzdudHWXS8K8qQBgWtcWj2G5AEy2+NuxY4d45plnLPAPrr4vf/nLZUxQuPvqyrGJeZlYQXctqnNsEaal5wD/ZB2PjbiWRsCTIF/u8gEJgvDggUU4WQMwxqVc82Djr6E/pQWcTo51ik4AyXM+dFxQIpBcoS4QAldyi+7CkdmDiYZM/kHPSbeCrL+GMfUlCWznJWBEm/0qgCP6H9Z0+HPvxa33+M7r5gKWjlxcrei4guOzBnY5TrbwdaC/R8zNB/AnBvhHwneVMQG5xYFgauWq80VRkoByrZ0bAAiLsE986xe1+VbG7JsxGoCQW/IahY7zsIPG6lYyANBNMv7n84WSeOZsIxSJWwu2AJyjeI+X9zWyJOksAMsFiv+3VHPRda6+6rzHzdCtc835dcONG3TNAlBTSXOqSpaOxYreVV5TPbGnWD/OAMDETlHGWCaBTAKZBIwlkHoLwNtvv128+c1vFnB15AIADJlw8Yf4eHfeeafYtm0bX47k86/+6q+kZU0kxAIQ+dM//VPxta99TVr8vec97xHI6suFN/WqtQ9fi/oTSkCxWFwRFoCs0KQVcALgMzU1JQ4fPmyb5tWrV0tgGoA4g1K2Cgn8Aj4BZGIu0jofLOt2A4DoT2fxhxcDW7dulS9H/Kb8wYP+GzM/Gr7XXfbV2G+zFZdFAwBPDd+xTkV+QPQLHu6/BA2SgZ4r5NEaO7RbZTdgmQyEugkChOk6d7qcOus4AUC2onPW8/ouE4IQkCXdbAGwuJW62Lzi/0kXYkriYFrK5C69sL5TzokEL82bmnYRWb0a8Gknp1p5qu6K9lrN32B1x9mDm6+GOwNoxGv6mqhSZU/AGD9i0g1My3WXbxAn7z0iKr2NNnNz+hiAH//yz8n6D5Ot/BYJOPSTCfgNs0S8xnkxZALm56/6YtF0Xr3qfeOuJ0TZidJpGhQpziLKjtMPi/+yqmHBN7rUnAGY4/9pyAgZB3CD7kqwc8MD9hiAQVrDDbi7U//SLQid5awbFwCo6kmsOy3nOLO+MwlkEsgkcDFIINUA4FNPPSXe+MY3ysyViGsGa7ibbrpJfv/2t78tvvSlL4kDBw6I17/+9RIMhAtkFAX9wuKur69PxtxDgoV2FVhwjY6OShdcZ5/tBgDRPysFTl7S9J0VXFZ408I7FCe4+MLVd2bGHmj6hhtuSLy1n5ucWQlUFUO3ukk8z+upXfyjHzfgD5bJuDealv1n7evItF0k9bCvV/b2oCktpBznIulLIVKlnX6l1wEsKNdjP8RmGHtdGmeZYpu1mgzEy/1XUHy2prhzodARAuppg+6Z/IOG1Fnft3vF/wMNAzxAzK8hd77+Wk0JyIbhO/bJbHQA9nRzoVoAsnwardyPuvJNPw/3ygZXAKzPzeRFw67JpxHdZzwBS7peCmD915XrFBd2naVFQozQMZe5uTzpFbS2lHPjJyfFPQ8fEMXL7TeDLhk3036O6bT86YGMljWZj1vuL2EE+PnFz7Oo2PvxYxTD0aCwC/CxmZ1C1DG/WULUz5coY5Kj5Gdqq1gL2s5Gs83p72uA1EgCEqQUKjOUw+TSIE0SV5d1fX5hHjWDrPdFTTejl0nAUwLSTD+mZ4hnxy1cBM9ZySTQogSieTK2yETY5u973/sk2NfV1aWnBa4AAEAASURBVCXuvvtu8YpXvMIi9ZrXvEZce+214kMf+pAEAT/96U+Lm2++2boe9gAPwXe+850S+Prrv/5r8ZWvfIWy1rUPAATfbg9gBgDb4QLMPKQNNNPNO4+FFRxdnSSdg2KOWJCI7aauPVj8MRAIt9+0KlTYcGAu0rq2WO5x88/AHwDgubmGixQs/oICf1jfoHd+MWKUwfCHQ12LKll2OQvOk1ForEW63np0Erh7NAgATiE+GQMrrSYDqQBHaWApTXLL1UJY2c5rN822GvovlZ5OkVtwR0lgiZijy+DJLQabTPzhkUCEe15cRUIloMgtSQvXS9InLDqb5oIWtGqBaRoDMA7rP8xJJwHCRoX49oz7R0QwlmqAxBhX9neJ89OzBJI2L9j5+TyFOGkAPX/5D3eKMuEvFUcimU5pOerxCyW+m6yKjQZM4/EQTankvu4NySe+Gj+/ogQAj5yaEqfJpdfkps4uwMXiQUtWYy7x/xgA1N13O+Y6KXM5ddm8zCy6JgeUX88q84U+kS92id5uzQ3VqtU4AACY9sL6cZTrIe0yyfjPJJBJIJNAWiXQ4iNx+YaN+FYPPPCAZOAd73iHDfxjrhAXcPv27fIrYufBZbXVAjpPPPGEeP7zny8+/OEPt0ou0vZs6dMOF2BWAlgpiHQgbSbGY2GFt83dG3fHgM/OnTvF7t27LfBv/fr14sUvfrF44QtfaNFK+lgsRjUHPB8YbxoLA4Bx8Q+6Z8+eFVgHiPHH4B+Av5e97GUC1p98Lwgiv0f3HhMVj710EFpedbUba/Tr7Bt77DY8oWrJP7w4jvkajdta6TRendWYKQd+bVXwyaLZwu+sNJSTwLFFSz2AZRcV6f6rA1jJfTO3WGmadpUEjpeGO0SZLP+cy8NZL2nfpfuvgym4q6rjYODXUa3pKzL/Rlmw3gCMGcUgpPXhGfevzlip1/zHOkguv1O7KEEVrRGZVMYxODUO4G337BEnT82IksODEoCjn/tvLcGOKnFHR15frR9lc6WLwQKQdQh+HjdLwfzMYqEgxuml5RdufZgWndl8FOglIMqlQ40EILr4fyVK8lHJ12wZdL8nvDSoLjWs98y5ttfskMlnGucmZSbgxnevo5WQCZh1fRhcRF1YZ+LPqOln9DIJZBLIJJBJwC6B6O/kdvqxfbvtttss2m9/+9utY/UAistb3/pW6RqMpBm/+MUvxOte9zq1SqBjJBtB7D+Uz3/+86KnR2OyEohitJXZArAdAGDarOa8JM0KLiu8XnWX4xoDPrD0Uud2w4YNYmRkRCaDAV+lUuNtdFLHYiI/VgLTOgbmP2oAEPTOnz8vLT8Z9IM8w1r8OefiricOOE+15TvwJ2kt5eitHe6/FYLe0LfblhTx/7SApYPXlr8CO6kbFcENWI0TZ0pbgjo+T3TV/ZTptjI+JOAoETjXTUk8nIXpurn/Suu/RmgvZ3P5vTAgRHGI4v2hXnMX2jZJOCm9ijSYgxOANQHg0CaIq7DJ+KVFJi36JndwXWNYT/nIvoNcditDPotPoX1t/yoxKi7I+J4U9FW5Ujs8fXpCXH75WrGUL4p//uaD8mRxwP4rzcH91wdMAiDkw3pT33zCa8xlU8tJJpbCT37+sn7UyhC+/chu8a1dz4jCEUKym6dbSxoWgKcuzIgrBxoeNjoLwPxsQw/XAYAgLhOBDPjcbLRcNE6WHD9CJAK5dPV0o4LH0UrIBMwAYBTrwUNU2aVMAm2VAO7zXvf6tjJj2Fna+DUcVlatzRIw19jazJhfdw8+WFMKAXrB+smtvOpVr7IuPfTQQy0BgO9+97slAPOWt7xFvPrVr7boJuWAAcB2ugCzUpAUGYThI6lgJhRwWHoB+FPnFHEgAfw5Y1qqihkr72HksdxteBxpHUPU/LsBf1gHW7duDWXtp5vj3UfP6E6355wGLMHG3b7lj56VMnkZ+rmV+l1v4gpMB0QdVDdgWGbBEjNoMhC/5B/gU2sxFZBX53hLg52iK18iJboxW3D7xX4ZpHWWcIKs/7o8sgejjxKF9cqvyZH1GdGFFRr91yKrTtZj+y4tMRvisPrp5OzJ9TMOTMGqZx3QgJH5N+rCbrG+ACTNkxuoovJUCZBld9Ngvxh99JigrAha91/Q3bFzlygUzonv3XdCFAsUDoLWE4BxtdSy/2qErFYyBJvUJvJYvpVoOmudWAl6jzUYlwN+/vLzzKWa0emf7j4kzs4uiA4Kw91rmBAXAODnHrpX/D8vbgCAo/nmBCCFevw/yYgLxledo63OxgDZrjWjmllsJBrE5SBxAFeSCzDryxoRZacyCWQSyCSQSSAlEkgtALh3714pYmT39TJJv+6666yp4DbWiQAHSCryk5/8RKxdu1YgnmASC7v9qWBRXHyyUrgSFGEeCyu8ccnMlC74OHPmjAT+FhfJzKFeNm/eLK666ipXwEe1hkjKWJj3IJ88H1Fb0AXhoZW6PA+t8t8u4A9jzRdLYmIBJjWtjDxkWw34J7CRawMvlT5AShF3FIYctbE4oWMAk50UKitIqSoxqtzaOS3QUK/lt8nkxlkczImeuboJI9GUbp00hxKUrLsCqzwBvPECOEukmSxuIPCPkFCIs/aW3pKQSiqRx1rQkzh1xmCUsieQTSbC0IxEWv81xKqpEfyUxGkhVIB7EKlbAehKc4iqXgVWsiWHdZ5rfaLZd3KRsvnWkTmN9R/a5pfKYt/YGfHUs6clqRJZgqquox3IQA25eDFHfTHQKYkE+Yfk4kV6pVsA4tnDzy9+HgcRn1p3em6R3H+nhKDfNOK8lugx4/CmVatbx8gCPDazX2zopri09TKmywCsAICugPas7iHDVM0+Z8G4oBtzvUzOO3zS+YLmM3MB1gglO5VJIJNAJoFMAssmgVQCgEtLS9IVDlK74oorPIUHwA6WcXCdPHaM3jqHKFNTU+L973+/bPnxj39cZlcNQSb2JmwBCPdAKG8MRMTRMb8FTDPQxHLhsUBmGE+rCi/TDfqJvk+dOiXgao41joI5ZOCP59eNLuqCd9BJ87zwuk3rGJh/3kC5zZfbebTTufpGbfGn9v+zJw/X3PHUk3EdS/CBiBMGQEN1d/8Na71jyHeFECiAIV4bfa9rht0YV1OtAGFBBnzDVARoK906fXrTAYA+TYwuI0ZfmeL55dgCpy44Gf/PQaGjRLH/ZPIGx4X6V1h7LW6iDTsmpw5+SbDHC6zSk1qWsxVgDS4T57QABIMYm3buaLzI/Bt1kX2RaLXWoNwZ9AcD8A/VK+QGrnPjZVLqZ/eFkpg5RYPqycmkHuo19fiSS7aIbz/4rHXKCTDK7L/0vPMsWDvgLUTxBEaJXonAqZVc1Gdvq/rQ1+9/ShQActP/mLLSagLCCQ/0mxm8lNq8/pQl5jOFfjHvCHIq4/8VatsY3B5cfnaiY771RCALDnPdIDEAV5IFYKvrwZpQzQHrTppL2alMApkEMglkEohQAqkEANXMp2z15iUTBgDVuFle9Z3XPvjBD0qLLGQZRgbgpBYGiGCVV6Cgy7295EMVU2HQbCVZAEJUUHzjVHB00wEZMvCXz9d2fFCELrnkEmnxNzAA8wezshIAQJa/ugkxG30yaoXl3wv4GxlpdvmOcrQ/3304SnL+tLBbQ3HZBeKyyyXZLIp/YFUU2L3XpOOwjAPEI1xBNqdjZATuNPRaM3H/Beu6GIBR+dUWh3OicwpZLuhFRD0cqS7+X26J7rEucgSssrCZEDSyGrRlnQV4wGvGpW1STjswChtbOgAWuEJZo4lh7k3cb20d+HyBCNkqztVaCjSwDg3kjSpYp0aF4ub1nVkiwB/gLrX0SCbw+MHTlNG+9ixEH0XHIzAnXam9f2jgH21DFZ+G5QwANBbrz56lZ4syVTgswBW44dmrpXV+dl68fB0hhfUylqdGjpK/YKbjyvABi2RRPEg/tpClQGbpAwRcL5BLOsrkfDM/bqRXUgxA1v3dxhr0fNgXpUH7yepnEtBKAPd6n/u9tt1ynkwbv8spq6xvVwlo1E7Xuom5wNZRYMgkEQcDYao7pelg7r//fvHVr35Vuhkj8UeS31CpYCgsHnncpmMNUo+VgJUIAAaRQyt1IbuTJ09Kiz8AtihYX5deeqkE/vr7G+4mpv2EBZ9M6bejHv/G0goAMv+mii3qTVCGxLGxMSuzM+TsFusxjjnYd7KRaTEO+k00CWCQVms68ID2Vx1uCFETofAnqt3QopSdqYMUXBtNQBBHM/lVtvWgrWvjrF7B3tYAAMQovEAntS+nCyquhR2jShfH1W7K1ttHW22K2ZfD/JJoJTCpVJSJPyipha4w+IfEIgI4oqLkymPlu659Es7JufDQqnLkuuosWiCOqsVh/XfNprXi2OiEgJWiK7gI12BMhkGR1oQubrzO5n0TBXL7pjiOBO56kce1Jw81LL9KeAwqbuSwolTjTTr7sb67/7StKm4HfuPPLADdJGc/f2rygjgxTUif4zeBxEtw8++qvyiwt6p9O1ucE1uHG3H3fN1/m39aNrLVeeqwBQAQxNYN94mFiVoQw4vVBZh1f5twsy+ZBDIJZBLIJJAqCTgey+ngva+vEQ2agRMvztmqKiiggnbvete7pDvt+973PvGiF73Iq5tlv8YWgGAEAOC6deti42klAE0sHFWhaQegiT5OnDghjh49Ki01wQfkedlll4krr7xSqOubeTT9XAnzwmMwBdBMZdOueqYAoBvwh+zOW7dubUryEhf/M/NL4gIA6BY2zEF5A6ADgEjXp9x8x8xLuYcYILDdr8ByJHBBE2xGQzQFoMLgg0wGQt/9MrXKDMqGfeks0CSvsMgykIefLBALsHuOEFyK3VezSlQYI2ApR27CyhkbuaWNOYoRRlcJAWIZcIUOgFKQacKLBGLdBkgy1sm/Q2aUtTfK4ecYw3iRrRm04aWsLcSjqesv2pd1AL6GcGe+InoIAKx216z/qj3uCH9xNa0Dmm8uTvff3AKu2eXFda1PGgdbOlrnAhz4yb5MngIruagv3/h5HGa8t/ziCZnAxdkWs1ciA7pOMvBzWwnFNYtiS8+c1VSXACSvxP/zs+KpztHa22SRC3WwaoC2TBO1piW6wV1Y6hOr+vyz9KwkF2BVXw4lRE0j1pk0l7JTmQQyCWQSyCQQgwRSCQCq2U9N3HoBhqGoFnImsvzYxz4m9u/fL7Zs2SL+x//4HyZNlrWOCgDGnQiElYB2AGZxC1VVcFXFN+p+S6WSOH78uIxFWSzWzGDQ9+WXXy6BvygsNnkscY4jark46aV9DH78JwX4Y7nf8dg+3/00143sk/bP2g067e0jwqI8WS31G4AInhRiuki7YcsNmHbJyHzqlwzEaWXnxhli7+mADR8oxY2c/jxlg5Vx/8iyx+n+K63/XDzwFtdSDEGyHkQB+NfEE6YLfwkvXpaYNpdmZRxOsBPjzMUQ++/y9avE+MHJZtkyLwHBPwngczIPpuHy2U+uvx1kxQfrP/lix8VqEMlTKkRTnf+immuBeNSBqM5uAWLCIjV08Vlr5VIGAJrI9qH9R10RPsxOkZL69jaM/CySWFtda4piS28DABxbsrvcFpcIKC4qULbPnIkIEoEM9Cv9EbdT80NGAOBCYVKGedm0aZNg3dkabEoOWNePk/8MCEzJYsjYzCSQSSD1EkglAAgLqfXr10u3OQAqXgUJPBgABJAXpPz93/+9rP7a175W3H777dqmTBufyBSMgof8a17zGm39OE+qACDzFVd/rASwUhBXP+2gy4AN+ooDOAPYx8AfQEAUyI+BPxM3dtnI4B8eSxzjMOg+kiqsBK40C8CkAX88WQ88d4QP2/YpwT/7XqrWN23c43b/rRDCgv794AG/657CCtvY0Q6AEqAGNysZbJS1QKqGOY7Lp7lUA9ccfWvrGZyUCUHyZdFN79265xnxI5kjbhrF0HKWpVWdojRY7xyD1WArAC514KWT1nJ+R/IS6RLrwkTOxfXZkVtAgn9xjHXzwCAZL027cEfyJbkH6RfjNSndsyXRReuArf8q3fqGwG+Kw1223yUsDKVLeL0jAKN2eNCEg+B1YHHqVTILQC/p1K4dpLASZyiOn6hbiepuL7h3Fel6tyPUAVzUe1blxRU9tZf3JbrRHUXgQKU44//5rd2OBUoEgudL8y1Ioep92O0AlScJALxqvX/4jGrHotj19C7R090jPT2QvFA1ZPDudfmvQp9kfYx1/6i5Yr0varoZvUwCmQQyCWQSaJZAKgFADOP6668XDzzwgDh06JAAqNLlElB63z6ybqmX7du386HRJ7sX33LLLQJ/XgVZO9/0pjfJKq961auWBQAEkNTd3S0AOLULAEwz0MTzyaAZvkcJaGIekHka4J8K/EH5AxgdJfDnHEua54XnI61jYEWWFWYv4G9kZESsWkVmEMtYDp+dbHvvVRfXQWkNpccHIuOxTDHFjFx7vTEAb37QVrfj9W4lr6puwED+vJKBeLqcOvryspzC5rmV4dq6Ilfi0lCOLHtqSB7o5i4UaBxkHegAAAuU3KG4igQF92Oq6OZ+KtdFZAzauI3si99caBOwUO+2GIAksjis/3oo8cbYwbPuY4WLtQZ4dW1A9SuDBj9Uqtd3mlwkEcMPfzDvzenbFUHP4YbelPxjEYuA6PgVfRd+rRrXfdZaFgOwISq3o1vuIfdfnx0GZrJMFp5wS1enDMDgFetnRE89/sGx/JAoOdD1gur+S3RsvyMNU7jnVxa7RG7II/Cgpp3tVJd9YRhnAqYbbEfXEunmHTLm85EjR8Tq1asFdEHEfHbbv9j6XsYvqi4WFwC4jMPLus4kkEkgk8BFJwGfx3Ny5fFrv/ZrEgAE0PXEE0+Il7/85Vpm77vvPuv8r/7qr1rHK/UAVoDT09OxA4AM0kQJmC3XnACwwXig5KiKTlh+ABwz8MfygYLHwB9A2rgKz0sU44iLRz+6aR8DA4CYA07uceFCw88JMf5GRpYf+MM8nJy4IBYpJqXJntpv3kyvS0sp3U+A9lawaMOmMM5S6fMHEWpJPFrgopVB0E7YcgMmFioIeeuwkAFnGIXfBhv1uLgBUPK6fV/LTUJ/wg0Y1lu1TLYV0X2hKApr7JNeoiQn+XW002fQx8sCjfiDSAGm8+8rNHMxNDSZCzcLwFoMwBpTAP9aWTpuQ9t+2Uaxf9dJ/eW666/+ov5shVy9rXnTV5FneyeKlBGafk2I/UelAhBQUyhkJLmAU5ZWx7UiW4biPNx/TbAbAh0dWJGDqv9XP2uyiykLcNjf287DJxyonrvci6vJFXimdl1aNQ+UxdXD9RN02iQDsBGAPU/rsAUAsOJYgLAANC0vf+WNYuZsp3whjNBFMzMz8g9GCpdcconUD9esWZPI+xu/wMZYMwDQdMazepkEMglkEkiuBFILAP7O7/yO+Lu/+zspWVjn6QBAbMD/7d/+TdbBg/Wmm24KNBNswePVCBt5vM276qqrxPj4uFfVtlxjADCLARhM3FEAgEgag8QeSPDBAByAPyT2APjXjre8aQfPMGu84TD5/QWb5fbUZv4BxAMA5IKwBVu3bl12iz/mB5+3PbI3HsRB7cRxjLh22gIc0okAaCuGP1npJHCA+jDpxshKMDwr7i0dzAHMAGjqTAYiXX/pvGlx7F1tzSK1AKxTLg50ip4iuQJPF6S8AQSxQ3CJsMBFSvphTTjAPzcLNABUzC2QNusLn1z+TwnE+syFGwBrjZvGD0uoOMrSlEtQwTr4F0ik1Kbc79+is1ARvefhs0t169Z/VRf338Jqu+svZAA30Epvo5+uRUx947ubnCDPKgDKFoo/ABgxYt4Cr3E0Zf0FzzJ+ngXp54lDx8XkIll+EsjvVzBTuMcV6LnQQ00w711rCo74f3Yr+SJZ8qnx/+Rs8M3Fo0OZCGSzRwWfS0tV+w80CABYzS2IkZEb5F4BLwTxkvjUqVPS6wQ6I/6gv0NXREK4KGJC+wzH+DKvBzSIEgCEjpfUlzrGwskqZhLIJJBJIIUS8FFZkzuil73sZeLXf/3XJYNf+cpXxCOPPNLE7Kc//Wmxdy9tcKkgi6/T8uree++Vyg0UnLe97W2yXtr/4UQn7QIA8fBWlYO0yo+VGrbYCzIOAH8HDhyQaxBKHeSBtXbNNdeIV77ylaT0jbQF/APPKwEATOsY8FsA4Hf69Gm5fHgtAfh7yUteIm688cZEgX9g8tH9xySv7foHlj4Vl02hBYTEyEyJXE5NQIQYWTAibbNgIpmVyG3ZWbwSTjjr4rubBZqsGwOeARCmTJaAXQs1s63OYg3hA5i5uIl2+fTc5YK5dwNd1HXhVofpLNenyVy4uWBzDEDyEDSAt4KP8Aok/zjSeBGhUvCSu1rPdkz3OTc3XrVe35m8HE+1nigEwDs9oNQq8ljG+dMAdrbkH2hKGYzbVmpL1bW7i8UFmJ/FroJwufD1+3ZR7EaXi5rTWBoVusdJ63AAgKspAYiSAXgsbwcAnfH/QLJ5ZTV31NFiIpD5ih1IN3YBJlaKlZpFI/YbcP+94YYbpFECPmGggAKPJiQexN7kqaeeEufOnZMAmby4jP+wLgMWWFeOmp0wQHPUPGT0Lj4J4N4DvSJVfxffNGUjjkECAR7RMfTeIsnPfvazAm69i4uL4nWve534i7/4C/lAxXck5PjiF78oe3je854nPvCBD7TYWzqacyIQk+zIrYxIVQwBeKnfW6G7XG2Z/yBg5tLSkrT+PHnypKWkIa4fLP6Q4CMuRclLRmHG4UVvOa6lbQwA/iYnJ8XY2JhQXX1h8flLv/RLiQP91Dk9NtVws1LPx3Uswb8G7tPohhQwU8u8RqPgR9VugAg6Buy0/GvY60f+jXazqhtwlSzmgEnwJheyylE8NLjYAhysDPhz7GaBJnmPCVspUVy3cm8nxbWjZwQBgBX6rcxfQmqH6gqKgXn1r15TjyMXejiCmAuTRCy5op55GfeQrJfcAMJwXDVabeofEOd1yT/IVVYFVxstPI5o/kr9vArd63XNl0TPXElm/ZVAL7XTJf+ARAqOxB9MtaSuaeJVWrD6L/OW3X/Rvx/QvNKTgOCZhsLPYvnF8B+0fWr8VONmZdgO1QqE88ENvnuV3QJw1JEBWAcAmnTTsUhu5mVKBpLzQXhdiM0UF+hK423MhcUBUSJ6XQb0CpVGGBAmz6FhYPUHnR3xoqFPIozMmTNn5B+SHkKfRJ3+/kbfTKMdn3G7AGfgXztmMesjk0AmgUwCDQmkGgD85V/+ZfGd73xHvPnNb5YbbwCAzgLw784770xVxi3nGIJ8ZwCwXRaA4A1vB9vh3hpEDkHrsqJrAgACYIbbN9w3WFGGuwbcwBHQeTmAPx5vkHFwm6R9sjLIsk0af8wP+NMBfwMDAwK/P3wud4IP5lX3uWf8jCh6Ii+6VuHPYctVdrH+g2+oYhAWvhOPlmWAf6ad6LEaD+qOSwAqWqGB9ioNOgZ42skGKORO20EgWgd10jtLXc1VxdI6atOFRvri5wKsb9XiWeJxfgv9Dg7NSUBzAeCfw9pLAmAeslLBGBx7VG2R2XDN/ZJ/MFU3gA8ZZ+OK/ddNCTfGDp5jFhqfdO/yS5rQqNw4QrxCjufXOOs4Itr9lPgD88TWf7KGJvlHkZLF6CxyAaqqVq9dhLvwc8HRm/0rydK5vuwVDL/5LLJyKRyAZNj7sldjPYj1iSAM3bP7sJinBGhN7r8+MpW3O/qnTLeI7qGiuLJuAbhQzonTSjYYWl6iMEMBRpWi3iOU09rDznyXKA/YXXm1FTUn58sF0d8zJBYLNX9jBCeYXhwUG4boJuxTdACg2gTeO9ddd53AnuXs2bMSDERyQbxoPnz4sPyDNwGAwM2bN4cCZ9X+ghyr68HodxiEeFY3k0AmgcRKAHvdz33ucxJHgYcb9rrwbvuDP/gD8Z73vEfuc8IyDw/Nn//852Lnzp1iz5498r6Hex720LjHvfSlLxV/+Id/KH77t3/b6PmPFxVf/vKXxTe/+U2B2Kp4qYJwCq997WvFn/3Zn4kXvOAFYVldke1SDQBiRn7rt35L7N69W8AaEEAf3qDBCmvbtm3i93//98V73/velhZo2madXYDblQUY8lHdA9ImL+aXQTuvsQDUGR8fl29lGZzCzXBkZEQCf2GUZe4/qk/mgRW2qOi2k07Sx4C5n5qakhZ/COTNBco51gKsAA8ePGiBw3w9aZ937tjXVpZgxSbIvUtXpCWSv2GRrqnxudIAdqDYZnoXgjgMannTiOKqtIhUNs2VHrKCon1mhXa73YUOkV9LgGCZ4BOApwR8DFCS13JvVZ63WdjVmXEDoORl7KpjGnV5MCfyq7vF3FW9otrjkH9tH+0pLpuVmiIPz0Ztugh2yljXPqUDgK0L7wBmPefGh7bX5Ws2rRPj5xzZf2muO6hPx0x4kbGulcia06/0TBUpliH9igD0dtR6kUlDHA3h7gnrUB0fcNXntmiWy5utT6wVE2tMByv2r5CP/UzTt5VuAcj6Az+LmwTgceK7D+8JbYUp5T5YEqv68mJdd+1tR839tzEjMv5fyfEgcflt6diszNO2JyQACHrrhnrFiUlYAtYK4gCaAIDsAszt3D4hcyQFwR9eNiM2IPY1AAIRYgR/CDGDjS3AwOHhYTdSkZ1nvZj15MgIK4QyYFERRnbYPglIE/7G/aV9HbfQE3huQ7n99tstAyvuDvvgxx9/XP4BbAPuArwlTPnYxz4mwTpdW3hU4e+73/2ueNWrXiV+8IMfCOyx3AqAw9/8zd+UYKJaZ3R0VHqDfu1rXxP/+I//KP7oj/5IvXxRH6ceAMTswfLqM5/5jPwLMpuvfvWrW96kAxBKUmELwHYCgKwsJkkOQXlhRVc3FsiSgT+mC7cMgD1Q0rgtX1vOT+ZFN47l5CtI36wIJm0MbsDfunXrxNatW2VcH4xzdrZmDcAgcZCxt7Puk6On2tkdZfl06Y42bxLscrkcxekKgXoABkzVJp1VUiA+0FGATamWNgEkNjdgAlQwji4C/yoA0nCd/mSpJ1nI5TvEwOmqKA7S3yqqwwMmUMMLZOJqWj4iOHnh2n7KZszM1gkSWAPAxg0Ys7qV4GTtG+q2KlaLbgQHpolYvOIvSgA3Al50JMqzZInlKKH7K5ML95C3ythBlnF9Z/Nyjqps8UfzV+1yADbEky7xB7NaVNx/JXgKoDjuRcqdGxj3XbgwK3VHflZx05Xyyc9e1idMx4XsyM8cO6MFYTF9Jr/dnlUU/693zupybMk//p/vPcSiRjxcoPvQRuVEwMNVA90EADYamSYC8bMAbFBsHMHlF5trWNwA+AMQCNfgIllYwjIHf4gnuGXLFqmLxuWJ0w4AsDHq7CiTQCaB5ZYAYpC+8Y1vlC8hYFj0kY98xBZi7Utf+pKMff/6179egoFhXkTgfoUErgjl9sIXvlDewzZu3CgNLGDB94UvfEE888wz4r777pPGXg8++KB2v4370xve8AYL/Pvd3/1d8c53vlNgb/bYY4+Jv/3bv5XWhX/8x38swyn8xm/8xnKLNxH9e2tziWAxYyKIBBgAjNsFWFUMWTkIwmfS6vKbTXUsMB8G8Ad3DC5w6wTg3G4XDO7f75PnhRV4v/pJvM5jSAqAZgr8sSyZ/yTPAcZ0epY2WW3aVMvg7i6WUrBGipuPMoVOahnU4wlu1yfmRt0103FxNbnHnafkGr01JpAx0yqwuCIXTQQL7JnvoD+K10aechi7fMkN/A2Am9VAOTDZmSvVgxwi428T+AcCbrw4iAfZ3Duaxv7VNfkHremuBZI/LNnILdsr/qK0cATIWbeWi4pphCU7enTKPt9kKRpWniaZdfsJ/KNE23brP4zLMbYSAdhV6cLePFosRSm3+iWZ/dfRvrkVnQHQqP4etJX8T5rIZ/zIUfHAAxVpgYX4bEnK2Oo/Qv8a/OziZ5l/i1qN2x59VuQJBBQu93oTOt3DFP/PlgDEbuFWuFC/+anEqEvTUiEA0PEqwrSprNffZ19kkwuDRu0L9SQgRpUdlQA0b9iwQf4hPiDiBAIMhI4KLwT8wZ0OIWhgFQhQMEpwmvVi1pMd7GVfMwlkElhhEkDiVFggA6S7++67xSte8QprhK95zWvEtddeKz70oQ9JEBAJV2+++WbruukBLAjdXlrAbfdP/uRPpKvxD3/4Q5lk84477pDuwE76sO4DOIjy7ne/W/zTP/2TVQUJYwH4vfjFL5beWXAFxr3SrV+r4UVw0Mpz8CIQT/qGyABg3BaAUC5YOWTlIH3SanDMY4HiCwsuxCPYsWOHBf5Brtdff718WwEli+s3KCTjiPliBT4ZXAXjIiljAEiGGH9PPvmk2LVrl1SyMRK8VcLDBAk+oGg7CyveSQEwnfzh+wPPjJMrqe5KPOcAQrmWGMEn7rNCrrGmJRKxREKkBtypfFd6Cfwj2gw8NQEeAAHpqV5be5SBl6wFe6eF6COLlfyaTrG0tkMsrukQS6vJhXiIrLBoXgDQAX+Ko5ToFaM26zPAP/Rp0G9SXYDBekXzChWANiz+OmmMA+fpk7LXIlmLW5FLhYC5qMsAocO2ZUiTHCbun+SL+LMl5dAwm1ssi+4ZSvxB12zWf5QJWi3AaorDDt6UCrAUVsHGmvuvUsHtEIRhCdtiMQEASU2QcV4PHDggM7bi+QAXpCTf84OIhfUHfhabtv3x4/saVsnORoZLvHuY4v8pFoCjigUg7lP5mWYAMNC6XuoUa7uBzIcrvXQPVsvkvB2gVK+px0VNEhD1uukxQhyNjIxIq5lf+ZVfkYAfgDno4QAFH330UfHQQw/Jl9cAC6MorONnAGAU0sxoZBJItgSw933ggQckk+94xzts4B9zjsSq27dvl18Rgg1WyUGLHwiH+80HP/hBiyzzZJ2oH3zqU5+SR9ibffKTn3RellbUsGBEOXTokLj11lub6lyMJzIAcIXNertiAEJsrAywcpBmUbKie+7cOWlGjE8UyPOGG24QeIsAd18Gd5I6Vh4HK/BJ5dOLLx7Dcm2m0G8Y4I/HxGtkufhnPrw+737ykNflSK8BaKzYY7Y36NNGOu5wJhUySbJcZRs9ux8ZblTdCUR4BWCeSo5kWVhDJ+p7UO24AAKSkYq1/uhlDepLK0AcE0gCgKXSQxl6KaNrcbj2BytN64+qAU/hP5UF02NYJ1bg9o0+HUW6/jrOuX5VBNARF1Lp2rn7BV3yDwB9cLXGiME2PvsIgO2kGIBexQR08mrfdI3kVKZMvFah72Hj/tVoEP8Um8y1EP3+U0u1ZYn1V59zOWpHuxr417wmmLbq/ttJsQRtADBX0nxGJkPvqZI9b9lypQz7ADAGv7PTp09LN6j7779fIOZQPs/ZejSMpuAU6w/8LDZhealQFPtPEQhqN5AzaWrVoTs1JQApEABYC6OBC7UYgLUqxUV6W0FZd50lEABIjS/pwk00XOnosi+QKWMLwOYswOE4qLWCnrFmzRqpn950003yk19IwjIQLnS/+MUv5MvLVsFp1vFZ52+Fb7Wt9Yyik6w3qdez40wCsUsAP+c0/sUomNtuu82i/va3v906Vg/wbHjrW98qT01PT8t7jXo9qmPVtRhxUJ0FL+Fg0YeCxCTw0tOVt73tbdbpDACsiULz/tqSUXaQQgmwBWDcLsAQDSuHrBykUFySZbhP4AaGwjcY3HRGRkaky0WaFBOeE1bg5aBS9g/LeznGwMk9eD1AdGvXrpWbPSjbJiUNc7CH4jS1q0gLMJf9vty4uVyLij+4E5q6/2IDalrXk7+oxgQ6+FP2nLCQ6mLDDgCEdL0J/JCWUDQacgmWv6c6IANChFfIYmPR9qXeZ62a7BtAoGxIsdg6DaysAPrKLK5Wv0yMPhHPjRVu5bTboQ0AUuTgVr9d59kKk/uT4J+S1ISHXhMtWV+uqoqeC/8/e28CJclRnotGd1V1dfdM92yaXSN1C2EtgMFgIXQEl82Hd20evvi+hxfALOZwuXr2QdhgPYyvn425wOUYYYSFdVkkAT4IIYMWI9lsElrQgha0ANpnRjMazT7dPb3W3u/7IuuviszKtSqzumqUMVOdW8Qff/wRmRnx5b/UXM0PeR8sxzgbY1ThGs3BmQj+gb6zi62LIf6ifHlVK+hilhyC5l+2SA+VqE58/2G/5ohKrUFhAM9+vFQMi8rMEiiKIM0K3fb9iLrl9zhnG28eeTKZrDrjjDO0GRRdhDA6In200WSKCxIGgdq0aZP2zUbH5fJO8yDXc6fl3SvvsjAMfuu2h+GBAP3VAQCYGa2oQQBsO4YWdJVHy3k1W21+PSrONveFJ445mp1HSflCK52w5auDxk2OQuF9ADYDhoWtK2w+atLQ9Jc/WrBQE5BmwtTKITjNH/1W8zpN1ulbMEqSOX7cAKDw0G/3h/CdblMJnIgSEHNa4gm0dvJKDM4hiVrHb3rTm+Qwtu3VV1/doMUo6c4kvPK8yY8zHxV4GGGd72fymiasI1IhnFgSEAAwaRNgSk0mAzJZ7DdJEuyhjz9uJXEiRVPffpy0sw0yYe/XPlmpNsQB/MkYksms+XVbrvXCtliqqGNL+JIW04LZr00EjsRfnVs+rhfDru/dyoc5t5zj6jB8Y8Pn9KkdRNj/MhZ8cgZeagH4qFkH+rLopRagqwaMEwRkTRQ2GwjetOwDa2cZyQRotAVplGvNLfvcGcW1cRUXtfZf2AU7ZdgojLJhyxllktjVPi0F6ABPGvzTKGmzthbNVmjGlWCGrUFAR94woFOTcvCezWyWMu9EbjRPhimOVxoA0Dh8yPoyr8E/uaFZLm+fYpbGs7b+dNJkROWaYTLsF7jGVpbj2R+jtGX3PQghqwrMLZn4vuXCgj9+dCUQyKitNL1ksAb+CLYI8EIQph+SzB9kPhGG5+8/jMj35s0appAjTxbmv4OAkbfVAcBnivYAICUX818HiVCHC9Po5HDf81roFZV8fbEuLQGkXCoNqZEh+3lnwXaCgDhphDnmx2ua5hGg5vgjGEhwmh+3af7GH/0JckwSpA7Tx0kDgGHaleZJJZBKoDsSEI06BiDyM9M1ATkpEweH1FjmRzT6CLzyyis1ST6z3vGOd7SQf/TRRxvnTH4aJ40dXicAyPc0MRLBS4wsz6td++zsedX0E7OxYgLcDQ1AAQBlctAPEuWiXIA/U8uLTrxptkMTCj5o+jXJZE4m8P3YDmlDNwC0OIE/kbWAPt3gX+qMsv3BA08ZoE6UktHzLmNB76kRQhAkrkW7B2tVgn8CSHjk6fnTkJEZDZj8am2xussVbXJnV0ppNskNBOTVOhCoxyhF1Czhvoc8OgXIkl2qNbi8tAQJRtVJhdpIvZLZeSznu7xtaP+hPRr8c+PLraGQX3HNoMou1FTOxAvcyrfZpkbUXJYngMpO6SBVnNGbHbTyRxD4A3XoJtD8t55qxj5PlYdxzWtc1MuYwT8Gi/CdGBZR4vg3gMM6ubY2YczMqy4+G2l65KUVyMUMgRcCLgReOMeQ90RbTCZcSOYP8i4Oqm5moaB2H5l29YnZKBtijDMAyBaAf0McUEi7Ck3/ehjKqugWAKRRQfidQ/uX2gYAF6qt5t0MBLI9AACsLi+q2jI0HAe6s+xi39FfNX9cDxCY5o9AIBfY/NGEfdu2bXpMytrBTYoyx5c5v1ueds/JfSDbdumk5VIJPB8lcODAgcBm850TNsnzgfmDytE6iiAawTSCap2k173udTrarxsNvi9ptutmhcUPHJKC+GW0dCbOe1mO7+vnc+rOm+j5LOEut10Q7W5qAMrkoMtNjVQdb3j6ddu9e7eOBCSF+UCZnJzU1/bs2aNk4ivX+20rE/Z+bodMBJNsQxLAn4wV4b9XAcBbHtklrCa+pbmqV+qO+S9XnU1QwosXOR8+p5TowpZM8WcsoAn61QAAEj+lWaW3fhYyeIGAuGSBo9Ce4j7+uLa/Xq++5uCDxSRxya4BHC+QBxm0JprRDinrtXWCVx1psnlVEvE8paW11NCeDEA8L568sCuamFdgUrsMU8chRApmohZdXCmDoCO6rwj+wQ2ga5+GrGwAEV2XV3tPEwcLVZWftpBom/Yf6l7ONdF9PTZWeQf+EHbKq5rcZmn+G5J7n2EppENvy6jWu8UWmUqFLXJPfAebWoFcaBB44QdG0QqkJiAXIzTH7EWtQHn3yrvMvaXNs1+/+QHrGeL7IGrm99rTAUBsEYCbGoDlBQ//f23cOjPHS2rj0Cp1rGSZGnvx43b+eGURp+0vNpoBb1/btCRxK8dzjAQ8nNngdTmx8wSnGbWTGj0E/jgmabpOTVVawfDHuTAX0By7Tq0fmeMnAQAm1uiUcCqBMBLg86ONZ0gY0onlMfilf/qgFGUtQhcCkvw+CkgeAQDpdzSJxKi9f/M3f+OpmBOFX8FHyGdS/CYhg6RoBs1zkqo3pZuQBGSAdwMA7AewiQ8+mj8Q+DMfFIwWNDEx0fiiMDtrOWiWiW9C3ZM42X7okyAhJNkGL+CPY4Ffs+JISfIfB3901N6NVMViUGsAelSGWzNR5Tx6JCNQ1oQUPBgxTxsTK/P0Su8TTLIBTTgWzb9Qpo+BICBbCHnV2++UmfPYKQ8W05GeHVpfZj5t+mueCLFvazPz90D/EJiqkTH4uvOVi89FgoCM6FyAhurQ8Zq9b0PIxTMLOpCgpNb8g1acDwueJMwL1QCtupFDRV2H7haj7zX4CSBMUnmc4J8/NzSrbrgLQDuoWRlQRMiHzyclikBGHebJvERFwtK2QTUH89Bl2Nhnoew1dHwZICcUDIEVSQuqDAMcIhF4od8hAi8MLEYtCQIw1LIQrcCNGzdq4IXbsIBbiKo7yiLzIHmXBRG75Zc7LfNfEVBQAZfrBNZzq8pqh0cE4NJM3qUUTrX5TNiSWauOqegA4HyliKEzqooGCDy1iJDqIRIjAa8EACiscXxxnPFHQJp+AgkGcr1Aixj+aMpHrUEC1OPj43pMpgCgSDDdphI4sSXAd5MkaggHJVrPMdH/bSeJpr58DnHNzufQ/fffry677DJ16aWX6sBaNAfevHlzSxVR+BVeSaRTflsY6cMTKQDYh53mx7Ig9s93E2A+RDjh5pdNE+mnbz+CPRItTWQpE12Z+Mr5ftueCO2QRVCUr1ZB/cQXCkFgAoCSRPszLuBP6CbBv9DudDs1t6jm4Bi8sZLtlKBPeR0B1us6189NfMArV0fnCUgFgQ5mBVyARslvlk18H7JyNQMGYqGBwDAM+IGALI/FoR4XeHbiv07EucKs6QncLDsCPlgU6n8FjIq6WHfkbwEEbZV05yCzVFUDPlpxDS4gOLLvKz/0Cf0CqoAowQ2aATsM/qHrw/3VsayglVj1Cf6Rm62o3CIhM45BtIHjRx9QQ7J5c/NDAI995YBy2nS8TiNDF6VCz6Lq/ZfjNYrmWbkGzUvQnyur4noUNIBKrUoLRvVzADKsQNGrsB5Vn44fjinfLMreubRfnbP3WXXuyduBAzfbajLJ95e0ge9lLl74E3NMAi8EYaiJxV8nQRrMeuPYl3mQzCf8aB6cmlX7Z+YDg9iw/2k57dWt2VUwj80sIwKwpUlCpdg9xaYJcNHD/59TS9iPV/PaUNEDUDQzeeyvHxtWB6apCWilXggEIryE3XIxPDk5qSYmJvSCm+ORwUII9nGfP/oTpJYqg4kwpRqAYaWb5ksl0B0J3HvvvRqwj6s2UyOdGsJBie8wpqiBhZx0+Swy02te8xp1wQUXqLe97W3qxhtvVOecc4666667WsySnfyaxyY97guv3O+UX9Lo95QCgP3egw7+TQ1ATuLCTOAcJEIfymRAvg6GLphgRk66OZkm8GdqQdKHACc6/KLplnqxLW58Bp2T/pYJfFD+XrwubWBfmouodnjtJvAn/An/PO6Uf6EZ1/Z79zzO1W3iiUEqaj4fD7th/lvLB8IvLXKIVTRxEiMt/tgkScAddDAQbEO3lCAgkKFlAE4CTgi5xnaAeXCk77/g4VJBP9eGWMAjEYzCr500QMTATI5D81I39pfBT2Ejpk2UUVBCHkg6+HZjPlg3DsD2dDlA4y6oSh38Azy2K28bfUaZ8Won6hg+WA/8wUKG9p+mYUQCLq/xD/whdZZHmzLNwowZlcsl/y3Hlh/47Cg9tABgczijho6V1eiRmioiOnN1JKPvIZrTC5hIEJDPqRwsoniuMsp8lqbrI2pK/cn3b9A33lhmSJ08Nq5OG1urBmdq6tjuWXXg4JzKwHfiVz/6NrVlg33OIeaYL3jBC7Q2ILUC+bGSmgwSpIEaWtTA4rzFfJ84mpLYocwfwtR9+Y/u110lcmuXqey4tdjcUTcB3l9apUp1ovz4UZp3f6G0O9YXp0C0zUAga1blAAA2WxoWACxUZqB52izXC3t8D/AjKH8MHkKfYgT/jh8/rq1mHn8cc4Z64gI6zjkNaUnyfB9JhnSbSiCVQIsEqK0b5PuupZDPCYL+kkzlGTnn3Mo6W5SPnNc7OSaYR83AU089VWvPX3TRReqqq66ykXTy6wcACq8kkAS/Nsb64CAFAPugk6KwKAAgy1DF1TyOQidM3l4CzThhFeDP1H7kRHpiYkJ/yfRrk0x0ZeLrl7eXr5ntiHOi1s02SxtYJ/tDxlkUHlYC+BP+zIlsu/wLrbi3dz62J26SrvRqXOT4rN857/fCFlwJRjxZA3iho+NGKOfDbgQqyWVtMQNGVToYiGAlzbWUPxMUPPzP+YKApMB8kKPG7vBH9JykOmapYAbhB/QyDwnovtaE9JnQf5xabOyjlXquEWxdBgahtcPCtoBCC9Fu0qTpbg1qT9a9w5ZGSzr4B4Fd0baMVtyeGwBfebX0uP0Sj/LHSipT91u4nEU+42Y2g3+UR+zXWilZZzi2KwDXdCKACQvdsIljJOzQHyzB3Jr9QcCSoCEeRHkAfNXFilo8ibxCU9FoC3lwAwIbTjdBYq5WUo8dP6p/mmfEEBsAsERtwd+78lvqv732HPXWF5+l1iEasJn4nmNQEP44VxOtK4IsBAT5o5YWF3f8dVNjQeZB5rvY5N3cv+2xZyz5ew8XM7vnPv3/MYkJ8K5CEzgtz+fQVR4VWEqonnS9Ljz3LDT42gQARwEgm2k6pAnw40efUdtXmSV7a5++/wg880eXORyTNBMWDUAe06UOx2Ov+q/sLYmm3PS6BPj+cM4z+oHnpHgkgEZLOd7nvN/9Ei2qBFTjMyOJxI9g559/vvrRj36kbrjhBv0syuUYXdBKJvhJfpnfK0mgEr7jzXJe+U/08ykAeIL1sAn4EQgzj+NuqkwOZbIYN/0w9Fg3HWtT48+06aepDb8ahEX5e6EtYdoblEfawXwrtVAO4jHourkAM78QB5Xj9ZUE/oS/TvgXGkltdx2FBkLCietrv+AfdLSFdXaiicEoIgE1iXITE3HIrMUMGOtQBgOhokwU0EQDNmFBQLJvAoF1tIXaUtrM2wGY2FqLwdDR5NoN2eE5AC/dTPRPR59wUceU9oUXklE2KcN7A650qsNopDbZDlmYZaE1N8jy4Yv45GT9dpBDMhNEyx+1tLV095h8Ums0a5Xjc6AyGhz4g3Q1+Fenk0X7I8k5QoPpb1GAzVoe0ZjnqmoZIGWGzKJ+/ezWjSJX9kSenBqBDSDQzAp+qNFZwW9aldVnHrxLfebnd6mhgYzaNIxgDOs3qFdu267edPppavv4Gl2S4B6DNDi1AgkG7ty5U//4MZOLFm7N97xZdVz7MqcLqufJ546q6cVCQ2uyk/oZAXg1HD+uy1pja3exCQCWPMx/WZ/WJm+j4tl5RhweU4dLTaf3YckMwX+nmaYXVsG8eQBDyGPw1DM/PfWseuOpZsne3admDTUC6cPy9ttvb5jPcZ5N/5X8dXNM9q6kUs5SCZxYEjj77LPVHXfcoTXSK5VKS1Agaa2pHcxnRVKJzxkmYhr0oUutR0nkVRL5ednLXiaHLVvhl2BlkthIS8U9eiIFAHu0Y9plywS8qL4rN0679PzKiWbWSpgAc4JKUwVG7hUnoJy8C/AX9eZeybb4yTjqNXPCThmZx1FprVR+k2dZiATxQnOVXbt22Xz80c/jaaedpgO9mKBcEK1Or5t1RQUwO63br/zewzOqUANCkHDSgT98AD69YPO5Hgd7ywiuEBkK8V+7RWeLa8Q4aZKekyaOCf55Kcf4Mk3gLgwISCIC8tWBQGpw1qjQJOc9KqK2FVluVw5u5n0Ba2wPTto/bYF/bIduSTRCbRSBiDUIWKEJe1jTVnSIjv4bx3gDrQo19zzSyMFiUxvUof23zPFQHxPlNeHAP1Zjmv9qM+awsibg6I5TtnA/WBDtP6ttOpIz5DtQrAIUzKga91kqQIbMFQoINDkA4RK+fOwrQKtq/5z6yf5n1GfuvxO334DakB9Vp61dp16xZat6/eSkOnvTRq0VyHkNNRr4475TK5AaWDQnTiLJe9d8F7vVc+WPI5r/UrZayHZqy7ipc6Plhv8/Xt1daJqiFWfd7WZJznuk2utwO9qUWaMOq+gAIH0VmqmKQTi7NKLWjjb9AprXZf9Y4Yjs9s2Wc2OZ05x55pk6crBEtZYxyWABHI8EqKPOvYV23wgkZTSVwAkugVe/+tUaAKR23wMPPKDOPfdc1xbfdtttjfPU0ksq8XkjycQ4eI68SiI/f/iHfyiHti39mz755JP6XJK82irt8YMUAOzxDorKnvnyNU1ho9IJk38lQDOCjQL8iUNPTiC2bNmiNf7anRDLRFcmvmHa34t5pB3krV/bYrYhCEAj8MfgHlNTU43uIPA3iYUUfdqsxOQyCv8Npruwc8Pdj3ahFstHll9FHmtAvyKRrlUJ/gUAU06CgBGwkHRZmTozrvCxNmt2YLg0A6Y2XkgcxN4CyiksCMiSyK/9pA2FkDH5tK+T7XWHOHIF+zqkGaJanYXVaHAJImp3ZLQFzKJe1scItFWYwy77+VfUnDa1/+qHHW3od3E55z6asvMI/LFg2edq7UaH9l9tyIJjqhyTAAfDyI1ypsYu0wDMirUma5iCOj/6KKTfxPxsDVGGm3ARea1l4WJisaYK65rnXcec5s7+py0g0E5CVVDZodKCOnR4Qd19eJ+69JH7qGyr1uSG1cT4WvXSzVvUf8JHrDNhCrwfiyACLqZWIM2dqM0Qt1agzB3Md5mDdX141xN7rdPuw8WtiOu57Oqy1goX819m2lXXAPT1/9fhsyBXcPcr6MqkcbJKVV1HYiTgIACQUYD7MclHfs6vJyYmdFRrauIQnOaYZLAAzsP447yLQCDn5LJGCNPmlZirheErzXOCS0C/6PusjR0+94Ja+9a3vlV9+tOf1tnog88NAOQ74hvf+IbOw4CKr3/964PItnWdz5i7775bl6VVn+nzjyepoUztQ0Yvv+aaa9TFF1/s+mHsa1/7mqbBP7/3e7/X2H8+76QA4AnW+yYAJrb5STVRJocyWUyqHtLlBIS+SKjxJ5GJOGHYtm2bOuWUUzr2jyMTFQJObI+0Lck2JUHb5Lsb/ZJEG8yJoFcbehH4E1mE4V/ydnP7s6eaX9GSqpcRP7VfOq8KsG5K3Px3hLOjkAiCF5+9eh7NamkdsAsG4sjBdLKthOdoWBCQlpL0gxdongomO9X+o382NwCRAA1lkGRiO1Ucs6MOhiGLZmDeXQMgp/0smmCbo/FZgFgEpOJIFQMks9EDHyP1wB88b4v8y2OerEfELY+HC/zBImYE6QzNfzkeY04E+ai153RNQC3A6lgWwT1QJ508MkUcXHEAgVbF1l+yMV0pqOmpg+pB/L722EOaJwYb2b5qTE0Mj6hTIKPTh3LaHIpADH0FigaWOQc06UbZl/euOZ9wln/gqX1qEeZh7YLcJr1cPQDIKfUAIEv4ysAgIEzluSFQgp9tAABAAElEQVRv/3/6RjUpRdufP4ZBwSjPEVMJpt3ONL2wWqmTDjtP245zsG/nHDOJMW6rKOYDAQBlnsxxIf4rCUhTQ4cLdSod0C8Yf1yQS4ACfpT1S/0mD7+2pNdSCfS7BF75ylcqRuGlGfDll1+u3v3ud6vzzjvP1iwCbbzHmS688EJl+uXjuVtvvbUBCrK8CcDxOrXx+Mx4wxvewEPXxHXe29/+9saa/13vepdrvo985CPqfe97n1YEYaCQSy+91JaPbjQE0Dz99NNTALAunTimuDZBpwcrKwG+oOlPhn46TgQNQPofEOBPHBFz8iHAn1/Enyg9YU50UwAwiuTiz+vsC7OGXgb+hE9zMhukwShlurHdN5O89oFzge1sV9LmvzWs3glARoUQouZ3tsv1mEQjggmudMyTpOlCV/viO25mjLgvICA1sDykp8E/+DULZXNHoJd8drJAh+xIoiXFLVNHBbqdbYwhBxl9GMUHoFt5tp/KRoMIuFvx8As4UIL2asVVUm4k/c9Va6q22n1amJ8qA5C0hN+i/QeqNKFlKo0CkY4A4lWM6L80Y/bodU275Q+qCpPyc3gyUEutDlBKGZo6Vzmm0ekiwbAagEJDtnEDgUJXb8Ecg408PncMv/oViCqPm3EtfAv+Vn6NOgdADN1g0IE7tQIJ0JjvUhu9gAN5b/mVv/yHMP+F/KkR3GlyBgDZUxzDo9PqkaKf/79Oni9g+rl9i2oAAGDUR8piDeq5jhQmEvDIUEndt+859codJztK9+6hfBQnhwIAmtwSfKarlcnJSQ38cVFPczvO3el0n7/x8XENUHPe7gQKTFrpfiqBVAK9IYFLLrlEB98glvCmN71JfexjH9OAHo+vvvpq9eUvf1kzSg28D3/4w5GZ5rr+jW98o3rpS1+qqHH4ile8QmsNMxARnx933nmnBh+5z/TiF79YffSjH3WthwDjFVdcoct88Ytf1OXf//73a23ke++9V33iE59Qs7Oz+n34hS98wdOnoSvxE/ik+0zvBG7w86FpNAPmTZq0BqBMBuTrYJyy5eSBEwlOHkzgj1+5qfHHSUecyZzoytfvOOl3i9aJ0A63NvQD8Cd97Ma/XFup7UM79yvoaiRavURJ9awE1ZMDWWh75uvgQhV+6bwArA7Itlc0oYa6mgFjEV7FYlwHM2iPWwgODMO31bILCMh+074dwyz2CabEMNTioBFVFHGCf6y7UwBQ+KcsGBzDzS9gFqBZXENtmdFxXdJAmYE/mqCHU/tPa2ti4k75VUfC+/5jVeV6VNQBgIva56M7C61cQSPRU/tMD1iMQzCUXbB8/5VXtaKF7J/yavwxO6rDsZsoEGhKAWwXIfHD6JubnptWL4Hp5TBM8xm9kb9O/LLJHMh8l5lVExR6aO8B61SrWM2sofZzq63AH2IC/EwI/38k3OkzYn6hrLYDPD1QjPb1ZKZMX3/2OeixEJGAR4eK6ie7nukrANCc38uc361T+eFz/fr1+keTPLrq4RyeC2/5PfHEE3qRz3k886YplUAqgd6UwG/8xm+ob3/72+qd73ynvn8JADoTwb+bbrqpxSzXmc/v+OGHH1b8+aU3v/nNiqbIXtrtfC5df/316nd+53fUfffdp7773e/qn0mTmAE1A3/7t3/bPP283k8BwBOw+wkA0iykHwFAgn0C/BEEZOLNLcAfJ7VJJHNiY054kqgrSZrmhF0m8UnWFwft+cUltXqUEQWsZGrQzc3Naa0GLmgk8WsyvzivlI8/4cNra/IvmhReebt1/qb7LOe3SdZX43rIb/FOrbAYFot+bagxcIIvEx6lO1z0e1BN5jTxCmcrca4Mn/mZaOvYVv48QECtPVUH/2pYdYvfQW55zdIIwxb9Sy3PkUMk7TcYWqtuOePRJ1z0e1xqIRHlRNzgn647xvFOabb4BQQIRgAwlgRaFYJhLmnkMAJ/1Kux+tqer1Y3Ty6tDW/6y2pqGD+1uo9DHf03guYgwb0GYEne8NNm5zhPP4b6QwCAqtwS9YJRjyNyK+uv4pyMWR7rVG+nHLa77QoQCF6ziwMIMLKsrr77gLr0w/+n/mh6+PBhm182agXSLxuDpJlzBK+2ydzBK+/ND+9UZchWA7ARxrh91Fi1Lw/WVHa0AmXCmtqWW9Anw/j/0xl503aYThocVwdUtAfnHEy0hzIjqgSNWUmMBByUCADeWtdoCcrbK9fN+bA5T/bjj1p+/FDPH8E/zump8SMWPdznYp4agZzbm77L/eim11IJxC4BPu9jeubHzpsXwS7x+5a3vEU98sgjitqABPp4H3MNTjPat73tberP/uzPPEE5L9blPANx/OAHP1A//vGP1f33369pHzp0SFsuco03OTmpXvWqV6k/+qM/0pqIUs5rS3+4d911l/rKV76irrrqKm2eTAyEzxhqGtJM+UUvepFX8efl+RQAPAG7XaLkJG0CLJNDmSx2IkoCf2IuIBMOTjY4aaU5S1LAn/AsbeFxHO0Rut3emuBTP7Tj+3c9of7p3+5WGwFa/9l/fZX6zRftsPnHkbDtlKO8FPjl2Gxn3DJeKpSg7n6tmp4tqI0bVqsdJ6/DC2+TesmLGeUuOLCIyVuvAIAP7q5ra8QtrDo9LoMCzX+ZKcJiMSqrNSAUejEfsSBKgS23pWlEQs7sCZDUVZAuf45JIAMpLGMdG7VaMZuuwRSSYB5BGcpxmYiP3tobpoEV+6nG0TI0CCtYB2exjq+782pci7qjtcHcCjna7ZYl6jmtvYq2R5VdYD0xEyS5LP0CQkuTvvNii/yrG8L+rqO8RsMyi1WVm7U+xvG0m/bfcs4ypSUgF6XJZX73AVhH1cHBIu9Eo7Sxa7CjdzUIyeAf+J+dr1qglg08tAqLfBiUxM0suTKM0/wwYVTQqVaZQUrvJgkEZpZAHcKgPHZVFtVNtz+l3v1fz9OBQky/bKZW4FFYVL35v5zrC7rI3MGcF5ntuuKH91nPBlNwZoYI+zT/ZddtG1pUuTrKLBGAS7P84OtdSRx9lV2iDXj0tG4srw7NNB2vTi00oxZ7URuFCfCemRmvyz15XubjZM5rPPgxznnb2Wefrc444wzFBT5BBAZt4/rk6aef1v69qTGYplQCqQR6TwIMvPG5z31O/6Jw97rXvU77O/Uqw48ENC3mL65E8+ELLrhA/+KieSLTSQHAE7B35Wva/Px8oq2Tr4GcLBLoMIGPsBUzoAeBP04KZKLBm5igH8G/bvkLMSc2MvkN24Zeysc+YFvYhl5vx/duf1T90zV3quLwgJqfnlEf+fL31Vb4r/yD106occO6plvAH/uR4/gjf/mvav/hWVXBwvTY7JJ6fPcR9aM7LA06LjjyQxm1dnxEbdk0rk49Zb0649e2qF//9e1qzRorlKV5H/QCAFiFlsKheSAy3uuojoewNg/1A/cgNy5SE2RBRxKNBj903OxAAhakEX+rW7SWyAnkT0Alt4RageMQ9NAaethqP10C5oEdDaAY3PvJLSz3y7g5NAiMAoXN0EyDldygBFYw6gq767W4H8A9GudISgz8A5dOOYdte1A+KE2pAfgFHEBfx5IgUzcTWZr2jhxcwjMagwfBQTQ4PJS1AGKAfWwfxyIj/lLDTgN4HDD1QdNof/1Y82ru40T+eP0EfAhGbg3BQ2227CDKivAOzBT41YFmyWDSkSRg0UAZZc3ikZlwEPY4jBsIZLTkQctyVvcBq/3S7T9X/8drXqS2bLS05CcnJzXYwjkWwZejR+bUVd94Wo2tKzZ8BbppBcrcwZwXSbP4Ptl5ZFo/b/RzXy6E2brINjtuBdQ4Jd+cr4oGYMnH/5+uzureMDV75pk9AgR4o+dlzwtrVw3ZAMC54rAqVTJqKAt6HikHh56L1YW258seZBM9LfNyVsK5ebuJ6wVq4/BHzRzRCuQ8P02pBFIJpBJIJdBdCbT/NO8un2ltESQgAGDSGoACAJI1ThKiTA4YOWzv3r06ephMNlmeJgOcEEShFUE0nlnNia454fEs0MMX+gEA/M7Nj6gvfednqgSfRXr1BRMygiX7S0vqkv94TJ0EEPMtr9qiXnPu2WpiYqItcLmdLvqHz/5A7T1YNwdqLC6blLigLZSr6uCxef176LH9Sv3glzoDl5irhnNq3dpRLALKauNJefiqfE6dcw6OsWheqXTbL3YnBkRIm+h7zzdRy8ZcZPtmbu/icq4+liIWT4wtEm6PpdYWkA5/SBoYAzDTIG2cL68FCIiAi36AnqahKcX3h9xQm0qAFGqCFTYuq9FD7UuXZpyuyeO0a96AkzXctO1ojQaQbV5uv/lNGh57HAfL+HhSBQjIQCEdJf2sa9X+G5qBdhZ88xUmRzGuWq93VGenhXEPaC3RDDrRpf0ZyEXfgvhTG2oFACsezywv4LlTdqV8LEAg+p6mv437vD7OKtCK/PN/vE5961Pv1tXxYxTNf/njnOsDf/xVVYb2aKlY08AgNbH4kZVmmJx3ifWIzMnMeZHw/507f6H0fcMTrWKVbKG3Q2MWirmjrjI8g5Dm0/phAh+Hs8aXQCdFPgf46zDt27egBjcOQAk1GrFVw877YUBNL65Sm8f9g22NwAz4of0H1W9s39oh590pbs6H3cZDO1xwjUL/YXTl0u25fjv8pmVSCaQSSCVwoklg5ValJ5oke6g93QIAzcmATBiDxMBJ6J49e7Q/ECkj/kI4CV2pyUA/ac4FyVj6ReQblL/b16/6/oPqyhvutybcMB1rJPqRwuKEWiaHcfXrtz+nbnloRv2/7x9VZ71gcyNbUjvXX/+guuNnOxtIFQEI7fssZIVURpgrlNVcHUB8GhEG737oVixSfqKyWKSOjea1SfHJ29dqk+IXv2g7wM0NiYOb37vrsZAtaC+baNL4ldYLdaOr/fK2c61K8C9phLEdxtopUwGgwzVx3a8aSdTX9wa11jPGxa7v0hxVOdbD5TUDqjxLH2xt8uqxHo8LoEkc/GMvYEwSWEhq6BMAIvi+DJCO/gHbSgDSKtTwc6QBjMNh+P4r4ENGYg1w1BnlUPv7QwHXYULtP0RHZqrRVNiR+KyuwbpUm/86L7sSdBCI4bATIDBTQGlDu5YgtqSd5QX1L9f/TP3xW8+VU3p7zb/8TB2ZtkxWTz/9LFWuzGqtQLpfeeaZZ/SPfnVpfSGgj8wlTELf+PHP9QOpod1pXmxj3xkAZHdhXFNZrsK34QJNgL2T0WzvTAFXlgoVtWN4rXquAK3GCGnIxafkFAKBBAGAo7mSumXX7r4DAGWOHEFEgVnFWkK2gQXSDKkEYpYA5xNxzSliZs2TXL/x69mQ9MKKSiAFAFdU/MlULl9xu2UCzFbIhNGrRYVCoQH8iVkk/fpR44/An6lN6EUj6fOc7BI061XgLGz7ZdLei+248nv3qav+/SEs2mAu6OZ6hz6kAAJycVEZHVTPFBbVBz9zg5rcvFb9+XtemxgQ+Mgj+9QV37xbL9hFzstxAUqgQ3Pi6fmC/j2556i65a6nrWow+RjOZdSasWG1edOYOmUHTYo3q19/yclqPfwPdpqmjs6rn++ClqLLAr9T2lI+yPcfV+jsT+c6W8rHsa2McNUevQaOw0QnU9FZUkNwEVUbaaNgHIJsgwbBVy9TQG0KvIcT7Ojt8ewXdnUHicU1sA+WonPVRsWspEOe/WolkLQMkKsM/4uZxehgo37e5h3oLSrMHylq7cjqCEx+43oW+jUkyjWAlg2ZunRiFnIQsZdd7iWtscw2ufWLHiBgxoVuFBbD5o0KBGrTXyfY60DC/vet98MU+Cy1aYMFpu3fN42Ijvc2WBocHFYve9lpOlAIfQXSRJgWI9PT0/onGTlvM1OhVFZHELRLYeUQ5eOYScO2j7DlmWFLfVM0AHcVLV96Qf7/gKzHljaoMfWcigYADmZbB8/0QvA7m4FAHjwAR4x9kmRuv1If5/tETCmbqQRSCaQS6CsJpABgX3VXOGa7pQFognYySXByuLS0pL8sH0TkMwH+GI6bjkW3bt3aE8Cf8Mz2MEqZV1skX69vexUA/PK196h//dEvtPj0GsvFLIsXtSN5goBY5C3DL1QJ64Gnp4+rCz99g5rYulb9xXtfp848bVNs3XAM5rx/96kbW9eC9C1F0zhDEyu2SoUQqihUqqowvaAO4ffIEwfVjT9+VF/lmm4kn1PrYVK8dcsaNQltwbPO3KrOftFWNTLsrxlBAgsAHD/0nitV9aW0zUwmaf9pYsbtUQUXrEkupiWIRbvrdS7AE0sc6FHIY1GbgUusZXRZ6/IyMS7bJszAK9Sk8kqM8FpcD/z5mFcO7/NeQUA8gUFvUo0rGjPo8qxHY5/d6Ew8p3QAFpi+0kdg2FStR+E182cKVZWH+e/SZmr/YQA7ACYz70rsOwN32HjAR7xBaETqRL7hn9BMvFJBs+jf0O3LRJTb1aTb6X4oIBAsZ0zT33qlTny9DED4Q5+7Tl31yXfrHB/78NW258nxGTjoROJH2MnJSTUxMaGBP/EVKB8PGQGS52gevGXLFnXZjfdYwB+F2IoZa5q+f1jOSLnxUkNxW3wAigZgMcD/XyfPAYMFvTu4iIeCfZg4s7Qc11zs7qdCAoC7j0UDG1sq7+IJmQ/LvLKLVadVpRJIJZBKIJVAQhLo8lQ4oVakZG0S6BYAaE4IZJIgjPBr8jMwK6HjaQH+hoeHG8CfWVbKrPRWeJLJ70rz0279vdiOi//lJ+r7da03al3RzNcvEQTMLEEbk5FJAQJSy4z7u47MqAs/db2a2LZOawR2CgSWAb79+UeugV8kjxUzz7tExvTjPa5r5GihWFYLh46rZ/G79+G9St3woF64ZqGtunp0SJ20frU6efsa9YIXbFI0KX7BCzZqk+JSqaI+9MdXqmPDWHFR0yWBpDWHNP2AlRMXfcmwoFtFbZ5EQbxOZBex3UPTFugdp3ZLJ+z7lTWDfvjlK66HeeoccJhSRGE4wIJGHV7nGxncd3g/MShKRC7ciUU5y9vD4/EShUyYvDSlYzToDCLqEkgOTPjQUl3luH8BjI0cKOB5i8i+eetB7QXGBtJPIgM+ytj60HaAIDgLtcZ1N3CzSsCaH3dg4uz5aEz4meUnFj8gkKa/roF10IVOlp8uLahv3vAztbh3Xh2ZQhAoI80iuJWZOG7Wr1+vf5y73X777Y3LohX42GOPqevvgeZ6va5Ghg52JADIOHwerMlavgAlAEhRRwD2Id7mc8CN4uwRfKXa7HbF+1xJtd5gNAEOSiOIBDxTLOh5cT+Yvsrc3vzgH9TG9HoqgVQCqQRSCfS2BAKW4b3NfMqduwTEBJiRtpJMBJo4gSHAJ6AZ6xTgT+om8DcxMaG/IAs4Jdd6aSu8SVt6ibcovPRSO+bm5tSnr7hZ3fv4kcaijCuVGiJK+iYs8nILyIixVYWj+/IqjDMs2sqrAQbCt9PuA9MNIPAv3vtadcZkexqBf/3X12mzXE9eHItLz3zdvIB7rgK5zCwU9e/pZ4+pW+/ZZXGA82ugNbimXFPTxxZU4exV8XKGfsnA/CwLgEFhzVQ4PUBA7EJkCcjVEY+1PCpps4Yk+YrcKIBEWazLawC7qd3CVvVqIojPKMOhxI7xWsDietVeAi4hJY5x7JWzHc2fFQP/ICKndlbSfUoAiT4Zl2kSzEjBPhUyjxMFy81WVBbRcxc3YyDKY7pXBiPHhQNMtbFWAfhHjWMknq+4mP/W40v4mqX3wv3nBAKreM4NOk1/dUvrf9jRNmEo9c+33K/WPLaIMWAfBaIBaBaXfRPoeelLX6qOHDmiaMExu1hQi8uW8Ok/s53kZLHh/28IXwiQ8HpRz8AEuFbBu57+/+xs26ps5zlgI2AcPPvsgspsAeBdb59xyXN3sdbaGWE1ANlNvzx0WL1kS0TU0ZOb5C4kBQCKYgA5D/1eSK6ZKeXnqwT4gu72S7pTWfcbv522Ny2fiARSADARsa4sUdEATBoAZCvFbJb+BulL5vDhw43Gj46Oao2/zZs3KwGlGhd7cEcmvjLh6UEWQ7Eksl5JIJPA3+7du9XlN/1SPbp3oTGPbwAHAS0ZmsdCDrNkzv8HCwSdllV5lA7v4VSfZoXQBszC1G33/mn1wU9aGoFRgcD//aVb1WO7muPVlSWav2HR6Vwku+ZdqZNcFEOTZxCg3yAiFBf2YzEFv4JM5bF27LQcDSHoB+UM9sEglB5kTba0DvkCzKMTN/+FCWqiUVwdokjyMIduI8itE1eIPZwIKCDQtVIMtABMoLEY1/uWhi+17ahtpTV4oaVZQoTifD3AdlDTnCCPLX9E2RCyWBHNP2G6TbBEirezZf9Q7pVR3LtefgFxX1dWO5jDc2T4UEFV4J6hSr+AdcC20b/tMBNjGY4Lef40yBoncotN7T/tow4fKc3UCFjEZzoXUUZZM58TSLNd6/KBAIGDMP31S/o56IiGXMa7cmFLVq0+YL/g1AA06ZrzBgYFoauWM888U33w0ussQJj3XwyvFdaZG7O06HbkrY/VB8ujqgDzgDK1/4I+FvDGjikV8d6cyK9VewtToSker+BrjaI6aTOFAQBXwQcg0y07dz+vAUCRWgr+iSTSbSqBVAKpBLongRQA7J6su1ZTNwFAeXk/9dRTjfax/omJCbVp06a++rLXC8BZQ4gd7KxkOwj8UQOUWgPfueuAeuo5y9dQozmYtNecJmeNi9bOIEAFaplxucO1BrfAeVQeGoFVgH7UAqQGYYWAIPzP5XDumToQOLl9vfowNAJfOLHRIubx95ZbHlM3/ehXwYsMLkKwKFZYTPdaGqC2C0E/bikoLGIGF+FTCfvwZAnNyYwGS9viGwtkgn0Zgq8A/9xaX1rrdtZRG/kKkc1RKvQhzR2d2i1hCxOM7hVggzwPHcf4Fo0lyq0fEu8PgAEmu6K7R9nqyLT0SQetLGoNDRThsZFRWQOAYxtBhxw4nKg9Iu8ex2XbofZRSRzLdra7Byv6sR5yrsAqkUExWv0CooMc7g2Gj5UUXZstbnRoX5kd3F3xNWvToF3zsGWPz0ED56q6aJlXAERrYAnPdA5dz9QL7fVkzuMCsU6j/ZKLfhzz8PmXI15VT8dnjAM5Wd+aAKDMJegr8Bf7AY5hxaBFY8dVHRTCHQ7kqiqbtxg+JW9pAIb1/8cafD8ShGPBlmudWq32qvAA4Gx5Ce4l8zrAlxAqIbLZfDGvVudbtQMlD02AmX6+/4Cc6umtfBCXD+Q9zWzKXCqBVAKpBFIJhJJACgCGElN/ZRITYPpySSrNzs5qDa9y2fqCy3pY78TEhNq40fJDllTdSdGVCY5MeJKqJ2m6Mmk3J/JJ12kCf6zr2z/dr3YdsC8yuGjXZoN+zCBPbq6pxeHMiqCBKjMLzRUsHsrQXtFBQrDAJUilNQKfm1J/9j+vUwQCP/Inr1Onn3qSk4TaufOIuuSyn1gLwZarLie48OyRNACfhBr0A/DXAK/A30CxogYK+NX5rGKBW9CLeDkTogGgQ6BG+w8jAOvT7BKANw3k+JFFX/kp2fgVDXttGRFowWnY7C352gUPWwh5nSBrPnKUYtl57BGRqDcl7sWt1LMiW4BQNUTMHCyjcTA5zQLgrwGUqxEI9Eh+Y08XCdHtvQD+aV69m+nR+nhPc4y3+AXEvc4o62YaBDg7BACwgojh1SF0kAHS9sJ4FO0/rfjF8UP2+YPWXwY7Q4bvPw4Puo4wE8tJwJoBDg6fxPEX4rb1odD9S35A8+ypebX+iQKe6Va7w2oACsi+87mjqlS/Kf3qCWy1IVTx/8cyO4YsDcDd9QjAxTnYsAclPRCCMoW/PrCA5RDGVdjEpqxfnVeHZ+2RkqkF6AcAMgow066Z/ggEIvNhmR9r5mP6I+NLtjGRTcmkEogmAeO5FK1gmjuVQP9KIAUA+7fvPDlPUgPw+PHjGvibmrJ/Kd22bZs644wzQmlleDK+whdWAjhLosndbAdNv2nqS40/JoJ8V995SD3jAP94jWaqQdp/BPEI8gUlmh9mpmtYrFr+Aat5aAPCLJjagNRc2w0g8E8/ca067eQNWiNQgMA5RMb96N9cG80nPxfCaJe/ykgQxx1cB+inzXup4eKUDUG7BWj94ZozFdfbzZOc1+V4AM7wBfRr1RKSXPZtcYP/Apq5tTZOcDY74QhHVYJ/vmo8EYitcNY81oIMctNInJDyl6D8GnUlvYN2DC4heAG2NFPUUb4xbnmvVxiBVsyeDT5axrlxjbtBACHBoV4xDe8IMHG0u91DgoD0C0ggln4mB2H+u5y1ox0jMP0lnra0Dhkd404fdvAM1E8nEsdPA3cgqE10uWVfsQLZcp/nuWXSW94Msi8XcAyeMojymz9kfzbS5Nv5bGho/7EdJG6QIWlbqldnO9fjB5SjV6pBG3xhM0yBD1oad8ePe38cNj8cylzif3zth7p/+ExaNp9TXhWGOJ8bszThmFU0AHcVxuv+/1BJQP/4NDdE7a1Zpg9j4rC19bzfmTWrh1oAwGkEAjllvXfY89G6BuB0wQ4c+tWzktdkPCQBAK5ku9K6UwmkEkgl8HyWQAoAnoC9nwQAyEhwNO3kVtL4+LiqVCqKmoYjIyN9Df6xTTLZlQmPtLPftt1ohxP4o4w47r5287MA/6yv+Ta5YdGlIzL6ADYECLMM/GEmLgIcp+QyL+VooopfBUFC6PC9DPPiwbIFLhDM2rXvmAYCX1AHAv/Xp/5dFWAqGykRAMSC2Q2oiEQnSmbUZ/n0QyOw77oWIjBI8I+8uSRf/3+UNRQRtF+/iOKgFiejMgelUHgBMjHyrTYnNjSOgmjzemWE7XaVTJjiHZQMRd7KRPbcu6dBJENABmO2Yf6LK+23qkG2N3bQ9kGscwn+6SQNw3OAIE0G4LNi2wkEmv3PweOXfC73Evhna7NHeyzoHvd7wr2utV0BtlZWQXhFO3ySm6sg6BK0qkcyDRNtipigEiO2U2Ozgu8JFrBmndeAE7pNwDuyr7uFfcyfmZzH5rVQ++4E9EcGfAQqbBzUUeOhGK5TZcTePvJFAFQnMOzzGqrnqeftp427iBotWNo8BB+cS9oUeBZbr2TOf2QusXtqBi9blKAg7aL1IhN4XgBAOKpQW4csQHJ3cVyVjtP8PKAxeqAFVhEpw7P75lVuWwaPo/AvxNW4X5wpyA+gaADCGYJ6/PARdeYmf3clTvrdPuYcnykFALst+bS+VAKpBFIJJCeBFABMTrYrRlkAwE5NgKnNJcDfzAwmgPW0Zs0aNTk5qegg+uGHH9YAoJgJSJ5+3Mpkt9/bIu0wJ/Jx9Ycb8Cem3//fV3+qdj7bBIilTu1rDQv9aoDvvxwCf7SsLUJM9FlmCMAhNYpKdf+AJTzZsjp4iLUW3Qkg8P/5+2u1FmIGBcJqukkbCLJpjRU5kcS2DvrRxJf1+S2BBkow910se+Ypw7yvYaKrNQjRZszjqSk4iJU7wVZdGBuKWGtUBS266m0u0Ko6KC9RjZbOrBOQDdpIzTcGhhiZqqnZUw2eJY/HlounjgM7hBhbHtXHejoPZWoGtmlJumNazvbViQEo+Qwa62kd5MRaT1rtqIN+2iwY40XkEKThx+vO7uOxaJW5SHPF5NbQZHPhoAJQamk97gHcB3yGJfmRgfJZGoeMoC29TLnjv4B4RPimzhxrHpNXLcRekqRDgASJ62OLpr1zp2XV+M6K1iCvIYiJmRiMRmQbpF3KckHjz6TdK/u6L/2YwTN7DqbA62AKfDwEAEizTP5+eN+TqqoRfJjy28XqV5v7NQ5CJG5yqy33Mdtg/puFwEsg/mxxlSrNClKrs7r/qdNxv9je2QrmKKcgEMgzBW/tPSflIRc/k1PQAPRLAgAyz807d/c8ACjzyBQA9OvV9FoqgVQCqQT6SwIpANhf/RWKW/EBWCwWtYZeNhutmwn80cSXpp309Sdp7dq1GvjjVnx2JAk2Sb3d2soERyY83ao37nqS6BMCf9QANaM8c5xNThIIXq/++6evU3sQiMMtccFFE10/0Ij+p7JNiyA3MoHnCOoNwz9gNVsFEIggIdAC0UFC6ACfC0UueBkZc3VGm6dmlhA0w1K/CaS9HAR4BVLwyEDQj6a9BP0AygUut3Fv0tffIHz+SWITqqNZAKzQpRjGFv67qqMZNXLYMoszaVZxkIOssbST4nrL9dQy/LNxEUnAQoABAS9kvcU1N30vBqVA81+0NY9vCjrACIkDlV3zTE3NbUOfQZszKFXhzN/ZhqAy5nUNSpsnktoPaAoBMppklsZaGeB9E7ioby3WM2cGaKZvrfEbPFGTzC1ps2BcyC7VYMqPng15XwotnT3aa06KJr91GQPkl0C6pUmLDABAdH97yKdTJnmLLW4GwMr72w1sdjwPOq2vK+UhRNGa5HOdwaMIAq7eZyDOdUYqpsYyhe/SJzae5YFnO9njB8GPZR0YiqbAAwdLqoyPSLmh1ptG5j8yj/jH6+6AoC2BxfURbDAPoHZI37UA3egAVam9xdXQBRxUxRAAYFIA7dplgnfhAcAMTOqdKUgDcCTXnOg80AeBQOSDuMyPne1t95jrDEmynpDjdJtKoFsS4LMkqedJUm3oN36TkkNKtzMJtL79O6OXlu4BCYgGIFlZWFhQ1NgLk/hCPnr0qAZ6GNRB0vr169XExIQi8OdMMimQSYLzelzH5I3ThcGkgBjSHrRm0DIBjov3btOJsx1+wN9JJ52kKtAue98nvqP2H24CxWZ72WvavNEPNAIINjTXnAya5dvZzwAbG54BkABFApoEl8ZgHlaEhiA0AvWLE2OI2mORgEAODfApC6F2+GqUwVhuBPIIA/qxatTPqJY1aGIsrxuGphRM9bDo1cAk2uNcz2qFDYdIeZhhAAYs5px+Flleg3ata+cG29yZ34yc9cWg7YLjgHN7z1sVmhbD0PojuMU+kbQMX15jB5ahEbWsiuute1GuObe1PMs5W+3M5X/cKjX//ElcHcZaUy+q3YCfpmiSqDpZmtQ2tXzd2+rRJqS2M/YDDeTwPuPzHhuvhSHBctFwJYzQsTaonY14jzBMzdFaBnhdgNafeYNQI2+QWtJ8QMUccZx1a/APH2EGcP/zuO8Tn6HNbyC6OTRVZuPmT86oYbgVkPu7inurMe4wWDyfS4ZQ+nKBFfJxKKbAx2eX1EkntX55EGCG8wjuTxVwI1ODkgPH/7FsSNB/Nzfe/DKwow4A0vy3hkBB5cUA/38gHfUDgT83xtV5DJYIPg6XXb4gBgGAGbyc8/jaWYRN/c5puy9tg5Oe2U1NgHumK1JGUgmkEkglEJsEUgAwNlH2DiETAKQZcBAAyEkegzhQw4uAj6QNGzaoiYkJ3/LdAgC5EKxUqxrwyNSBOuEzrq0AZ0mDmXHx60VH2tEJkOkG/HFcTU5ONqI8l8oV9Scf/1d16FhzzDh54kSdvvn8Vl05auj5rEp5KeTaplE989PHHTULK6MYO/hRs0iChGh6GFOhgUDkVQy0MeiG1DSq9d7RC1boN1DTDwt9Z3sIYtQI8EE7p5bDFj8CQ14An1TkpCPnnVvKcBmr2gyiX3ppYTnLuB0XQgT/0GZ5HgtFAhBD+LZAjS/6buT6yex6nufiPVuoqoWtIEK5O5IGQXnJcb4nD/2YBICRg+srT3+KpmB6snEeTPE2od8/l8sEafi+8QL2dBH0eY0awwACCdyzp51ipEavvmdwtafBPzaIzxnce2zJErT+atrC0WgRd+vjWbeLD8MQIDtJByXWKeBfUN5+us6PFaL91+CbICo/cGDsFNbhI1AdBGwE/0DG0MARBddnSb9XIBdtZu/HO8bj3Cl5tW/flCsAKPMGziM+f80djfeF1gY3hq1fFV7XWJz4vvj/Y75Thqz5w67CGPz/4eZweea30Euof44dgnbeyS21eZ4oUc3ZkY4vjaoq3rME+rzSKkQCJgA4teTti9GrbLfPy3iQuX6360/rSyWQSiCVQCqB+CWQAoDxy3TFKYoJMBmhBqBX4kKMJp0E/sx81OyamJhQDPIRlGRS0A3QLJfJqNmlohobHvJfQAYx7XFd2iITHo9sPX+6EwCQ44Cm36aprxP4owCWimX13o9fo45Ne0cT5JI3SPtPR6CF7z6vxAWD91WvUs3zBCGGADDSP2AZ/gHLAALpC4/HjY/3WHCEAgLDLEyaVRPp0GCfBfrBax2YqQ4D3IMJsgb5GP0UP8vMthXkICm2P55kaUwNLACA1ESjU0aARg/zQQeHQGbcRDUIMCfHtR7bDdmwD5jICZ9FjUJYyGcBII3vrqrZCaCgDjCkMsoy0fnXldX/dFbapBSwr5vqDngNU/kDzWb0arekTULdLvTyOYJ/WNO6gX+abd5rAMNCyR/9zsAoAwgSwueIDTSE3AgA9jz4V++rEiwLy3ydut0YbEf9uwL7XPc7VdA88tZJBm5AoRX848kTIGltZZd28AMKx4sGAWGwMDSDfdPkmYMmzODrQznxXhg5XFWLmzCY+Iz1SVUEr/j0N29W337Zn7TkkvkP5xHX3/9o42aO0x1BbqwJnJkagGHMfzXD7McE0r7nFtTQKVn4I3Sol3rUtVBrVXOuQVAzS6vUhlXeH0ZHGAmYH3/wAtg1Na1OWw/EukeTzO1lfhwnm/JMl22ctFNaqQRSCaQSSCXgLYEUAPSWTd9eMTUATWBPGsQJngB/ZqCQTZs2qYmJCWUCiFLGa9sJ2ORF0+88wb/phSW1fjVQgJhTt9sSM/sNcu20Iyzwx0oWl0rqPX93jZqGCZFfopmejmzqs5AdmnMJ/OFHtM1r/Bifh5lxFWBgGSbBpTF8ocfcXZsFC03w6QsEEtUwgSopV99qQAIVaQ0MrL+W4deOi6blQTxmQdtrSeZ13kG+7cPGWhY80MxQAwt1wCEK0cUtnrCOnQwrdDQqA7CPvu5kYTqI9U8DgLWXto4oLwhv7VMAAU8jYNokuJxzqcCNht+5hlD8MiV4DYOFii8cb55o2UrzGLX54NcW8derPIdRhAU8TX0ZeZpjRvvJxNjQmoS8rbzq6JHzbGZlFf44QOwGe/UGCLjCQ9Fu1GOjkTHaDoeOq+ZfrwssTDPxXvFNbCOe0zVEBy7T+wnGi07oDNm1Tnj/Jf7ab4ma3YP4NxoSBNy9vKS++/2fq//rP7/c1lQBAOeXKmoJLj4U3xWQh4DUtsxtHFC0udW4mevp5IYG4Hgo/38sFlqTUyoJua1CPXEyv07tWjoSqsRchS+11i840wv+AKAZCORHT+1UHzj3N0PVtxKZkgQAV6I9aZ2pBGwS4AOp3573/cavTeDpQa9IgMuPNJ1gEhgeHsZEF2AGJsEmwMeJ3cGDB9WePXvUkmF6sHnzZjUxMaFM4DCsSOSroEwSwpZrNx/bNZqH6cT8YuwgYLfb0q4MgspFAQCjAH+s9/h8AWa/16jZ+dYv3yZf1P5jAAACbV5pNVYWtUqA0zmvwm2ez9B0jP4B4dOIwSxKABeoiUa/gY3EewdPxhYfgThfwwJhGQtLgnzalBYLe9HgY3m31rqda9TVhZ0BLOKqCIjCRbE2ndOLY8wg0J6wCdZKoBEiN+Q74MAJs4huSrDVBEFoih2Y2A/wdbhmZ1XNnYygLuivKsG/CHy71cGxGb7lbhQ6PzeEeDlcxDIKrFfqKxAC3TKIPvYFdesN1WAXkbEoifceZaXLAcSHb8+V7sMg9nnPMDptGEYFACRNDW5okJRCjd5K3lmu4B+JnwBJA8E+cpGAIDXgMjracb3NkUAjCrHPknYZAZ4tELCCMUDU3Gf84J76x/+4W73xvDPV2jXND6oCAF79s70N0E+/4xzP9XbFkxlBEKt68Iw1eDGsyZbVHNSgDy2uUpWlEKg++ybB/hmvErEPBwAeLy+p7OAQvIPYGbIiAR/yFJEJAN7/3H71Ac+cK39B5vYyP46bo1T7L26JpvRSCaQSSCUQLIEUAAyWUd/l4AuVWnwM5EGAp1AoqMsuu0wR6NuyZYtuD/Pw+NRTT20L+BOhyKRAJglyPsntcC6rlkpldXypoNaMmOH9Oqs1CnDWWU3Jlg7TDo4Lmn4fOtScpLqZ+pqcTs0uqvfB59/8YvPrvXnd3KemGf3ueYI1mDDXZppmQGbZpPe5JGLE4cEpAGPgkWbBVQCABKU06AKAjLDkABdPWPRUMd4EjHFbTrmdS7oNYelrsIsM4n6XKMMaeEK7fFwUtZBf2AoioBGUtGmesVDMISqzjsBsLETpp0ufCyJWv17LZ9TYvppa3FhTs5M8GcxHEGlvfcygkm1cd7KLtWIeMXO4ZNQAkRdJ+5rSK1dPnGc0Y5rWh0laQzZk3hZ6ehxZJuK1IYzuAFPHlvJdOMGAPdrvnHEfuFZrjAsTAGTehhYgR4lx77jSMU5yyPiCf0adRrH+2SXQEiRXtIYaovQ3apMdhRMyyfM+ZPbeyIbnMyO5W/4RM2r0UDAIWIJ59J9/7jp15cff0WiDAIBPzUC7jQA2kvWRy9rv9G9uvDl/kAjAu+H/rzyLykK8Y1h/iCHQNpu1OVD3+TBjEoZjD7VuVV4dmYPPCiMFBQIZpQlwPT09RV8QvZtkbi9z/d7lNOUslUAqgVQCqQTCSiAFAMNKqs/yEcyhlt/3vvc99YEPfEAH+Tj//PPVRRddpEFAAn+jo82vvu02LwzY1C5tv3LrVo2oPUdnFMHAfDaeYbxSbfFrZzvX/NrhBfxNTEwomoB7fY09OjMPzb/vqKVCMGhH0GkQ2Srj3qvNLExxVZAplzSeZCIs3qRY0JaLiEHwQfPU8qoBVYS2Ik2Cs/DNo3EFb/aDSPfMdfpOq0Bbion+Fhspgkzhz1yV1oQTBmuQnPS/pRfSjkWd+P5r8BJihwFS8seg/nW6UA9RqFeymEIBTzmAfwR4tO8/n+ZYYDQK+OTphSbSDz61fcOmToLQSB3UNKS5cQ2aRFqOEUAyoRH3lt1cwfeo5VaLwMCqnAAg+153O9rpvOZFjPUv4vuezeedV+Y+PZ8hzhIG9MV4GMCHnEbSmqONo+Ado2hw5t7JQeBTPq4MQsM+DAj4q4UZdd33H1S/959/QzeEAOCuA/OKz309CCGLdsa0l1RMAHBH3fyXEYCLDAASJiXcNzoQyClhGLHyrF091AYA2LSgOLrk7Uc5PBfJ5KQVUQoAJiPblGoqgVQCqQRWUgLxICcr2YK07hYJ0OyXL26CgFdeeaW+ngVItnXrVvXKV76yI40/Z2XyVVAmCc7rSR6fetJa9avnDquzt230BK6i1L+SbYnCZ1BeNwDQDfgjADw5OekL/LGug0dn1fv/53dVoRhObUeDTj7afwSiQpmASkMTnvBr/4DzmOgCCNR+lMDfibCI1oE16L+pDsDZHOdHkOniZqwEwwAsGB7a/BfPnjwicCpG63aAVxwbNnNrs48deeWSbBdOQQYHmCjXomwDqolCKlxeR4V5mP8y0UQxMLGfHOUDy3QzA02+m2vZUDUTpIgrUetwAD9qA2q6MYyPdnjTWn/8nha2r5z5LIzeVjXbpYEXrfXmLGDLqr+PhAL//MnYifbaEZ4rYW8HS/uv2QBqPUdqeoTnY7OWld9z+unTIODBslrcioeN172B85/7j7vUG88/U42PjcDNRU3d+NixhvmvbpXL+Gy3tbnVza8FpgZg6AAgCffNc/vnVf7UjCou0w4gOK1CQBVnskyAnWebx6O5pgYgA4E8O3Nc7VhLh5W9lUQblFzJ/DgODjk34Y8fnL0+OsdRT0ojlUCgBPA86TuN74SfgYEySzOcEBKI8bV+QsijrxsxPz+vPvvZz2pQh6adx48fV7lcTv3BH/yBuv/++9UVV1wRK/hHYcmkYCUAQNZ/+ub16rr7HtOTCR53ktyAs07orVRZsx0E/n71q1+pn/3sZw1zXwJ/L3rRi9S5556rzcD9JmDPHppR7/v774QG//SkDgvXCqJ3uiZM+oZmEYnW9eLKnqTmRJbmqXy5ngAv2GyhCr99zcXJoKFxOeDwWeQleSrOlEMqCmsfW6Cbp0UTwT+X1I72n5ApnCR7HW5XsG8zCPxBAJQmdaEBwA6bm1hxDA7fiL8eFdO/pganPa5HPc1nSQbraQaZsWm5RiXURn59f8A3pg700cFDjVp+zmFJchq44gWf+5WXQ4F/yBenKSfIdTVZ2n/hqqQGug3wcgo3gEzfLQjr7XED1wcHsmr0AASCd69XKsIU+EMXX6svl0oVdayMh1T9EU5wO65ECNcEACUC8NPza1WlGOaLiHVPxMWPGx3eapsGELY7ZMpDO92Zphf8y5s+AFn2h0/vdJLoiWNzXi9z/Z5gLGUilUAqgVQCqQQ6kkCM3+I74iMt3IEEZmdn1Re/+EV18cUXq2PHjmlKBIHy+bz60Ic+pP7qr/6qA+r+RWVSYE4U/EvEe5Xmvw/uOaC+fMv96v/+zRep973x5W1/UZS2cHHKL58CpMXLcfLUhO9KpaKBP6mRwN/ExEQg6Cf5d++fUn/66etUucJlbriUAYDm5/svU4QGWLgP640KuRDm0qWD9XWDlteOtTTCF2mse5hoPoYj66Af/2IVUx22L0xMcIQ+Gv3MuigPLiareQTdOazU7HiAEFBgGXUOH0c+DxM91q8X5gGk3C5XoOHFwC2d9ojVq51SceMw3LlhU/svBBsEgJxaPeFqSjgX+rsd8E9zBa2PZaDsoUAWaIzSzx+KBHY+6WWhjVjDOONYCaW12oGYqsDWdWCcEP1oq8YtP2WCBjhlQq1d9r8eB/bbWZPkfRoW/NMF3Oq2MdejB3ge09Rc4XkUlAY4ZkwsiULiL7hokzTz92HyelYICOinCfjL+Rl1ww8fUo/v24exCGHV5eVFsx3xZMYQAAR+CiWJCfAThzfIqcCt8x4JLNBGhpEizJHNMeRDI1MPaGJmWSgNq0I5Cxc19QmFeRH7TgDwvn371ft+0x6N2VFkRQ7Neb3Mj+NkxO/jc5z1pLRSCaQSSCWQSsAugRQAtMujr45mZmbUF77wBfX5z39eTU9bK0ua/f7pn/6pBn5uu+22WDUt3IQjkwICZqLS75YvyXPnvXCHuvnR3eqKOx9UV93zC/X2V72kLSBQgDPy2q8AIM2/9+7daxN3VOCPhZ/cc0Rd+Nl/U5UI4B81DLhQpR8s1wSAKDfXnPy75lmBkxZH1uK7scbuPTZDS4as5xYrqrih7sG9XtLUAGQkzQoWLm4TcPp+qtYjHbMo14FDMOktrauvCOv0bBsAuyMA/7yCMtAkL7dAzU8fGjaC9oOFbeSjvbJ2So11rfN0csdkG51Cn3U6GjIOtd+6MDX24jhkW6BtFyWITEtT6zJpOW+ewPOCHwuqAAwwUvFQRt+xHH8+iZq8A+CvhojRWstSF/IpEPESP4dUqBXbVK6NSME9u/b15/g4wqbSzLmGaOXaZ6oBrnNoRAL/WC0JsmCADJm1lxKfV4wCHYZt+nStMep5PRFEjToEEgeZCGaGBJikHWG2bhqAUk6DgDQH3oKK3QSCc5+96S6VyQKMlpUBx0pTlEKq7W12DTqynrKYLGwdWlSHSiNq6ljQFyYphS1vwIRTdRYjLSQmWcu4MzS9uFptXTPjyumIEQSEGZ6qf7h3zbyCJ5MGAFewaWnVqQQsCfAZx18/pX7jt59k+zziNcZXe29Ibc+ePerDH/6wOvPMM7W56/r169U555yj/uEf/kERHOkkPfbYY+rSSy9V7373u9XLX/5ydfLJJ6vh4WFdz2mnnaZNbW+44YbEQTdpw8c//nH1t3/7txr8Gx8fV3/913+tI7t+5jOfUevWrdPZOm2z1OW1dYJmXvmSPH/29o0N8gWEcyUQ+MZPfl199ccPROqLXmhLoyERd9jPjz76qLrnnnvUlBFV7qyzztKmvoz+7Ab2uFXzq52H1Af/4YZI4B+X6IMAgRhR18v8MzcPrUq3ClfwnH6P6lUl/hiL78QXgAm2eRCgbXmVrOCaFZk+ANlkp2aHBjawNqQGpxPIGzkKSQHgdUsE96j55ywjeTOLNZU/XlMZ7VVezkbbFtdGy99TuetiG4ZytpY7/jQW2AGM9tw4RFsGoWXHIBydpDCBLWjWS3lprVE+OPCjKLVrLtbvPhxxwSrHwCQNs2CPsaszR/hD4LZC676YwT+y4LwfhS1+VKEJMGUhpsBsemTwj+U18OMjOObpuWQFZgrzAYARxlv8t7YzVhMUET8E5Kkp3Q5fAX3j9QyWYoMK5sAAAQdK7pUX8c1oER9zrcGGMclBpweeUOhsm11D5NNK24cWoCy+rHYXx1SBEYDDJH5kRB8nnaaOGpOBgMrKWjW1NZNfJGCnBuCRHg0E0g0AMOy8tFXC6ZlUAqkEUgmkEmhXAq2rxHYp9UA5Rrx95zvfqWgSK4nACP3f8ffVr35V3XTTTer000+Xy5G2n/zkJ9U3v/lN1zK7d+9W/F1zzTXqta99rfrud7+rNmwI+QnRlWLwyb/4i79Q3/rWt9QFF1ygPvjBDzZAP5ZcvdryQUIfcEkm0QBkHZwsmMdJ1mvSPgXBQEaHcmqx1JxcChD4LWoEnvcS9SdvCDYNNnk3Jz5mXb22z/H9zDPPaP9+4leLoHShwHCJSm3cGC1AyoNP7lcfveTfoQEZbZLNRSrBCi/tPwZ/yEUMFmCTNRch0ViyFXc70ORAV4MsaK8NnIy5Lrf6kzhHtrOLVVXZ6FhQwf+fH5ikgxhA68/TbBLAwTBMgQub7VwPImryEB+3hmZSIwfqzC1g8Q6NHAWtksBEkMZFM6U4jvNu9AMJtmYIwUVroQ7PDB9BFGUouBCMYtLmqWEZYYf2UBoAKEeNtE6TBir86OB+1FFcMR6oaUiz3mWOIcqNPzxvlvnMkWNuXRLHvDYLxs1dzeMgTDAbFzqoKh6tPw8+WaUXAMhrlhYg2gtGamjU4taBVqCLGcMkNiYBADNM1e3koVuGqpdWuYNgFlrGldVG49q9f1iOP5/+clQd6pBtycEPKMclo4GXY/6w4acBKAwSBBx7tqxmX+ASdRd8DeAeEV+RYYB6oRtmmxtragCeMjSni+xcXKOqJSDrIRI1QRW0YfkRIOausdU+NVVWw2h8UaPvtkstB4s194mNXyCQVQ4NwApA1wNzc2rr2FgL/ZU8IfNggnTmB/KV5CmtO5VAKoFUAqkEOpfACQMAPvjgg1oDb2lpSYNf9Hv3+te/XvH46quvVl/5ylfUk08+qd785jdrMHCsjRctI+kycML555+vXvKSlyhqVRFgofnt448/rr70pS+pX/7yl4qmt295y1vUT3/600Rfmjt27NDmnkNDjsU+xgVNgZm6DQDqSrv8ZxCTk7OgBfjA7v0tNS9BI/Dyn8I0+O5gINCc4JjRz1qI9sAJN+BvZGRETU5OKo5tBv1gitKOe3/1rPofX/xBJK1J1kHtv6yf9h+AHQb+6ChxMRZ3wgrCAsVA3MGedT7uCpOnl0Hgj/J462PdNP9tcIF+oVEu/fxpjTSNpjSutuzkZ5dVgcq2dRAlA3Avx+8LLqCKNvkF8JddgmuAEOAfF3NeXUzz39iSVyWxVWAnRFPZEXhn0NFq65dCBf+o5w2x/rRXmODRAL4pDGEMVIkbdAjIaqDCfd2sW6AX+sZ4ZOCUCn2H8Rz+a7CMfcn7Vu5dDiL5aSrNP9RY1GbBMHuv8XVp0G7mct+rIL9Vxv16XGf9wBY+jzT4B7m3pflnMElaFF2/JH5gKGM6w671S9RArzoCMlBmEbq6QT6orkbGKDsQOkE/ebdQs5XagLWQ4GaYqvxA5EZ5aIjn8GzOz9RUca3ts5fmrSb4KceJ7DcKt79DH5e5Vc2PtCfnrY/TT06tD000iyJVTm15zyc4iKlvuw5fMw/mgq2G5qr82Nr6zvULBJJHFGBA+GhCXf4YcD96aqd6kIugpwAAQABJREFU18tfFloW3cgoAKD5cbwb9aZ1pBJIJZBKIJVAshJofWslW19i1C+88EIN9hGk++EPf6jOO++8Rl1veMMb1Atf+EJ10UUXaRCQwTL+7u/+rnE97A41CEnfLf3Wb/2W1sT7/d//fXXttdequ+++W914443qd3/3d92yx3bODfwjcQEACRQlmXoFNDtr20muAKC0PQwQ2CttEZ7dtn7A36ZNmzTgLNp/LB8WALzz4d3q41/6sZeVpxsrjXMCUnhp/1EDrFOTwUZlMe3QGrXhwwyLCfsyyFqkrZRPy3abSCA2U4D575rWZ5Rp/iv0a1gZ1xit2QXAkzy2LfKPHoD20fYB7ctRR+V0KZudR39D+y8L01+t6RVyBZ4BWKgDlxg0uUTS/tZsjLR3QPmEMSNsj7pLKSxQV+3DeWir5Ba5WoW8MdDCmv9qigkucl049j4FzRua1BIYyUNxp7gGjIXsVzeivppK1MalBrIxDjQABjnaZAdmBAjUft5QhEqkmkkyyp+ReEggUWvTIaiM5sGnDXxG6LHnfDgYNCPtOvhxlg0EW8D7wsl4pocIhOGkbTumjPolYRywn8Lctzm4GSivcyBWBIraTTKW2i3vKJeF5h/9U0ricMjxXiIgHdMY872v6hXnoCHOuuWjnAkCUqtWsCytBRgTX6w6Mw7TY4PeKXyQID1+iF+VQiTc3DLXIFju9k4LQSV0lnwBWokhFBOPlxfxqBoX6/wG/WM+kYD5aKMfwEUEC5H0s33PpQCgCCPdphLolgT4nO+ndyLl0m/8dqsv03oiScB4HUcq11OZ7733XnXHHXdont73vvfZwD9hlH4B6Q+N6ZJLLlHlcvNLpOQJ2nqBf1KOX8n+8i//Ug4bPDVOdHFnpUyAu9hEW1WmH0DbBceBAIG/9cmvq8tvtvsINAFA+fLpKL5ihwT+6IOSmn0HDx7UWnrU+DN9/An/siWzYQDAn9y/s23wT4NOAPjKGkhqfZwMEAiCplgvJS5sRAtDv0m9Fom9xXagCHPzFQTqaAX/WJBRfyURhCpgoVwbxWLZAFnkut+WChFDU/DnR6UHJ3iCvuaiUsA/fT2Cphh5HJ6qYLwQ6bH4pbZTSz1+DAZc48K3WylDWeGnoxfXbeq0JlkUBiiGZtdFKRlfXgBPWd3fYKV+72iz7w5qINglLgucZLSJscu4JHgn48JWBjwRGOS4ZtJgIe9pgi3cOuTHMUD/gmwTn0/OxDNlaDlWqGnU+khzZo/t2E8DkDzNnQoAuVPwj9y2Njm2NsRNiBpzWuM0gHAGrghqjqjnup0dtLX5jgioPMRlavrpe8iRl2MxR3+AMaVAEBmmpnTHoRM2eQTlGgJwKscaoCNTSPVHlnUQw9/sWvucewd8AFZQyePPhQMAs3iWLjMYDhPuy7j5swg3/1bnwt38Vbyr1q1qNadmEBC/NOowA36yBwOByDw4bg1A89mf+gD0GyXptVQCqQRSCSQjgXBvuGTqjo3q9ddf36D13ve+t7Fv7hAUede73qVPMXruT37yE/NybPumabGpiRVbBSEJiQZg0ibAlKu8wGWyEJLFWLOdvX1TJHpuQKDp58QEzvYenYlEO87MNGEX4O/AgQMtwN/WrVtbzMyjAIDfv/tJ9anLb3FdV4dph15AY05eGXHJjYlxbq63An/opQ/+1JcRGiDwegg28rg0redOUTsCgEdtyL017Ce2vbRqUBVOynrmC2oXwazBKiTjAP+0L0Bo/rESav5Ru7KxWAsiWr+utTpQLofyw8cqKrNUtUyOQ5bvqWxox+pnLfzJHEeho//WG6PLgtaKJYwpLrydQBg1mTqK6I3x47WAN8Fqs90a2CMI6JUw9AmAaC1L5KHsNIhDfMMNCMQ5gjIEj0R9h2VpbrqszYS9KmrjvDkIPIp7AYDs/tlTAf7RR2cMKR4qMTASRALPNJrJhtH+40eJFj+B6F/HYyqoRvv1mO47Ppfp988rEfCmq4A4UpAGIDXEpf+1j01UOgyzfn644b3SMP/F+UAwMSLDpv8/Ft2Rn1d7C6tVsdDUgvMjmYGMbPy5v+r8SES6dugIOi5kWruaDwx7mlpYJY8V+4X6kTMQyOGE/XW7MhFwUub0cQOAUq2sHeQ43aYSSCWQSiCVQHckkPArtDuNoK89JoJer3jFKzwrZXAOSXfeeafsxrqlv0FJjES8UkkAwKRNgNk+mRyYoFm3271xbFSdtHo0crVOIFAmJNKW6YUl9d6vXq8+8d1bI9PupIAAf4zqGxb4k/rCAoDfu/1RdfE3bpNikbeWySkWzB7af1y8Ze0f/SPXIQW4aOl0PSblZQFEir1mmiztjbJlu4Zmyqq01l37j7SYp7AhaznIb2dVjMV4FWaTFQCItlU1TPRo8ksfkEwE/zJYN1WHmlLWF0L8WTa0vrgYHSzXLN9rIcqGyRKdozBU3fPQ3E8Hn8C6cKCOdOnFKwCqyMkSbeRiHRdAP9Lnlg38kxkDhKmj7BL0bTO5AV4D1E4yxoGTtKcWoGRkJ4NHrWGIfXLHUxo8JAjINb0DDCSYSQCmosc360eeFUjm+JfqyX+c4J+mS6J9kDj2dMTlAF4z0EDX4KjjuSbmogHFvS/HISfQMP3+uVXG8akBQo7LDpMe9z40CABK0uB4/WD4OBilNiyZYWLb23lW6cLuf3LjjOBhpXVQIR/DA2TnXMgoKHQLgLmEDZSs399CM+5tcXEQgUDCCWH1SOu7twI0dr7g9mXU4tQJAJahnXmkx0BAAQDNOWXcck7ppRJIJZBKIJVA9yXQ+tbqPg8d10gNKSZG9/Uz0zUBOSnTceUgcPToUfXUU0/pKMNXXnmlJnnSSSepd7zjHXGQb4uGAIBJawCSOZkcyGShLYY7LETgjoFA7nhiT1uUBAgcAp3XbFurzj7bUjX551vuU4vlivr3R59WR2cX1Off8zsNjce2KgooROCPUX3FzJfZGdV3cnJSbd68uSFrPzICYjKPAJnO/N+5+RH1pe9YgUKc18IeU1OHay5XH20AjMTHUFh6SebjeoZJ1jd6heOnTcTMUoj7PZwGqvCUB9DEVfsP/UDAozyOhYxjgRy2STTXqQLkrTo1kND/QzTvrtPV4B8WaRUCwu3U5QBeps/giWaPheXXM1+3+hP1rHrWGj5aq7G+5o5s/ltvCBfqQaznZmCGvzZGWaHuFvAP52ygHarLYk2/jDFQXdVG3VxbN/EI3doMxpTf2GEtjKS6HOSbCxn12p2CYx0ky7L4cZ+3hZyg79LiGhz7AI8s1nbSlQaXdmINZDF28I9skHCvJ74/YBpbOKneZ178Il/+WA35HA8PtlH62KtswPkw910ACXwcwbAKes+ACIcI21taF0Qx4Dqeu8tQv3YDPweKlmZ2g4Jx71W5EuD9WB+rXtq5jbIRd8hTdrQpCGr/MT0xtSEUJUsLGcyZ3YxD8/kailDETJV5CGWMXw38U94RfEZyTy2uUuMj7uqdThNgyp6BQN7+sl+X4iu+lTm937qqXSZlnirbdumk5VIJdCIB/XGwH96JRiPNjzfG6XQ3lUAkCfQ9AEgzWwJwTCeffLJv49etW6e1BAmKPfssVmgdpNe97nU62q8bCYJ/1113nVq7NuTXTTciHZ7rlg9AsikagDJZ6JD1toszEEi7AKBUWsKC4ubnptWdX/kP9aZfP13d9MiTckndu++AevcXr1Vf/cB/UUO5eG+dOIA/YZQTKoKyBP/cAMCrvv+guvKG+yV7W1ut/YconuVRTsrNWblFjn7/GkE22qohvkL63Q42bS9NnGzl2l4nTaSwlLKf7LEjtm1kqqwWN7eaIGn7I2boANgg+FdBHzt9j1HrJlMCcQJ9yCPgHyMKt1ufUwNqaVNQD4XvDKsnu9OX9OlFzVdtLs1IEkjshqjmv7qgFG4ctO4MTcPUHpp4cQKAWfrndxF/CzCA5uUYzRc3O0HiKKmGcUnMoZEI/oVIOiIwovn6AYUNMmCpAQRiHS8ccss4I0UEILVMR+VKo2TXd0xwlZJIBPzreqvaqzDDd8s4+8u/Xyy/cKjD6WsU4FY73yBs3IYbjrYi5oGX3z8zj7mvTYFhbl+LbshgkrEC5TSV7RrXcnCpYEt4bksqjeEZb8jQNLWVPJ1ss2sQ9dboSgEAHw0ZACRL81+3KReeUQxcYpDuhM2WsqVZzKXGWk63nFgqWgFNnBemEAhkYoO1PnFeG9UPTvvZe57d15MAoMzx7dymR6kEUgmkEkgl0K8ScJni91dT5uaaL14BvfxaIJpx8/PWF0i/vO1c++AHP6h9tr361a9up3hsZaSd3TQBXmkAMGwgkDBCXqpV1Q0PP9GiLPHU9LT6/c9fo44v0Ct+54nA3+OPP65MU19q/FFb9VWvepVy8/EXplbRynQCgFd+776OwT/WT0finNCXXRYrA1jI5wAQxZ7amOVrLlDOBv6xVx1rIVdeE2iCaz0dnBwswfcVtA+Wc8ajnAs7CW7QKfi32gH+ATWhX0cb+IfovQS8atDKWs620Un19pvg0hKAmU5ouYm0fc7cqHmcw2J09T7rmgSl4JGOXmt0kUdp19Nu2jySMQv/XdTUy1ET01jQy/V2thma/XoIy6mlpukjbw4LdPqBjJJsEX1RMEvzXxMl8CBG1qgFGCmhkAQKIZdlWOYtbiP4p6lFIhUpM8mHTAIAkr9Ewb8IPIVkPd5sGMf5Y+gbl28atoqQbwTaf6Wx1hvL756x0fA5sL8zfDK6XAry++dSRJ/K8d4L827yIoDzruAdtMSdAW/M9lXoskHGBQegDZn3qSzkpewavCCMdMpQXQMwDACIdw7l6fXscftQYVTV0e4yNQBDpHKNX0Fak18gEKcJMEs/cRQDv4eSzOllPtlDrKWspBJIJZBKIJVABxJw+6bWAbnuFzUDbQwNBc0YEfUsb0XrIvDSSaKpLzUJqSHDoCL333+/uuyyy9Sll16qdu3apc2BabK5UkkAQOExSTV7mRw4waZut/3MbRvjrVImxA6qhwqL6m2XfFtd8d/eqk4+ibZj0RPH3549exr+/UiBwN/ExITasmVLKFNfv1rd+uTL196j/vVHv/ArFuoaxzxN/1y1/3BtCACRh+hC0Y81ExgxFzqaNhY4rUvG1lpZjmuhXk3kbWSqpBa2GxEIxY9TB8Af20uNuTK0QkwQjv7ZcouolSANO5jjgOAfxgLBrppEaCSBdpIxaGZ+LdzCK2w1BumwRdrKNzSNsYXFKk3qBuvafyTUtvYfC3sMwgz6YnjKMsHPQhtTL5I7fKMPQgNJgydeAkPf8/7nELAlHCOopyriplkOGal2Gao7S+sGtYnkILVJITf6+Qpzb9JUuMIhEnGcExwpngSRahNiZyNsLer6AQFAdvXshAN0j5mTludhzPQ7JUdwtwaz7CDtP/rN40eDlmBDFCJ/nXYvabSTUC7I758XWbKsTYHxAaTd5ATWSYfPaac4BqgCi1TBtFk/5+sZ9IcYZ2ads/0/OWgAmokagAuVrNo3N9bCl5mP+wRFORZcAUBc18B5UlqA8+EeqAPaBLh1wFAD0CuNOKIAM9+hRSLAvZMEAEzCBFhameTaROpIt6kEUgmkEkglYJdAuLebvUxPHRE0kVQq2ScZct7cFovWl7qREW/nvGZ+r/3JyUnbpde85jXqggsuUG9729vUjTfeqM455xx11113BZol24jEeCDakHyBUy4CfMZYRYOUmAfIZKFxocs74yN5dcqGNWrvMdjgdZD0NC5gAjxXLat3XvZddem73qxefGp4oDdp4E+a7QQA/+nbd6p/u/VRudzR1tL+ctf+G8TtpR31d1SDR+HW+bVHRus08ZdWM2QQCathEbE+X2YSuJhdrCAoRwaLNyIHYBaLoKiAiBtby0AIytD8WzZMwgg2ZUwNLQP844JRa1K1oEJu1H3OgQ6bwFRaE3ADWtl66y+YX/WcxRLN1STADIdRu/7/NDWXcUhtu5Ej6HYAWpT/IF59NCHUvrzalMoAlJoJXgatyLngdgWRcJ6uvTQIGBAEZgCaq0MwW15YB9+Smt8Bpd/MBBcJBKIt+gdgMIO20SSUxzIqBhAgJl9AWYDONDvXoEfA+KO2cpnr8YB8bYqvtZgw23rF9QxB9KTBP1asAR5XDnrjJEHtUtB3NYyTYUT+LQBAbkkEgyLKvoUGT7jcd675HCfD+v1zFGsc8h6kFm4V0ajbSS0agHAFkoH/v5ZUb5/1rG9eFU3U5pnO97KrHRqAeFA8jQAgMLANJM7ov0xeAKB+KLC/2+wvTdzjz0AJ2vVlwI85f+KDo1xK2dtIkn4AoJsGYAnz9SkEnlu/qrP1iUdzIp+WOb3MJyMT8CjAj0hpSiWQSiCVQCqBlZNA8Nt35XgLVfPYWNNBRxizXgmKIQBZqEpCZiIYSc3A0dFR7WPwoosuClky/myiAUjKYeTSCQe9AgCyDWfCD2DHiZPJEAuIIuCK//7176nbfrk7sEpqqoqp7/79+7UWjWnqu23bto61/kwmZMJGrczP/svtsYF/BJoI8LlG/oVGAbX/eiFxkesKUoC9sA891/K90DjwQA29oZmKKqyFKhO1/jifjqgN5daUGrSy6A+qAf6JyS/6vLGqxhgQzT9Wq/3+xbHiJg38n5tED8VBz2wgGU04DcPVEwHnKkA50wSRptFhnide7DnHITUxRw+BJOuC9g6j8fJxFdks1qhwgCAb168kFJT88qDr8vTKAQ09v5RH0BJdnzMT+p3aSASUCUwU1w+qxS2Dau7UQXV8ckAdP2VAzW0bgM/LjFpE4IcqlF/pbmAIUUzph5LuBzQYbtAlsLa0Ac8sjOvYx5VRT6e71Hpz+trslKZbeb/uc8vfzXM0UyUAFgRSEhTiPWb5b7Rz6Lxf7FfDH7VDhx/AsjF4B6FvwwFqxbaRnBqABP/c+pwagKzBaf7rCbS1wYsukoWW+AhfIFbKAeHfnFtUj08Fz9U4HrRmM4r6AZOx8yzMst4QZsCzlTpKaZTj7tSitwZgSxAQFkBH/RgWRL2SBACUOX6v8JXykUoglUAqgVQCnUkg7Fq4s1oSLE0QZcMGzO6R9u3b51vTNPy3CQC4Y8cO37ztXmQAkPPPP18Xv+GGG1S53PpVsF3aUcqZAGDSfgBlcrDSJsCUT6d+AKNOuauYsH302pvVd+78pWv3EPh74okn1N133626AfwJEwIA/vO1P1c/uOsJOd3RlsuFwSL+os1uvv9yC4gy2FENwYXD9I/Ogz+tix6cjIBPcgHYq1+qh45XVHEdtA7YSAJ/nQBm8A+VKWCRiG15DXqwDiQSaBqawwLMXI0b4B97qwZNrwZYGNx9gTm4kJsDyBNn4rgNMifsuD6AFiMHLCoE/Mz6NADYSQXmmAXANYp6ODap6UOwLIN7kmmQQHA7CTQz9N8XUux+C3FdPYbQMJWw6yaGLSyxPoKWUV+NGJc092SwkRI0vwqbB9X8qRk1fSZ+iBg9twPn1gM4hPJMFaBDJbOsilh/M5Ksacrewk8SJ0LKUqrWHyzCdoAUanPb5ihps7ZoxbT5a0DwD/I/DN9/RWoJuz33zPslWvX23BEFRaAqR/A7hsThM9ymOzgzmAdZ4bPdLfEZwgjY+vldH6+6yTG/xDNry7Zu2g5fAVQuf/KYNW93403O0b8pTUQ1wFfnUa7ZtrhmvqZs1zo8GDiOB21AmikvyWvTlnN2aVRVqu4CddMAZOF79vqvY2wVJHyQAoAJCzgln0oglUAqgRWSAPXW+z6dffbZ6o477lBPP/20qlQqystfBTWwJJ111lmyG/t248aNmiaBN0YoZiCHbidTwzFpAFDAJpksdLutZn1nb99kHkba15NfTjL9JppuFJH/4pvvVjf/apf65/e/RU9YCfzRx5+AfixGM+yJiYm2A3u4Ve11jn3ynbsOqKeegypBTIkaFxksckoukX+1f7gYNB/8WGW3BK3J5HpLF/IEeHefivvUSoItxHzyd+HSMoCVyvCgqqymqlmbzEEzVJuNQiZcCBLQLdKcrk4vC5NfBnqRY90sgn9YTNLnHxMBqI79/lmkGn/L0PyKGk22UXgFd4YPW2OLZuem9h8XpZ0CgLqHOQ4h/9GDqKe+npcgCdokH5dpIhs5gRa1jVxXr17Ewgw55BmGP8TCOjDu0Exl4AaaSA9BWyq2BERBj0ftDpNCB2UO39gqSI6QBf4lR7+Fcq8KBc81Rp9e2uLfb8sAdukrsrzKpSHo93YfiU458bkYOiFvu37/vOogaD00taxKALWjJFMDkKby8rxw0mD7Sqtg4mrgW4HgvpNIiGOn/79T6CcA6YmpYEeHmfqcwuTRq0ryThA29qT9APo/rKrQnF87OgTz3frLsc4EPz1NL61SG1e3IsNeAOBjR+HboUeSzOnlI3+cbInvP9nGSTullUogtAT4nI/yrA9NOMGM/cZvgqJISbcvgRMCAGTEXQKA1O574IEH1Lnnnusqkdtuu61xXrT0Gidi3HnuubojKNA0gbgYqwgkZWoAPp9MgF+4Zb3KYMFZ9dI+8ZEcTegsX1I+mbwuYdXx0MHD6j/9/RXqd8/eoV6xoXlrdRP4E/au/OHOWME/alFlvbT/AEzku2T6qx2XOwAFabO8E12XS+DRBGakTNDWlVZQoQSvE2CiFlRtuDm+QldH0A9jnItnLv6kbUWYRpbGccSVM+6bHHyzaQ02cyUt4F99HUTQQpv+hq48XMbZ0wnRCmfhygTlipdaa218bowCAGSiSaoZ/KNT81+LKgnD5x/qEP+aNGuVRbFoAEY2ASb4h752AnSNOj12QoEEdaEPzwAEXGvUgXZQq7SCjwjOqKQe1UU7TeCPyRjf1oku/o0w4LoO/lEMEfjrotR0sIcKPLqY2rPO+tGtOlXoH8/lPdDOM75OsnUjlbVeaTlDTbW2APgWSvYT+lnN927IwDosbfoAzC56I2K8VZzPJ3mm2Lno7Cg7ZgfFdtQjAD911F8DkOb8GtDDeOUHg8CEfOyyuIf38mImFM01q1sBQPJMP4DuAKBdLtK+g/MWQCrHK7lNEgBcyXaldacSSCWQSuD5LgGutvo+vfWtb220gT743BLNU7/xjW/oS2vXrlWvf/3r3bJ1fI5myDT3ZDr11FOV6aOwY+IRCORyOcUfU9IagPJ1UCYLEdiMPWs+m1Wnbw7+suxWMc3SGFlQUZMmwuS/QQszzwqcgF372F7193fsVnc8O6t+7dd+TZ133nlq+/btsfr4a9Tp2KHJ6of/8Ub1JOqOM3FhRU0CamihITbSNB/UwQNsZ5M58AINdHeBNfx3T9R0c7/SF2fZPsvkEztGcI5A5rWmH7T24B8Nbpe09h791FEWpFmAGV2JZr8A++hzSpv8OiXlBP9QrsJxYAKEgYyEy1DY2H+9NAKtPHKttf/0QGy2taPov0IG8qd/QdG85GkCjVr+uMYgGUyR7kHcy+2Af6yHoFWoVM+Xpzkw+GQanl7WwDGftZH41aUD/hDR4NjmLyBrL1xeEfCvFxruxgPGR74e0drtspzjBzoCbaUx+ztIrkdx8dAo47XjuJe9smm/f+4u4LyKhD8PkJNBUaLMRxoagPqDj3cjaEZvmv/qOgxtwPBM+ufMjeFmNxI1AA/BNPZ40T/QhY7+W3/HhAImcdOHymfwEmqXJrwIOBSUxkat+bYzn1cgkHy2gld5K0DLQCD33XefOnjwoFpptzoyp5c5vrNtnR6n2n+dSjAtn0oglUAqgfYk4DGLao/YSpV65StfqRiFl+nyyy9vAHAmPxdffLF67LHH9KkLL7ywAY5JnltvvRXrKSyC8XvPe94jpxvbJ598Ut1yyy2NY7ed48ePq7e//e066i6vv+td73LL1rVzogUofg+TqlhMgFd6siLtO3ubZYItx6G2WDxyLsZpHoGSBgjoPX/2JgsijIT5/WeOqvd87cfq67c91BVfcgT/Lvzsv6lHnjzgzVsbV6j9l4GjfS5Yy87IhNDoocZY11IdSGipD7xx4d+S2KEoQ9CrrdRuubYqcy9EuVNTw1qssUEBCe2l6RcDdTRAP45voxgOLX9p9YV0dgH9iD5uAfVAiwBv1rCA0pp/Lto3Bvm2dovQ/onTn2CDiQT7kMEzxFcXTXJNf4nst8ZivMFM9J0swIWcYV5v0/5D/dKvOgBGSPLsbzcNqlDFo8wawBzvyzw0AaldOnysqrX/BgE2a41Fr/s5FCPNTHRBQO1gkUXzSm/urST4x7p7LTGoh37G+fDG25gAD10XUAu6JfEZ53K6JV/IE67vE0dZzhni8vvnIN04pClw/lj4h5hoAGYYFKdBpXWnuDZrA8wSGRdDVZUdtoNc26Eu+eR08EdaRv5mkn63jgL+osHhJRVAy7w8F4yMDufdH4zTEQOBcNwdO3ZMPfTQQ4rrEvqQTnoObzbV3E8aADTrSvdTCaQSSCWQSqB7EgijWN89bjqo6ZJLLtHBN5aWltSb3vQm9bGPfUxr+fH46v+fvTePsiw568Qi35Z7Zq1d1bUvrd60NGpJiJYYLQiLYYQsYCwBBwmBERgjYcYjg4E/gPGYEcw5wAEfzhwkzZmxGY4lDBoNuBkBsnUASc1AS7K6pd5b3dVbVXdVZWVm5fbW9O/33RvvxY0Xd333LVm60Z317hLxxRdfxI0b8bvf8olPqI9+9KNCnRpZH/7wh1PXRF9ub3vb29Rdd92lqHH4mte8Rh09elT8DfJL3Re+8AUBH3nM9IpXvEL9wi/8Qup68ixA8+PV1dWhLx7010G9WMizDVlo3QE/gP/xS4+kKkqtAr1Y5m8VG5IWPlB3vyjrm0mp+vk32y31sb/5svrDLzyg3vvGV6n3v+XVAjInJZM0H8G///4j/1E9+WxGz+ERFUlkRtxv2Np/qLO2MfzAH0HW+jtCNtNhq34CDMH9R5BczNkUyo8LVmCTOP4Sg2ItBPPAODbHsqt5Av4xgir7kyaZBIM4+u3dM9pO8K/qB5kgrTbeGMMKqLBxwsXtYNfYe6Xukz0YLVfpuRe8eYMytUem+OizL7qIRFwT+VtuPLvafyin/f+RRFKNujIBe7uvI3iwbyUyATYLcWhBQHMvAZBA1QwcUyZghzy83p1jzTJJj1Ge451pd4A2eRRy+DdBf48T/JMWJuAxB0kkJ4F5ZhavrR1gQpHmv1yt+mPJNQZzNf8l95wWoxLu5+33L6w6Ao0lzMUdztkxST46QKaVBmcld+KdQPRfnGvg0F0i29XKclD7j1ROzWyo+545GUlQ/M/yvY3mSl/HN9uj5+fPfSxcx+A77E80IZyXq+4BE6YBSDL0A3i9TxNySh05ckS99NJLokzw1FNPKf4x2OGJEyfknv7oHsJKbpf1ml6v8fMgPKmB1fJoW0Fj70mAa5IkH3smqWV7jd9Jkl3BS08CNwwA+OpXv1p98pOfVO9973vV+vq6AIC9ZnpHBP/uvffegcxyv/rVryr+RaV3vOMdiqbIc3PwrD/GpDUAv5lMgCnu1JGA8QKgrx0zcb1JzZsACMgMSReiRl6sxdUWgMCPAgj8D0MAAtuI3vrf/atPqQsvwOt+zokASgXaR9y02tp/lJlplphz1W5y1kd2ml2GavexrwgQuCklu+pe0ycrmzEXq+SmR4CROFCDEXzRDwR/kiwKKK/twyUBYRjRuYJNpRMMcoB/1DwjeDOMxL2e+PUaBvEh0aTpn0S7Bf2Opf3HKsW/1gB1U0tOXBIYNEztP17W/v94rIEwHoelEsDEKJAlrJx5PZOmEAZnDX5CW3OAYzlWfWyCx5kBQD7b3JPLkBzOuDTbncfx2ME/NoLau3wpxc0teTQ4AQ1q9HGepp+3sF6k3GScRI2XvOfqGHrD8vvnFBn6bBrm8wyQEiokvyCBvKm6B7Y7aeHizsEy5AmhaoGzrQO9KN01Vfaxc3vpABYTC5WmeiwmAEgFrli0eWjq+YHvTown3bRe7dmPdjcT+AFEcBpXigMA+8rg2eR+pl6vK/oTp1shruGpFci/Wq0mLmUIBuo1fh+NHC4QqBsGAEjWSFtbXOXAakGikEAhgUIChQRSSuCGAQDZ7ne+853qgQceUNQGJNDHFydflrfccot697vfrT70oQ9lBuUYNOQv/uIv1Gc/+1l1//33C+0XX3xRXsxLS0vq7Nmz6tu+7dvUD/3QD4kmYsp+GEp2vTgYtvmA/jqoFwtDaUwKoqcPLePLalVtNSxUL4SGbEgdazcuIAl+tWZwQE0slufGNd4ahDm95K9CZb+FK3kDgc1WW33gX/6xeuGlfH3+afZLcMTNfYFT+2+dwhht2jVMT2Uz7ei3LkcUOpGlARJBtagqBiDtLMq6xOG50c6+jOLXD/s1AikokHSjQ1dGBP+4oaL/txK0/5wgAORGYMnU/CNfYvqbM2jAEdRYRptp0qcfkr4GZ7+QVDapawCv88971CkbArYa1CItAeoGeLvSpLWGR9rm39Mq7F01NQDjTIBL+KAhWoK94qmbzQIu7as4QtTooa/C+v4pmHBSYl7i3JspYeD0wL9MFPIvFCPXiQD/dKvZBTH86qzD/YVZOHzcNZfITjhDotWG2zQVds5ZQ3gVRX1QGarfvxCBU/N6BqbAO4fC5SRF8e4owwVEVGrsIwDYy8GxOQwA0Pb/1w0AshIdAITy1cnkU1+L/NVtiRZBJAn7ZpJAIM0p93pzJaUJMMHLNt7xDB537tw52VusrKyoZ599VnHP0Wg0RCOQWoEHDhwQrUBaI+WtFWhq6uk1vi2X4ryQQCGBQgKFBPamBAbYokxmgxl447d+67fkLw2Hb3nLW+SrVFgZBtSgaTH/9krSEYiHrQGoFx6T4gOwjCAVt918UH3lgmeOHddftvafmZ+bAA0CTmHBLNoK2OynMu/z1+smvpEHENhottR/+y/+L/Xi1eFEjaP2H/3wcK/YtJRZK1sD+NUzBZz2mMAYfH0JEAjGfNH2U+ENgAvoqsESGz+CxGoitf7EoTvagz1GGtBPs07TXQH/cKEmPhshIBeYp8E/agb6iUd5+/0jQNbApr8tGoXY+gOMZNs6DG6xBxLngdp1j1Fq/5mRf3mV17ImAnnTDvCvD1RkXxkKNlEmwPRVmBtghmfQ0+BI3kL6CSX/HEc148MBPzC0w59idwV8rgkchj787mLjvDpR4B9Fh4e694SPTzL0A0kt8vr+cB4oOwGdAYwwEJWr3wm+5z4cQgRE0HrYfv9CpUEZwFdrZza6tVEgvTyHABNNgUXlD+Ul5gbFV7UiAB9XWwC3ptQTK+EdTs100er3m5gaAES9erxESymmAeZtCG1qE/PefMigQN6dXWMyNsruNGtqu1FTs7X++zQBthN5buF9z3UsEzXlaP7LP4J/1AokGMh1PYFB/tG/OQPNUStQr/ul8AD/tFo9k+dhAIBaw3MAFouihQQKCRQSKCSQUQI3HACYUQ43ZDGtAbixMRyASAtNLw4mRQOQfNEMOBEAyI0DN5MRyQQBiShx083NdAtgRWIn/3oxi/WjuSjNCgRu15vqx/7FH6mr1ywHYRHtSHuLWjtcgja42Sh7i1HSIFhTBQA4rrQLAJBmbKYcA7zwBtFWbhYHTOz7YSducMX/EsFNM2ETwLFJE99BNrgtgFHb8PnHcSsmoy7gj/U6wD9eFuf83DDmkLj5rCPyMGmyB0mVZslltDNvAJAA9jD6j3RrjMzpJ7ZJgAl9Ab+Zo//SJyPBP8e4E1DR6DsNButqBeDTJ8YvFVPEoX4+XSiUucF28WhU2z2kNiNNzRsLYAD8D6IBSMBINC1zbEuX0UEOIviZNPBPmsm50dAAG6Tpg5SdhtcKzk/OjxE+YdGIhnxL0MYPnfQdz8sgfLGsc3yjnlH5/XPyj+dnehWmwNNgxH5fGAV2GZCW8nKk7UMM/sFn0b8JUllANgfpwKUSgn+Ua8GX8PHylrqwsaQaERMkTavN8SD9H6Cc4ES3LUHWpFmmVqE1Od8Dxexy11sUeG+dZN6/urmgTpgvDf/mrAMAJDJPAND1LYwWTWfPnlVnzpwR4I9WTvQ73mw21dNPPy1/+/fvVydPnhRfgXptbvKS9Nj8oD8InaT1FfkKCYxNAnjmilRI4JtNAgUAeAP3uAYAh60BqBcHBAC1b49xi5WBQJIkbpqTrBW5GaAmoF63chNKH4EEMjyn/Alq8ysiNmXXmQYI3NpuqB/91T9S19ZpDzWcxH6kZgbfiwHtP1ynLy+b/+Fw0U9V3tOVBGEd0D/upXg/zagr7PdhjWm2hRsv0VbAxk4S5EsNMI5LMfGNYi7BvSbM13f2l1QVgT44ZjuoB/ojqiyohNGLqFfMfg3NP5InMCnmuQnqispCDcTGEoA//Grgj/mlP8EGgU4CWnknD2LMmSoGlo6KzHb1af8RWMkCrgDYpuafDSaSe09rJzhvmP7/mKcbVVePJV4kgBwFnDBPlsShI50XX7i66YEA9P/HZJotR2ktBiijLgH/WKcxbAN5JvBkIsG/SZETxnsN36+2DoUzRPnpiVy0XV2TOsfEMMYFaVpppH7/rLr1adcU+LC+0v/rzdmOBiBrY8ky/2Vxl1z7yaa6Yvv/Y+GTNUQAXo02/zW1ms3+T1w5ms0PHgSM2/OJS8Vn3OCkHg4Arja3MDXNYyj2T1A0Az6x3/hq5Nc259AK5FhuwrdvVDK1Au+4446ur0C6+7l27Zr80Wrp2LFjAgZm0QoctgYg21doAUb1cnGvkEAhgUICw5NAAQAOT7Zjp6xf+qMCANngYYElaYWZKBAIFlppQAeCQdTG0onLPAnAgDUh/QQm0gb014bAWxzLRMNH4BcfUO97413qR978LYFF0trGDsx+/0itb/Sbjmi+8vjlJp2sEkAytf/om8eUQR51JaUBkYnQnJoZmgiZpnCj1886d7JfVuz3W7IC8bm4sfE0W3zCOphHQkA6rgY2nya2zXmApdhoN+dQj97kwRng9GUEZJhBw6jZGQL+kcdB/f61AJA3FgE6WsCf5l82eGSNoKfxbOn7k/ZL7T9T+419aAN2mcx/0QfU/AsDxFo0wTeBPZxWDF96lBNHEkHerjYPwL8KFYR5I+ekTexiyaJf6S5AxhIjmKKdZj/H+S0U+miTaDdKA2NrHH2GEPmyzSG3Rs+jXaNMpvbF0Z5zvHOe8j4KuOuWQDqUI60n9fxlZeXHA+vRsHJkPKWMjLl/HH7/Qjnn1I2POu159wgTuTkKtzBfiba5UUyeZUfeQS9VLPNfvpZPL6yrTz15WyhpRj03P9p057LQEsEbdM0gIC3GBFMHv2lpeCX7/+1scrsUvu5q7rbV8ty0Wt3iYA2ma5tuJLLPBBgyYvupAZg0mVqBBP+0r0BqBV64cEH+qBVI82D6CtQf7OPoFxqAcRIq7hcSKCRQSGDvSoBvtCLdoBLQGoDDNgHWPgApRmoBmufjEu2RpXm1f35GXduk+os7ycaBC/wUSTTCuCExyvBaFcp41AZqEzAzbxr5uoe8jz8uiMPArC34X/n9v/6S+oMvfLULBF67vq1+HD7/NhwLzC7tPA4AGJUBRFE0DQJHfqLpb+06V9a9a/reSH5RbZi8pH7NFlgM2StmYlOTzVTYKkSZitYfTbAI+vnafpHtsmjYpxx3BKDpT4+aH7LhIdP+rljMvViIlTNRkQHHVYBDnWpHxqEZ8INZmHUQ8K8J+6UmTD7Ji6nxR9o6CTu+cLWps76Xx2+e/dblhwOLhLE/Y1AVMUft3vTlRvO7NIngH/wJ8plzJdH+gzwFONN9iIy2BiDLUktO+h/8DQv8Ex4TCpfaf8zapBsBjEea/5pFwwBPqYP/oB1d8K97MeSAsjGJh2QbxeWJBv8oJsjKGEqjEEmwDoz56TW6A2CXuTtNgCl/Iqf2fUg2GSOh94K1pjozueL8NDa/fy6u8SzV1mEKPItedJgCt0NcNuwchvmv7f8vi7ayiyfzGuah6lLwi87Udkkdm9tQj189YOYMHMucZVwRsNI4Dztk/wjwB+zN7DeCtm3Lf3EYjbjrnXo5dl2xf8ENAIZFAu7TAPQfyhbW0WkTtekYFIR/9BX4wgsvCBhoagWavgIXFxcjqzBd+kzCmj6S2eJmIYFCAoUECgmkkkABAKYS197KrAHAUWoActFA04NxJy6GqAX4hceeDWVFNpahd8NvCFhjrjL9rNzAl+C/hibBYV/gA1RBQ9Z7+MdBTm5u1Vvqo//Pl9Qf/c2DahFOqIcO/mFjVtnm12eAN/TEXentDmprHSyAnZwGmjWME/qJE8fgccTBf6J8cXTM+/6i3LyU9pgkuKGlTAnkiKZDWL+HECcQRL+TBOY6BPvYNdwgY6zHJqsN1FKjGatoc0JmvG1SIf0ucBhLvJeBGqME/vSm1KTZy+UdyQbfz0AgSALs2Jkm6Fxr/5ElMaeGDG3zX9EC9kGLRKxD9lXMGabZm12uq/1HWel+RDlXGcqRyiOeHy2bUn7norkZR87wFdqCJiqT6f+P59y4hyaOCd6PGkShhUd0w8HbpIN/I5JMZDUcnxRdezY8m2hI83bUGNHPQziZwe749Mfq9y+kBWIKfBlRgY/0Z+D87UrNBZj/mvMT29d7xbuKpL+G+YduB+wAIEdbdSic76rHVsIBQPsdIB8zojjgXIcPWazP1WK+a8Wa1mxzFL3Ie1j9XC+p3UWuj9xpYc4tzLBIwH0agD7ZZgoNQBcn1Ao8c+aMYlDE1dVVAQLpK5BmvVorcN++fWIeHKYVqE2AqTGYp6kurYR0ypOupln8FhJIJQEOx96QTFV0bJn3Gr9jE1RRcZQECgAwSjp7/N64AMBJEdudx24KBwAxgYrPrJyZJThIbYUOwECCNbGfjLlqhcYdHfVPceeItaVoFYGOuWbd3KyrJgA4VTOv5sw82RUNHfCBRdrOcq+uyha01aI2Yvmz0qWYCPyjHLmwhNzzTgyG0qHGXsakF7wEbNi3cZS4vSCI3IbJZJsafZilvY0QSsYVdvEIsdiJdCuItigJACLpi/k+KudxGr9/pEKQyjMzTsag1Kyzot8IBJmmoTa/ac8J1g2iVemqzwQsu8+plVH7CLUuh57Sj6hoN4Xk0Np/vG0CS5SVq300qa1sZRwnITy4LgdABFcGXOOcQR7Jd4vmv0hlPEtmKgEkdCZ+TOGDoMeIM9PkXTT7aPK4mxCO8LzPwB2auJcI6WAZX/7rp0xXtyHjQObTkHt5tJbjF3ErPC3UPAjmTQNtLyPCdpsfXYzUBU+Na9TK5nxiypLjNdeEZ5aA3NRiS5WqwWf7+NSW2oIN8jNrUPt0pDKiG9vaoKEAIPsF6yypK1hNgDKbR3+AUUBzoEDMydRaNAA4Mx0CACIIiCvNVYMmxXpOj/MB6KLlukaAjea//KOvQK0VSKsgAoP8o1ag9hVoagVqE+CkJsOu+otrhQQKCRQSKCQwmRIoAMDJ7JdcuNI+AEdpAqwXDbk0YEAid0ADMCzJBjrsZg7XqV1YxZ9oAzLKYVQSc1BohnEBjAWbK5GebKZdN/O6ho0ZwS5qlHGjsFv1d2AAKCviyN/NW17Vu+jIhjpigR8og3w+x4HLg54QpCAWkSWVNqjuB80BI4qypsNmtTE2BOyjRh/BPr1BCxkHumzi3xDZOTVUUSc1FAXsJJgawwNJN+HaqEUz8Zi8Jr8sZwJIGhTNEwBkffZm0uQh7bHoSJqDC+216bNdafz/UQuUZthRqUUNKYdszUAagfIcqCafgZs5nsRMBYzUXQMwwSTgn2+m2KcBiHnNTt7HEFyNqcMuN/Jzi7+9BP5poGHkMkOFEqABQ2M7xAJRniO9MsV4jgSCs07MCRtewkeSyrbV0QnLjiQb5obadd8U2PhI5fqAs3Oo2mf+m9TENlFb0Bf8oEFplQ4F/eDxdXJielM9sbafd53kbPNfjgMXAGj7+XMSMy4yfyLXLEaZ0MPrHJiOScsvULFAT01ndWse33nhAsF68EJNgAfUANT1mr+0zKFG4KlTpwT4YwThixcvilbgM888o/i3vLzc1QrUJsDDAgDD1romz8VxIYFCAoUECgkMRwJ6mTUc6gXVsUpgVBqApn8QvWgYa8P9yu84Fh5eUByPYx1nO/DPk28uhGlmKdqA9A3oXvdKlWJyicUhHea7FkYeMJcnd/20SjvQ2CHYgNU6I8fqxKi/4zD95QaAqvmx2y9m4A5jTBqKWk72b3mzLRpPLWrU4Y8aLx2a7wLok40X+XaAOzadzOciQHdpb4PoyAB+aHa5i81ch5sZh38p7rlbUGho+X7d3DVEXJV29+5rM1ACA5OausAsGSQY7wAedMCCJG0gsFDdiM5JQEk2rjob5eYnl/+/zcPQTkGE7FEkE8B11Vflxwx/eOnov8xnA5e674UG8kukX54YbZV7E/yPNBP87iGWx2fxhHl6+irmFgiL4I5LZjK2/GFMH27OTBwPFDz/XER4P4ckfv9G80hl5pamwLMvdtT2sZ4gXB94+BwGnlvKLq+2gZaY4ZImUmU/PnwZqblZVWcPr6rHV0PMfzEuJNBLrwke+Gecc26g6XjaIGQkQRCwQ4uMAVNn263hp8lOVXwB6Av+bxuDfX17Vu2bC37xma1ZLz2/eJogIFZVsadc42mtwNtvvz2gFbi2tqb498gjjyitDTgsADCW0SJDIYERSIDrFL1WGUF1uVSx1/jNpdEFkdwlUACAuYt0cgiOCgDkgoKLBIJ/kwIA0gny5ReeUwdmqmplJ7gYlR7CwpdmIbsEAbEGM9aZyTqQC7WEhQio0M9X1zdgSLldAEOtBfj2QnQ/E3Bj8A0ClbJ4J9A1DNAIGjva3xbBKa39V2rA9JemosOoM0LSFC9TiKi8m2YGFMhrL9Mjnv6IuBD7eRdv6N3FMkA0OF3nnmHE8ovb4ZOnKNNqLjC4adotox18S4B/tq0JrR1qdLlAatyOTexXAltmknGHC/LLSnLoSKsKs7rUx7b23+yLEIfdCFB1bbpdlRHMZ9CPOB67vv98ImaVtv+/7f1TaudAHEUXN9muBYAEiwRNyasb7Ej0Nf5EQ5Qneo7hsZ+6JsDInjjYhy48zl9f1GwfO3J0ks+n0ePil1q+Fbxzd/ZRZv1cyPxgrEplnIfNBxgzw55WCfRHjfV8emNwKgQBq6sd1dznCcvWAGwA/OvwY44hS5lPjPNBuCD4Z35MrSwHNQDr69Pq7C1r6v/+xi3Oaqj9V7I6U96bzI0+4P0wP39OgtZFWtrWaxhdVh1WttjTTgOBQMCPKUezUDPCrwwDgdgAYLXcUVU4j24ykheSfiKaGYKAmHwkPTa1Agn8MYKw9hXIiMJM29vbcv3mm29WlYrxcCatxJFPrx/0ryNLcamQQCGBQgKFBIYogXxm8yEyWJDOLoFRmQCTQw0AjtsEmMAfTRlo3kBeji3U1DUAgGUs/GpYvCxO19SB+Vl1GFGCjx9YVKcP7VMzpYr62J/+vXppM/h1Nrvk+0tyYSfagNgAUaunu7i1sjLwAkHAKkBADTJ0g5VgAS8bkuiP0BbFZKfluqH9Z/j+q0L7b9SLtO6mWg4S8E9QdMTaf9wDEPChyaf46WOf6M3UgJuMBC2OzpJEbuCR/BPkC0scs9TQopu2nYMctx7wpzcpYeWirsum0yKg90y8zLGexow2tK4kMggtHLzhAbj+NXT89DWrAbjFdiUBAOmjr7be2+gFa+qdkR4jOweSrhbjveu/ERl2AMpu3YTBN8JxFwWKVKD9p0FdjhmtRWpr/7Ft0veQaW7gH/tdy4kVDDHJEENdI6ou15bk+Hik4mvawxRkbLvkJs+aP4+KRpieUx21uLRwHdkGuzQuQaXlGs8+1wxNrB0UwEAbANw5iOAf1mpf5uK09Tjy8wOqft55mx9MKovBj66NNQCAS6vq0ZAAIOLn0aJNdxRlmF/H+fmzigVPIY67z9ysPvKBf6ze96//T/XcFJDKQRImvqkNaFIucQXQn3Z2LY0+IwsDgZxTLxlXvEMGAlnb9jvHH2/D1ADsYwAXuMZjUBD+USuQpsFPPPGERBPmR/2vf/3rohVIX4EnTpwQU2EXneJaIYFCAoUECgnsDQlYS4K9wXTBZTIJjEoDkNxoM+BxaQBq4O/555/vaiHy6+Y/e/u3qnOnTqoZAH9R6e1336L+3Z/fr/7gc18FljS8VT+/HsMXtgcckSXXLghAHxfyFfjQKmGVLua/mnmiMYavH315oF/QFFM88GJq/9HvX5lqYiNMInlUmUjFXbNGAGGIPJYM/33NGW6yCLagwhECLombl2LoSqRIBH0JS/QDuXMQgHQOwB/r0H1r19fVAsMNAYSiH1W7eN85N6B6aPTdTHlBtP8MYjPYv9mRf0lSwD8jn7MaPGcC/oWLvFsszPcfizLauH4+GtBi3jwG9HnUYxFzlASNsdpsav+xMab5r+3/T+5DWzYv8I+sJBAtqx0soSI9lq3mD0Z3lKXHwDg12avAX5oYs0m0/yQ4Thif7AD+hd3PS5bDpp8Xn6DTQYCw+YtttXmSmuc9whRTe7bf/HfXyNPLne6IIK3tu3VqHwKAQHNcJ7qTWG411EK1qR5fwZckO1EzmHihJesKXCQMMq0dnp9T/+v7367uetkxqfEnv/N16pf/37+1a099PrUGMDUEALzeJsDoXo1QA9CV5gUAhDNdJl9sowYAvcq9f7lupp9Aav499dRTamZmRjWbTVlXU0OQf0tLS+IrME+tQJOH4riQQCGBQgKFBIYrgQIAHK58x0rdBACpDadBumEwpf2EjBoAJPDHBQk1/nTd2qzh+PHjopmYtL0/9k9eq7732+9UH/ytP1bPb9Au2FqRJiUUk49UadrEjS+1fJzagKibmoAVmgMbH5u58e8trWMqSnjbpf0nmzVE8exblSekmTkbhKPBjUgaumuo/WfIJ7KM6ybLx/RzhxpXAAFbTWhDos+0RpOL3FivpRwYYRFraY20c7AkpszcqGtRD9o20RpzEDP9wHEjOKgyJ30J7iKoSh4poP0H+c6suOmGybLLAza509D8M5/l7j3rwKn9xzx+1dr/Xwsb+OvY7MeNX4t8bqfsT/tZJW8EKJk4HLvmvzi2fXdtYYxtHvfBdBbYI0keM/SFeyTskUaMgfnaqiebRljwD2pRa77iJgHM+THTdj4dkXJOzafS7FTaM2VVW+moxgGa/HrzTWOp3/xXanDjVMkrx3Muke2tEuWDQbXy5mZNnV9egyuWGXVla67bxbqYaPi5OpOy1+NBZ07wW5sqqZ/4x9+q3vf2uwO5v+dNr1S/+ZkvqrVa3OAKFOs/2eBADWo46kyrTVqRuIG+MABwtubLC+3Vb9u8ogBrvrL8amseagW+4hWvEF+BXGevr6/Ln9YKJAh48uRJAQXTWoukzZ+lHUWZQgKREuA8s8fm+T3Hb2QHFDfHJYECAByX5EdQrzYBZlX8mqcBwWFUPWoAMAz445dLmihoftK2df/SnPqp7zijvvLUirr3oRVVH0I0Ns0TTZi4+OXXevqN61vsCggIMAb5ptd9raYMC2Jdn+uXJoliSgW6ZuTf6vV2dzHqKjeMa1E+6ULrg2xS72MI+qVM+xbn1NUrG32AR0oyw8uevkmqA1Mxc+VDQInAH01w8wT+2Ogo9kzzMdEGG1BKpeaURFcekIz4cTSfydoKxlqr/wEkYGeb1wXqxnij5p/ZzsB960S0/+ivy5Vwmaa01NRdP4N/wvK5yuZ9jSyaHct2+r7/WFUbAW/o0kAnUwPwOoA/jrXRoDiag8F/pbloUq9Vg9P8ZqAwRb+X0OiSdwy73Wo05Wo+Q4wm25fJKDMS81+jvr10WNmBBQHkzXUFzXN39vt+aI1GsB8GSsDQqKFp9yNplq0AIMYtx94AAEAASURBVPX1mjq7/5J6FAFANMBl1i19bV7gMT6YTKWd2zCI3nzHGfUvfvS/gsUHGu9I7777TvXxrz3ouJP8UmeLAKA7NTpttThbVde3+wFCmgC7Ek2AJRlz6Tg1ADWP+oM619L0/ce1Nf/oK5BA4AsvvCAf3XnMP2oFcu1NM+EoX4HUEi9SIYFCAoUECgmMVwIFADhe+Q+1dhPw29zcHCoAqLUL9aJhWA2jKYL28afr0iYL1PiLWngk5YkLnleeXFL/9G3fqv7NZ76mPv/IM+6VblKCEfm4gKZmTFcb0PFENpe4kabvMWgDcuHOBZTri3lEPWG3GORDvsKCZt13IF4G4lgGiDLKlAr806yB58QbQb3o1GtP0kghwxrz8n9dfpTCiasrI0/ccAM/leiIO9QYwZ4pb+CPrJO9MO0/jmVTA9DzE6c7mKXHl2yeZy+7+WqHmfKTdbSPAT+0Vlxca0K1/3RBjkHsF9cA/pngmr49yl/yaia6KjCDk5jaf8xXJiiB3/XzZc+M3iy8R44ra7uqtZ/MWo2fcP6noM0tJp9VH/kZMfvTq3jO8d/OsjfH2OIS8E/zhEGiP0rZ+eRcJhQc6fzOTPlcZBUZp9d8GMhAhf7/5i61MZ/DFyA0cj0gPkjIaXUQzBJ+hv4haBcm/r4AIPD/d+7Uqnrs2oF+mgT6qDFsEYvsf5sK5tjlyrT6vf/xe9Utxw/ZdwPnP/G9b1B/8OUHERAkcDnVSVwgkAML024AMMQEeI4RSqzUGlEQEKvawKleX9sf05eXl8UH4G233Sa+Agn+ERSkZuBDDz2kHn30UXX06FHRCmTeQssvINbipJBAIYFCAhMhAQfcMBF8FUzkIAEbAMyBZCgJvUjQZgOhGTPeGAXwp1nTYGYZ2iu/9oG3qwefvKR++d//FUxYrIUadwbWwlXTSPtLYKmKRTXNL9vTOLG+frfmAQJi7zZzFbewAB9oAe8zN0WTVn8XTwCOPoQEsFiH9h/MaEaVRIz8J0nS8mZ+bBz0qbOoCfpJJcjFAimAP013dR2dAxpJtbh0uaH/JpWbg5FdjDGaYHIsDQP461YpMu+eBQ640TP7MCooSaBgxImpSRSRLfIWozibqqVVaPCVGyanveKmv63eVRxh/DH6twmKBe47TiTwh/Xsm9mwX1ZbNxOsdfNi5h36MacIw5quCp+lZjL9/1HTmHev3V4B4DwBvJuMJjyeguYl3Jip6qVdtXMEhTLMIwmryjcbnjF+0KnhA9LWMfQC+LbB23wrDFJj31e2UCcuU9Pd7n1eN99nJbpRszOZJDlnRN038w54rF8hA5IZefEOXFbw415jPz4UcD4x5UWBZ329Q/ZR4B/nzepCT/uN/v8a170AIJ95+nyfHDg/OgEilFPhinZdOhJUCYHK3vuuV8WCfyxUrZTV206fVn9+8UKXRvoDBAJZx3tzH5nsT4tz7m3VRn1WNVow0a4YkyaKz/kmwObHxeYQLU/6OXZfCQMAdW5+bKfpL/8I/tEVj9YKpC9u/i0uLna1AvmhvkiFBAoJFBIoJDAZEsi6DJgM7gsuIiVgmgBvbQ0vwi2Z0ACgXjREMpbiJoG/J598Un3xi19UFy5cEJMDLiTOnz+v7rnnHnUai7k8tP5MljQAqMHMV54/qj71v7xXvecNL8+8bjbpRx1TU6iKzVIJX+7t1J6dUtuH8VU/p10JNf1kXwB69WVvh1BbGz34x3aa+xO73c5z8OycvCgb/hEp4fpcr9GZWTZCqWuSjet2Ax0DktxwTEzKyAo3/x3sUWTTDZCb/w0rkcUosMHU/iMPtkP51HxhbyUaeakLBguYgATvzL7olpGYNLr3e7JRlkAGQdKhZ5SVmP+G5sANRMacFADN7FeaeFZ2egOyDR+Mu2Ji7jWGPkVX7ty74J98GAFYwTkA23818xIOOM9MeiK/+G7F54zP1sxln2f3cB5Ka8T8HZTpJ9M11wS0/5DP9hVpM2UCJfa9vM/pH3evpemrLXXwwU1VwQeL+r5+8195brP0P4YOo/VGyb90oAnT3Z7EGhtQtUOF55ahAXi1XwOwTLA3S8L7vQJ3A9Pwd8jxMj9LHyrJ0j//gbfA4mKwZ3dqPRydnIUfxrB0zWEGPDftf1g2WGq1xz/u9Fper+3D2sTrNP99+ctfrt761reKv0Bq/jFdv35dPfzww+pzn/ucevDBB9W1a9cwbULr00fw9a9kLv4pJDAGCXA+24t/YxBVUeUNJoGQrcsN1spv0ubMzc11W76xAQc8Q0x6kaAXDYNWReBPRxzTNAn88Wsj/YzkDfqZ/LrawoXKB7/vHvXfvPkV6n/+6GfUU1fg0XxIagh8GVW4JgRA11zARtrwo8XNPzWAqAlYHmCNWN4G0Cf2xKgHi+nOdEU0BkRbKcvmwBRgwuNdgnQA5RJXpzNy421+RNcbcVwWkM774W7T/9MFEzJmZSNYxr6mDosAVqxvSH1vVR1+yramTNz4edp+nlhSFs+UPW6zafv8YwCPQRLHbweBdQZJov1nDJkyvp1w4+tK9JfoSmWAYdWU31wiff+xEo55A1Rz1TvKa2Ii7VdYRcRwM5nmv23sh+uHINAIzUaz7EDHfC6MvhuIllFYAhWA8BSfffwvIOBlTNGHcTLuucDg0z6kRi2fsRJ5xIuF7SgjsFS7tzSwi+R7jjm+6i89Wo462V0m2M6osu4vOwZbHGpD6GOjhu7hiKrp1jfIATX+Fp+qq5lrLUT9RSRgEBMg3sKjzOc2TX0E6/D9ITKVDvgBLfxcjfVpNY1J+cjspnpixQIA8VHAZf4bWQGeP74jqtD6M3mZnwuZiB3EDuybV3cvH1b3b15x3E146TqF2tN0NEtFKbqtbM2rI0trZnY1V/VlZsh20nwABhiOOOG6nOtz/lErUPsKbLVaohFIrUAqJmh/ghGkiluFBAoJFBIoJDBECRQA4BCFO27S1GSbnZ2VACDD1gC0teaytt0F/HFRoYN7DBP40zxHteXIgUX173/h3eo/ff4h9Tt/eh/25MaqTRPI49ffVE5fhc8p+I42N9S7MP/jxlNAQGzuUicsouULODfkON7Z732yH6X23y6/wKMdBDtTJ5QpcSPOxB/86Y2anPJE/vRVZkyWZNMEAIkbJ8/MEtFb4cNNEuoUfllJetI+kRx+WH/CJFnB66iBP7Kn645i1dYATGMu66Jr03PlibpGQ1UTkGDe2UsEf90d7or+S+1dDXpE1WXeo6witf+wWVbGhwCz7NiOtUjExFN6u8tKc86bUxhYpj2LyxMMknWZDjuA7OWDDNqrm8ysdMUwAwxh5yDaPgpwM4y/kOtTPj6hQTX2EHtldmVXbdAMm5PZkPuFzwGBGrqYaDuAeVv7TzRmTSHbbYPMh8yyXePkn+O9NHu5peaf3pGPgvLBECbAOwdh/kthmfJEX9jzW5IGUos0ifuLyr4gKFYHAHh2/5p6YXNRbbeCIF2o+S/aQ5DSZJs8SiAZAH8uf6oLc8k1AEnrf3rPm9UP/ts/zvzcdrYtVJVEdYr4MrsCOdhJBwEx10LNCfYBaPMfdk6twDvvvFPdeuut6tKlSwIGrq6uKiojUDOw0P4Lk1xxvZBAIYFCAsOXQAEADl/GY62BfgAZAXjYAKBLay5NwycB+NP8JmnLu779TnX76cPqJ/+3/6SL5f+LxTu1mWqIAEwNpAasKrRZHRf524cAAiIyaSX40T2Wj/I2dlFa+w+L7fZMBeY0bWwOvU17LIFBMlDrj+DfNHRo8JM46d0A+FUwNzRBP9IQUpr9FDvEBmbA3RkP7JONEesxyldo9tetmzVBdABdwzS/vBxD/DehzLQ89EZKN2GInPWRjtP+Y4ESgS0jiUYgUVjdl8a9kRxK//dqYsCNMDCPkXhtP1UMhMGgH2nlLeBICIjE6LmhfgZ7rI78SGsSUfvPbC9BUX6kaGLukufEvDlyLgevUD4A6DkhOFwFFOGHmIkDAQmUQWOUGlaadXmmgMTRjcH8iwABb4ZshgkqY66m+S+TgNtdRrxrWiPZO8O/plZ392LwIHHQp2Cx7GcTPnbpxmPpGzuQc094nZoHTjX2QQvQWuHLnJxybhUt0iCu55Yn+tsMAOL5/6upc7esSgRgu1CY+a98xDHHCtYMjC7O/GHdkUYDkHzcevqIOldeUN/Y5Qs+fZJAIBS5Awds2WrtBvkVRyAQDQAa2dRe8AFo8ht1bGoFEvhjED9qCBapkEAhgUIChQTGJwFreTA+RoqahyMBqttfuXJFvroNpwaPahLQzFX/JAF/mr8oDUCdh7/njx2EQ+cyHDv3Ft/m/VyOsRDm5qmyha/8V6ZUYxEagTSl4gIZgAE3ntPXAFIk9aWDxbQssLkJwIJ9B5F/eXjTzKxaBVA8tCT1om5U0EawkUzgH5kDnbKxCddAl/BtbhrkQvQ/DfhUbAP8I1jhTJAPI5fqHbTmmf7MwrciTkr5XDTaHUaQWQSYQaeyVSEtCyue23VhNUHltsae8IzHSYNLaRkaBChzaf/NXaIM3Q2xQWBxSA/Aw507vCWUVZNaclbiOKtda6s6NHn0GLSyjPVU+gg8VjE3makB7b8G5iiJOmve2IPH5W3MWQSkdaeyqfrYbw81o/ghZucAboaAuCNtOtngRyHwSXNJ0yeb5oPBn2av7KrtI/pK/r9ibsxnGaSd5r9UDTTmbAaXsGXbx5XZF303878ggFn+ZAeniHfT3KWmmn+mHjCF7dBFAEBd+iZt85gvdyOlbg8AZI4la8gbFPUhwD98WKzMo4CfGtc9/39n4P/v8dWg+S/nSg/o07mNXw4YVsj3L9Y1Yu5r3HYdzqfUACSND/6Te9SH7/2si1z8NTxUEghkPwdkMO3shn+RvQYTYDvN+kFAvK+Y3t29agJst80+537k9ttvx2MfP6LsssV5IYGhSIDzDf/2Utpr/O4l2X4T8WotD76JWv5N0lQdCXjSNAAJ/H3jG99Q9913n3r66acluAe/FJ47d0694Q1vUGfOnBmqn7+o7k8KAFZgZnP2yP4oUvncw6ZSTICxIJ4GwECNE5rDSMJCqg4WuOFOkipb9P3nLb4OHlwAAFZW33LiiFq9NkTwD4t9anpx0dem2W8SRl150H6atcn7GkQEgKAGC+iaG0lXUfsawT8B/lg2JIk/OvO+L/IkplAhJLNfjnnh87YOSMENf3irsrOQpqT0TQImXLLMGgiEG0oblEvDswjN5Bl72dq6eaFHjfI2zX8FrCP4F9NPPQq9I9H+MzWx+Jxf66j55yYX/CP37OPKFiOJ99pCrcitE4iQeSMEfCS4KaCUPwY4//SaGjii0g8/xBC0GHcS8I9zQA+L8VjSqz00QoK2ABysWJGb8+Rda/8xKI/WXNf0qSk7jajE3USNxX4spXtbDpgnrAOCObOdoe/ENUWAj2FWmI3NMt7hB762pRYvBME/SnPX1/7bPoxv+/JeNOpAhlQR0jF+aJKdRAJ8L5cOBQOA0PyXSQKAWP7/Qs1/WQB8UuuZAT6m6euP12LSQgofgJrUW193qzrYzK4DMbXmUP8D8evt8K+xV8M0ANFmc2W014KAaJkm+WUQEKYCBEwirSJPIYFCAoUEhiOB7G+/4fBTUM1ZAhoA3NzMZuqQlJ2koBmBPzoGZoAPOgZmIvDH4B78G4WPv7g26bbo4CNR+c8fO6AefX4AZ9JRxM17BAF9TcByk9ob0Bpa2MUfMmGh39iHdTNU4yLNDwWE85bTJdCrQ/vvWG1RPfYA1JyGkaCtV2piNwWglDyKyWSi7YTBjLn7EP5xj9cy7gS59GzMgwB4EjJgz1uOGnX6h2WJxOwzYGzs+zbW/UXzvRLGIGrhLW267HOab90ZqImWSRJmIFNbA5DVEUwBlpc60U9Vx6FJl4gQKly4oNTmadCA6SrT7IsYZiEqM7KR1jtTjHOJdBoADjwacf+y/7q+/yAParwsPA9mIL+1s5Op+ddtE9pbMwAkmvyunceSwgQzu5n33gE/tgTmmYjnkK1jNNJpaALWqQmYcX4aVEra7x/p8Hkwp1vxB8cb5I3zGX7pD/A6cZowLWjmz5CoOaldU7i0/wju1OAfsLlEFxSIek/MJGbOiAUIM/BpFhHZ8ZnGuOYcRIBbPmSYmcZ5jHlm4dm6mr3YdIJiArL6H/cai/3mv8K6nrPi2gEZJAX/OJbopmT31qCdMAOAUKv63NKq+jdWBGDpb5sH0OHHn8p6R1WDpOycfedZNABJ5Ie/7VXqd7/05T56SS7sbnL71M/oapPRn/o1/UjzmgMAnGUQED6PxgMwSSbAej1M/otUSKCQQCGBQgI3hgSSLgdujNZ+E7aCKvdM49YAJNj31FNPicYff3lOsO/s2bOi8cffSQD/KCttztzpxO/oz98cNG1h+aElbKzb1FzDYpF7JW6gCATyazlTcxGgHvwEhnF96/GDko//3HrrEfXixpZaapRUpx1Wops91cFUva3KGy0v0AjBPyTRTsOGM2aPF6zHzMwFMtnkBifj5ppSaiyAn4oH/rEyU3uJ5zpNQab2Pc2O7bdOlxnKr9e1faR5mYAqgSiKQ/PWl3HEF8hXCGbWxwk39C6+M2sADjCMGcxhenNK7X8YvzjmWJu+5uLOawa1miRhXAr4lwWxBIE2wBf686RJHIG/fRfgixNCXD+DzvU3835NE/cze7Wn/bezPKXWXjYh4F/IM5NGgK7nP0l5AQHHpQnI54njkMMWv9SuNafKwHPJPBhzzLAAf4AeAJGkhcnyEBBiElNUTxGsW5DvK767yML8C2AazxDlFpty6NfQOvBxiaAf5bWLZ5u+87yPEx44KeDgAPNLaL0Jb1TXW+rgVzfUfBj4Bzpa+68F3uVjm7W6Z18kSminF/U6UW5xT0LSpcO9Tuy08VFyA4LES/TY/HX19Cq+UPqJ2qcmmEvt6Qp8/M2+hD/MKWnBP5JN6wNQ8/L+73m9mm1wJKZPuyGBQOqdllqYRic4UgNq4xv14ANRxoQ/U+nJjsXGrQHIta/W1BvGurzQ/nMMjuLSeCTA98pe/BuPtIpabyAJuN9SN1ADv9mbMioNQA2a2VpzBPqo7TfJGn/2GNFfPCcOAASj/MpPk8HyDhbR2K1w00KT4Nb8LvwD8hcAIRaUNEcz1/sHlmbVMxegnoJUQrnn8ZX6VSdvUk98+aJcG/SfqQb8/2xjxwkthc4sog/CtFgnRoDcBZiRbZntU+GGlruzjIl7t+YiTBPJh7GRlGOeW6Sr0GAJ7p57Fbu01np3czrCpriySdNp8A2ttt2q15uyTqFowa/Fck4VD0iGTCVkLEyT0gO0ExIx2c1QhMXJB4F0OQZKsvgMwD9E3y5x4DoS+0B8DRL8Q8APV1RKR7G+S6RD7T9qQ81h88seZpWrAP8kkmdfiQm6gE37DMyUmTaPlNT2TeB+gOczr5axxyjXgRL6dRoAld0ec96Ios9I1rVVfGzYB05GJRNUpU1/yRvdF1B7LTCCzRPwReCFshJ/gJcV+jCqVcnviXnxjleZaLdaMpjBeNes0MR6GlqIHURdj0xDBt/40aH7LgAj/LDCZ7EEcIiAvKJGIP9wjfdEM5AP7JCTfBiAn7/Zl2BeG1HXLj5q6Q8GOy7zX5RNFP0XbaUvxqi6TDY4hrT/YTMCcNP3/3dkcUNd3FrAsqC3HhDzXxDhGoYuBMRPpUk05fHsTBUK/dk6g2uo737ZefWpC0+krBXjown/yRgTyrGLOrA4DaCPN/sTA4EsTFM9t5fma3X4YO4Bg60OXvxjTOY6Xq/tx8hOUXUhgUIChQQKCeQsAcerK+caCnJjlcCoAEAbNIsC/hgBrFqdXCdResFjLoLCOvH80QPqW44fUV9/8bJqtoa8S/GZoO864Gyy4eNCnX9cVNN8sL4MkypoCe7wqzLwPtm84P7x/YvqkYueGfitt92kHt1aV53HPEDQJ5v6R4N+Jb/d9CfYsb58UyJc+yfdUHSZMAtgQ67b0b2f4oA8mOCfSVrIyC7YIIhNjWwGjYzm5r9EzZlhJrS3CvCv4u8RSjBF7EBlgubeHfTtpH49p1QCWkYxMnL5/2ORRNpADtp2xEtHFuelGYAfpkYK21GCmX1Y8gKNoI8AEhHsyZpqcxU1/XRdVfx9IscpNf9Cg9JkrWgI5Wbgv41A+NrpsmouZ9t8D4GtXEhS+8kJ3OnHnr/hw0N4kGd3DSAg5mMnrVw47RExwT8xYQWgZWAuXkabZ3Yb5zJ8VKoQjAGY3YIW+aCJGrGk0sE8Jr5rDYIl1FOzvZGEAO1GMXk+B+fMpGgcS6An8IwH0JznKT9q6PIZ53XWL/cpWxwTBBRNO/+DjEExl8PatZZaRITfivb3G0JVeEFgLZ2aCw7zX2bqZdFZ+37LBP+YN0lC/3Y1PWsw6Z7rAV5d/38HrqnHrgWtJKqYO2ZW8U5PUkeCPPOzWh07QWZHln/2njerP/1Xj6tWajN4PxDIAc7cwbQ4F76+JQB46gC+2hpprgoA0Dhv5myVYZBOdGiuffXaPlHBFJkmdR2ToglF1kIChQQKCexZCRQA4J7tumSMj9oEWJv6ujT+Jh340xLVC54kGoBL8zNq5eEVdQiL5pvvPKIevbKiNutBcw5NN89f0ZjAAtwESwioEPRrzUIbcAkaHYc87cCbof33yMOenz8qY1xs7ahb9u1Tjz2bXvvPBP24UeBeoYPNB8E/rYEg7QRCuQsgTc3Q3Dbl1s3MjjZS+yJrigX/QJgbP9PXk0Q1tbRWTLWioWoAor1V+FXT/rPYboJq3JhTLJO8aBbwz+w7Mh+RwuQoYEZEOdctavExAEiK6oUMTfuoQWsmAolh2n/MRwCQIJEGaM2yaY6PNWrqcstDefkcXT9ZBuCQtgVpaswpL57t2tW2WoXJLz823FAJc5b0q6tZ7KQUiT7UOCAbcMswzGT6/WM9ErwIv31TmI24IIPWAmTmWQAzG9AuHwiApkYYtSeR6EvT1mSliaeZCKB1eopP5q3gcUrZBwtHn1GDt6stadcDmREE5JxUMljn8OB7g9d2CQgin3yAIBg4YJqC39wlfBiYvtpKNJ+JdrjvLqAJXsXU1+rrJHMz/fKZbYxrBn3kdvMfh4ai8czU1z1Q7uzyWgAApHb3LMA/I2tcNbH3s/r/04QX5qfV6w/drL6w5q2R9PUkvwwEsusAAGex7glLK9CItNOcjgTs3xh3FGATAByGCbDd/uK8kEAhgUIChQRGK4Hwt9Ro+ShqG5IERqUBaLKvffxRk+7MmTPqnnvuUWfPnp1orT+T/zQAIMudPL5fbazV1eP3Padmn2uq1xy9Se0DMDjs1AFY0K4EdyxcWNOsahZaTQQEdw5jQ0D0ys9G33/Ts1X16IMpwD9sSKrrTWxIsCnBbxnnBP868F3WWgAIAE0mAf8ADBAgnGqgYqooUivB3BVkEQjIZJ2kuF9rLqG0b34ctukwtb/It1MzjUCkn2TTY5zr6wP/giaDKpjgHzdzzSVwzg1eWAMGrnhwAiKdlPyF+VLMolXHDXrK6qXRXqCPYPtNMDh4B48Ru4EgB56xQdIrTt2kLl+CypWfNo6VxHxfn0/ybwX+3dZvRPAPQp9ml4TMWYk1o4zOozll1feHZ1zO7xCTnADpevDzHM+CawwLCGTXzHJaoxntnr+EJ3mAuY2+/fR83ZzTTHmVMjCINhfVbNB3ZJi8dR55d/Wm3+7lPA5KALGo6aeBLGcfg0V+XBBtP0elbCXLc96mBp18wMArMEuaudJUh766hY8SCcE/VLJraP/tHMLXCce7wjUeTP4YMEbMWc2LUcd4T9LqoJuO9D560v9fk/7/kM4tQwNw5WA3GyM/B0dF91bmg4W5JAhyNPmf+4G3yLwenav/rhcIpP96NcKk3RUIZA4mwGaaJABQr4dN/orjQgI3igQ45+/FvxtF/kU7xieBQgNwfLIfSc3DBgCp8ceovhcuXOi2h8Cfjuo7yaa+XYatgzQmwCx66sR+9cDXnxcq9e2meuzvn1dl+OS5+66j8LW3rV5c9VUirHryOCUIOIUNWwmLbjNxQzIDP4At4JAXFnfU3Bw2t9icXO7sqCq0BIO5zZL+MUE/+PRjFF97U8SNpJj7cuPBlydAPzngxgOgIKlT88PW/nDU0n/JZIwb0YwbKXIk4B82tdw7RpkQm+2rbLFBJhMei/YVapzthlv59Lcr7grBv+tBjc4W6IuGlckPgVXKecKSbDBTshWmAZgFAAyAuAllw00vfWWaiZv8KO2/XQyWwMbXLJziePNSb/e8ebgEjV0Nm6QgMoasuxh/zf3o6Akcg4OKgwCVjKOU4ziu3qqYFENuOZjXBuriVADQqYu48RA4DEeS06rWm66Dcz/6sasFiHI4g785aI8fwUnahDmsCvNfpjbqImjWTbjXp/2Hm7aJcDe/ccB5wpwCjVuDHfofe6hNz6AvkvyfPsIYE2wP/QCKv8C+DN4FDh3yy49IfE9yXhTNQHZKRCrVofX31I6aXk33whPtP0M4rTkE9bK1ENGmPnNwkxeOGfwlH/boZ3yoMvOXDvYAwAb9/+EuPUyeRQTgj119dbc2+nvMO2UNAGLycerYAXXb9LJ6pOUPYPNmxHFYIJCpMlcg7kQTYDvZGoDNdrpxYNMb9NzUANTr4UFpsrwOLMLjSbZmIH9FKiRQSKCQwI0sgQIAvJF7F23TAGDeUYA18PfMM89IRF9TjK997Wu79ZrX98qx/uKZxASYbTp1Iujjhtfa8Iv3+JdekFX1t7zyqFqBo69nrsApVN4Ji38GBVHYvLqAC5qh0SSsvq+k9h+eVYeW59WjT4AvVwLP1S036MfsXLqLue90GRtlbAAADgoQ4IN+miS11hisZOBE8hmIcPNLn3/cNZJnE+BzkmMm2ZcAgMMGL7Cz0QXkvj7xNngI6JdPcoB/YspFc1BjcyfHAgDmU21eVEQ0GbrbqWkJprgZ9ZxcpiCaIqtut2j/6RP/1wMArYv6FP0k/sD0ecbfO08dVk9/2TM32wKYtnMgA/MZ6x6kGJoPNSf8mWNyEIKTVBbPlWh1RrXNmgPSsE/QmOBxayG/vrbBP85hBLXJprMZvLiLSdW+SZZ8X4BsUwVacQTyRPOYFxKmCtqocY/WPAoZ9VAT0jaZZ9CqREAysZT8xNZtDd+LAs7xXab7Vv92cwUPGACE39qSzAOkKeAlsRyKHsCcgHPWS232UkMtINCH1kIM1hh+RlZN7b8GwD8GubJfmqL5adXZpYr3HfsljXhpxmsFrVXlfQYAuOZr5KHOQzP4AArAi/Ql6AfN4nNOeQCAZOln/us3qg/+yZ9DGMmlIYFA2HRrLdAKe7khq9sEGJ1gpEnRAORauADqjI4pDgsJFBIoJHCDSKAAAG+QjgxrhvYBuLnZ0zgJy5vkugv44xfCY8eOSaRf0tjrC4b0AOD+cNFhlf7kA5dkU/aK2w+rF6CBt7KT8yoYC9Y2oolOQXuNER3txI3INDZ0l2vb6tr6dnCtCtCvDNCv4tD0M+kQVCP450XRBUHRAOqvixoPmcE/kxzRBm78UiYxmV0AE5AJKMjGziTrIif3kVl8Z4Ut/knMSNSaAXXjSsZDC/xjkyWScwiASg0li5WMFedTjLzEmZc5a0K7wzQAKVWaowU0iJxEehfTgrE01atZeDzHTqgmoc9vDj2u6i+hcqQdACCTEj23J0n3kYw5ahaFPR/uYuO5muHRFL91UW1j/w/YGq8OgICI1D5osv3+kR6vUdNZxnFYBRjjfRMItQABABLckgQ5MEgDI/im8Qc47T9PMoeZHjAgu9kr/ZO5fKTxqwz9kYEXejf7DYJ+nEsR+IFt16kLBOoLrl88B2ZwEFcW85r0Nqqg1jj/OJBEIw+BHpaf3IHmdzZtLwH/jDG7cxD+6Ni/1vByLAk89tB+fiC0spus9x+jL6s9zwVyvzPXgVuRXht0AJCZWlOt16dB36thGOa/ZCAPE2DSuedVZ9XhT1SwTuq1hdcj0xQiAcMPYOdQsEx9l6q57rS6NafaWFCVDbMEWwNwUgDAPLX/TGns9T2C2ZbiuJBAIYFCAntRAnrJtxd5L3hOIAGtATgoAKiBPwb3aDa9r71cHGhTX4JmvMdkmg8kYHHisuhFT9J20AdgXOIS+MIjcMyHtH+5qtZmdj2Aw1jAy82s/4AON2w0YdULbpsUfRRxM9DAB/q5y54vP24Gt47W1DbAvZmVBnzQBTdqPNuFOTM3SvyNSqTdIXCVpU0UkJlQcXRtZmbvOAv4p6lwM0jNFyfvjs1/xAd+TTL+F3QZRVG0DpGbshYzQQFX3cW5QcX/k5PYb3bfJeBONGMi8lELMCkAKGCIpYERQVpuzUIBz2abwT1sU3rJzP7H/s7OH1eH6/4dJw+rC1+5pBp4VjePAUnI8qy4CA/pGpruNZzg3w2aaP4Zqvmr25zDQ8fxQ/CEmoBtyz+eribRLyYKAc/NCRL8MQq8pIiBqruzrx6W4UcN0ebGMcYl/QFunPCO+/JbF2g+recx0UjXdJCPAXPMYFUsSg3nJB+KOC8P4xGhRqIXzduXpW5P0n6G7F3BQTSZsF/pGtQhLiQwo2yemFGta00182Ij0k2FTY/vWjH/9W+Q7fasw/wX17vArkmE7zv0S8RQMXN3j2n6aw473tg95q0Hedxpwf/fpjcZn9m/qh5f7a2NhmH+yzrz0gAkre+/6xb1+w8/ysPkaR2TowUAbuBDb1iiBFe35tXBhZ5rmFnLB+CkRAHWa+GwthTXCwnseQlw8kw6709KY/cav5Mit4KPgAQKADAgjhvvRAOAWU2Aw4A/RvQ9depUN7CH6dsjqenspEo7rQbgMqLsLi/NqLX18EWf2dbWWlPNQ1uiBVc5jWV8PcbCPZf5HMBRC77+okBA0XDA5mwK2gda20FAO1zbOTwNc8Rd+EVDwA9oJRCQEi2DMsxACILA118HQKBrR0b+w+6ZbU90TNSBladIbazBW/PYmvi7RW5s0mxuaNYUmhy3TK2R0HJRN6CBIj7/qBGC5IGXbtl6Obx/QzXUzEwjOqZYQrVLYngI0/7TxcQMWJ/E/BJcaKd4k9FUUUcqFdIcbhR9UInDqzVH8I8EWyt1Rd+OjPirx6pX0eT9K+Afd/v2jn/yWM3OERpZo3J8CpSJ84rIJs0E43PIIjVoYzcIAmaJoIyx2mf6S/Yxj1ChiM9kZFOEeZ8Z84dTJ54j01ccPyQl9QfINunEd1A3QVAu7b/6csJBlfI90K034qA7d/NDC/gz51T9Towo3ruF4vKRgrL35/Hezfgjypf+B7ePTOOvBvNtaNJdBRiIj3Nx0rF9/9GHaIcdbxfkgLCvEfwDAMqhkCYxYIoZoEqXnTrS03ZrXKf5r0f53PIqIgB7AUDK+DDpKqtpDPK7MOubHA9CxC/7na89o/6PLz+itlM8m7ub/V9HVpt0/Gk+CEHmaAZsAoBz05NpAjwMAFBr/+nfoGSKs0IChQQKCRQSGIUEUmybRsFOUUfeEshqAkzg7/nnn1f08Wdq/NnAn+aXL3MCZwT/kmrO6bKT9qsXPQQ12R4NCEbxefL4AQCAIb71QgpyQVy5DKANuzeahTX2VTwfPiH5E10mCKg1AcN2grjemq+o8npLNowC3IG4LNsBBNYP1VQdQGB1ow2fgNQo5E38i/9LTciEs4ahpcY9xkDgn1TASvyEDYq9Z9G3XL8E/5ow+9Waj7IJ5j8pUoUAYJi8HHQG0gAE+DcNzT9Ng+CVOMNPUb+DpZFfEvDP7ruEXOi2h2VPAwCmBYtF+0+PD/+3gz1knw9NggM5af6xnbceP6Se+dqLav0cBqzx/ITJYJzX0XTvIUzzII6T4Yx1e1pQ8YM4FTAUwwtro/m5gIAz8XWb5FzgH+/T9x8TTUAjKYbd5Nwj9sPoeGNsJvEHKD7h/G9fnIsJaulEoF1rBuprkicJZsMxyL8eOU1ioF+CX9TeYxINQ+/Q+5f1pUngjZqEnAs5Z2VnFWAgfOtuQyt4++ZpaHMCDLzcQCCv/mjAtvYf2d3Zj5cIn1WLAX5YshM1RVOPZ7yzqptu4ZQO9tDP+hq+aiIxAAgBwPsunJDzaZiUDyvNzycZTMlq55rvdfsW1N/Uk7vMcQUC2W431VytrLYarq9K8ANoBQKxowAXGoDJ+qvIVUigkEAhgUIC2STgWB5kI1SUmkwJaA3ApCbABP4Y0fe+++5TTz75pIB/BMROnz6t3vCGN6jz5893tf7sFmvgbK8DgCbgl1Sb8fTJnqmLLZe48xJW9DVslA6vTqnXHDuiFgf9og0Qj9olplamzUPtyJwH/nGzhvx9CZvA5lJFbR2pqDrAta4iBq6X6DPJ95vEZb2Af8amsY9W1AW7aoIu3cqiCnr3CJ65wD+bbCQl8ecXnsO1WfJ8AIaXCb1jgX80hduT4B8bmErIQYk4te2MLGkAQAEijbJRhwxuINp/GLjsVzaBY7ivj/1xOEAT+9horeyozf3Q9s2TaF8tg18Q8I9KLTf66gDPvR2YIlR6HCQ5Jg6B2ipETLcDCZPL758UBcZQ1jhDzNiKHHvo76meMpfHFYBB+gOcwoefsMRnSlcb0P7DXDdztX8yry8jd5KPHSiaJFsYX67rlLf4yfPfV/Y8JO2QB8BVOvyaBAfBXB4upfCyfXfQ6PYswKNTs2rlVQtq9ZZZVV/iA+mlXQBLpmBgdYtAYAAQe1l01oBGJy8S/EsbbITl+CHQcFnHS91UWe4NGu3/jwOCEYAfWzkAoeCD1xCi/2oGFmY90FGfD/LLtet3vPIwtBWT92SnhQenZwXdrf7AoukIs3tZDq7BBNhMs9M9GfL6je4D0Gx7cVxIoJBAIYFCAqOXAHV5inQDS0ADgI1GQ6L1ViruLufC57nnnnNq/NHPX60Wv8giAEhtwW9GAJAagIOm7Y2meuzvnlO16Yp6zauOqAtbG+rKdZqSpE/0r0RfTIy85zK12Gg01cvOHVDPXIYqChN3Pq41LzYjrYWymNeWtzswX6NGJEBALOo79bbahSahqTEitLL+w/qhTCCbsAQ0aErZQuRDW/MvaXldRZmb26idpmNDmMXki5FtteYfm9qEhZDpx0nzsxd+Xc7m0/BdigFdRdMpIcGkvgJJbg5Kuvb4EF9eJjKC/hbNINfzkJAnO9uZm/arp55bUTUA59N4pNvTu+h/T1s3iS80m96wzqXJBBJsIQ2rwjHSnWYwg6jn3uQtx7GgyVLE09cQoR3a1qbWnL4f+CUYRpDPAcpK8CLcIouxzYnKwHtEwjFPBeZ0XBd/gMdZf3Bg0JyWEY6ZCPVR+1wncXGggUn/ouRJ6P8wzYcgXWfkL9rF6L1a+495nXWIICMpuW+ib0hbIgSTRh4Jsm/jHbtxFn/gv7LZxjsEmvl4r1O7j3556zD/xYzVPzbIgzFeqCUqQUhS8kXwV48xu2hnsa3K0+xV9D+QyNaW5/+P74c5qH5uNGbAM/h2AGQ2raznefoA5AffGmT66oUl9Q8NK9pJGIMMBHINrlxuCg72pTnI4qq7UKwGYCdIy01leFf1Gl5/1B9GTa516TDqKWgWEoiVQF7zdWxFRYZCApMjATcaNDn8FZwMKAFtAkwy1AJcXl4OUMwD+NMEteZcUq05XW7Sfs1Fj14IxfF46kR2DUCbdqPeUo/9w/NqCputV991VL20W1fPryRcjBrEGLijzA0EQUDjuj6cO76o2usJ6ZLOHMyT4K9QgMCVFjY6MPfmhs+heaDriPy1mQLwYuxXIova4B8zk5xNMpIIb3KzG5ccWWQzTmAQckmUUI8G/7hdai6gnEvzMo4YqyM/CauNI5flvkMcqcnYmjc2gaQagAQKd709p02i73x6BSb3QVdLAlrYoswb/GMlz1wE0oP+JmDC+sgHzSt3cblTBRgIjV0BhA3zyb4GDPmC9CsfQFsgQ643b/JkXzD7iHYwaIWAPxF5TL76NETNmwMcs3oBAfdHgIDomDDTX44t/azEmv+iLhPndrJN5TI+U5byEj+yiD/Ao8FSDGqi52wB/zRAiPlu5mr/TNFA5GsbRAxS9M9YlH8J+8dJw7rIiLfi49Dg0dWvvNbPuUUs7BTCkA8KkGEWTbswsnIdfLcWK/LHAc738PRaG5r6qNTx3Epf+/KTCNFZTJRRT+06/AWHdYQRAMTT/vMrhLpgveUtDIZp/ku55BUFmLT0eu+H/tGt6v6/uD9RoBqWU9fRVgsAnMNaKSytbPJB6KVZREwuYULqyJc1AOnw0TzOpOVgroXz4CfKKiUP+gWNQgKFBAoJFBJIJoECAEwmpz2bS2sAsgEmAMgXPH380dzX9PF3/PhxCe6RROPPFopeLOjFg31/r5xrIJP8JgUzk0QCTtv+XWyinvjKRdmMvOrlR9TG7K76xktADFKkOp7w86cWoNnZiziniz/x0gq0BP1FKtftSXY9BhBYWW+qKv7a+3o+eFrTPsiBD9jhy1/NgfHLHXvCj95NKKMy4qG5KSHrrs2cUYPzkD4N40A8F12KiwCC6TTfWQHzQduN0X65IZRgH/D3mGgT7CJIwJGgZRbw0EUvw7VBtf9YZZwPQGrRJEkEP9pJAECIjL7/7ETtwSkTFYFsXf1tl0t8jj4XQEEPM+s54ymDmFALdQamlJ3yrmoBfGlgjNAvYdzYTMyHIyMDn8j4JegDQIzP6+4Yx5WDxeFcQh9X4QdumLJNwzjHB0FABmDiRxs7hYJ/yEjNrG6J7oFNwTiPm5Qxv0yBIb577DmKZpFVKIw3acLLhDw14/tRQPuP8x1I2Km5GMeAX4LPTZL22BWEnWMOpta2uFvw8wgA7Mrv4NuVLfQa+B4kOEgoXfOG/x7ehAa8DADIS75mGOL1sSQ0muOEHx7SC5Tae3RREppu6qn2NdZ764CblzbUM9eXMKHgw9e1QQUaWrvcmMvRBFiv9w4fWFCvnD+gHqgnW2+5AoFUHM+ybsmKZQLM6zO1htqqe8h7YQKsJVX8FhIoJFBIoJDAMCRQAIDDkOoE0bQBwOvXr6vf/d3fVXfffbeanvYWbATuBgH+dHO/mQHAfcuzanFhWl3fsFSMtHAG+OXy+6mvvygU7rzloOocrKpHLobYljjqeXJlU911+1H10CNBBGRjB0FAANhlStiAtJZrqrWEjQU2V2U/qiAdoqsadiHYHHLDRQfxro1g316E+80EjDSxPm5Pu8G/1C0BjwL2xBUM2b/QnCoOADTBv7yCfVB7Lq7eBKLMlIU+tGh+Rp9TBKpskCARUWwK4zQAkwKAsulNUOkMHhcCbWZit4rWFDfPTBwP+ti7Mti/BDFID+MrKZhBYJRRaWvYeBMsbs942oEB7aqUXEk7wQP9lHHcyNjhw+aP+xKeXfqQo2/E1pwfnTbumUjJwyRlFx+QSTtEMx4yB+jbg/5yHpqBhurOQYBvcN+gU6jfP2bg+PKBcrKXpElRWI6uk+PDpQXICmbWAFBD63sXmqqUo57bObfxmiS8D2YcPt84d5tt0/W5fnN9DlEBAV95NxmVhc1B7IuBuxuiyCc4iMGw41BAPTDLQCviloL1oi/4J5rRGCMVsQDojSkHGeclmv6ybNTAKh/sTar1tR4AKBGA4f+PY8QOAuOsbICLjzz0oKqo8+rYsWMqzMVNUvIaAORH4H/+T9+kfvQ/fDrRO253Bw+NlUqV8FG0BdPonWZFzVTRcX5iIBANADbxgX6cSctBr+nHyUtRdyGBYUqA873sA4ZZSc609xq/OTe/IJeTBJLsuXOqqiAzDgloALBaraqPfvSj6uUvf7n6jd/4DfXpT39aotueOnVK3XPPPeqWW25J5Ocvqg16sXAjaQAmbQv9mZw6MbgfwCj58t6zT1xVz/+XS+qW3Tn1spuS1/fA8y+qMyeX+8kbu8bw5Wp/se4VlKcfouYiTBhpDqzpwVyJfq1aCCAivs4ADBILkWTvRRJq/zUBOuUG/oER8Yek+fVZ6/shbyEpzo+dCf7lGexjbC9/yIIb6XIbQWsAUs1egQbQake0SzybyxBBWZe58baHgJXF28xGyL6bP4kwMPBmPPy8W4wH3KB3I//64F8cXwECUScEZzjgrTfsbtx4M2hSY7QKf4FzMKVcfH5Xzb3YUdXrACn9ADxG1u4hqxTgEJqNBGqa8DXfgrVZG38dAW6QgXtVNhRDuwz6BEa1uWJ1G8AOQB5qDd2IaQofJFIDEhiHfeMifFrILDYOZZqpd/uXY4g4QF/lXhXUftXDq6vtFVe7LhCVD2NUhimeib6EG/Mv4jpMFGvQWNWJ5us6TWP8aGBQX+NvfTlJ5cjIah1Vk0aWRC1vPos62r3QYJ/yYXGlHOvONTiIi1fjGocJxxCfZ0Y6ptZ57VobwydkABll+w4hn9oGTH8j5iuKqbLsAYDtZkm1toE6IgHClgAgj68cHLr2H+trNrfVQw89pD73uc+pr33ta2p93RiYzJAi6fUeAcBX3XpcnZzioiY+dYiAW99+2zGOgq9tLQQIz033CNzIGoB6TOnfgBCKk0IChQQKCRQSGIkEEq7IRsJLUckQJECfG4uLi2p2dlb9/u//vlpdXRWgb9++fRLVNw/gT7OtTWf110N9fa/9cmGSpS0nc/QDGCezF59dU5fuf1Gd3qmp4/Pz3Y1gWDkMA/Xk6ro6fADIgE7mvsA81vdT/MomA0CgK9GssANT4xb83gmIBwAisPfCibtkj1oDPtLaNC+2NjOy6ellS3UUB+BpYmGiCdMgYTlu4rkR5iazgc1xh5qWEZspXVeS39CNa5LCA+RhBEmzbj4nleYUfBsCYANIVaWvqAhwSlcdZ/7LfJS59m2my7l+TYf+rvu8NnsZm2IHoNWNmumDAWH9HEY39Dr6XOTEQY3nLkA3bqCHEOXGnn4DZ2FOt/DCrpq/2FEVjK8OeG8bYB+Bvjb2lR1qW1EbV4N9Nl3QI/hHoIBA6BTtgf1EgFdAHNzLE4jR9Mf2C1kRuE79HEJWo0oEYmkOzOcoyvSX/cKgDt3U677uJddBEg1AlhPN2BAzfM7B85cwfnwlJQ4dBpxiknnPYfLZ5pxvvHq83CH/8vlJ2J4QCr3L6HP6/uMzYiY+n6FV5N3feOYpn6SyN/nMesy28f02t4KGZki11Xbv40hI+d39LVWqevQb6xSwL1G099TCmvrG1X0AIPMWZpCZEgbKLedOK358JnjHQHZf/OIX1X333SfHGtALlgo/02tX/TH7J7/z9eGZrTulVU62vbSzG/IA+Vn6A4H08o8bAGy1vBemlkOvVcVRIYFCAoUECgncCBLwPtndCC0p2hCQwPb2tgB+1Paj2S8T/fr9wA/8gPqlX/olMfkNFMjhRC8W0i66cqg6dxIEALkY1AvCJBWcOp5fIJAk9THPykvY0b6k1JHlaXXzHYfVo1dX1Gbd+ypv06BCxxUskqewKaImUt+GhOv3Ya7XWSfAhjZ944AHAjxTzY66rVRRy4cWVataUle3dtTFaxtgw99MgKUGokYyWm7vitcysurSNPHuRv8rQJV2Bh+VNUIepRCwSzbB0L5AE7MH+4jiaRz3MHgIFoUl+okqYaNd5Wa7guAwADzDTISjgFOTPgHHKNCAgRw60AqNSqxrBs+HnQhIiPYfOol5oqnYpSPOCSxwTxxCsO+ZiyBl3qKCSRPgHjWtWmizyIVgYhakBOOyQvBPAyPg1QR2WS9BnhrytLEnbVJRJbi3ZZY9l9hm+wNCokZEzAGJyqfMRICc4CxB3LAkQR18vviTeBhgztvdjdbskjqRjxrMLl+AvE8eeY+BqsznnAEfXBh3fR8HWchDIRX2/kmi1NvLHX0kICkEZJseR81Bedbf5Q5Nl+AgeOcl+QDSLZfxgPMMQbzqBvoI79g00eZLCO5VwkeAsDlMs7R7c2+d4QUA8e4IeIzDqbWyKmMNNcxE/3+0arntttvUxYsX1bPPPisagGtra4p/jzzyiKx1T548qcyAeGE86bWrXsu+4x+9XP3mf/68Wq1hwMclBgI50su32TER+v7CNgA4a2gANsccBESve7Uc+rkf7Eqh/TeY/IrShQQKCRQSGFQCBQA4qAQnrDyBP5r6/vqv/7q6dAmf6f20tLSkPvKRj6j3ve99+lLuv3qxoBdRuVcwQoJsC7+CpmnLKEyAw0SwsVZXj//dc9D0rKqzt+1TT29cVxuN3mJUl2tgYTkFQI3OvccZSIKbQQExamX1EHiqXr4CoGYHJqYdtTxTUUehTTm7f1Y9sLPuxCRl08t/MiYJ/jEgAOjaRHbBP+yfJNJvkjoytmGUxTzwJFmN5RaiT0OBgJvP9jSDWvh97QMASTUv6bi+GbELZQCEKJCE3NL0V5u3mtyL+S8AjqGBf+h/Dlz+BBLGQxwA04IrrQZAtxbAPvpaJK+ZfC0GKvZPwFMF3wyoTUiQgL7CxJeci1fcYj+WGPgBvMTJ2lXdxFxDX0sE6L4OiecwQ5F4ohE5CPZKAJiwPOgrguM6acBFn8f+JmwQfQFSC9HZ73yW8fWFFgY6sIbMfTQftxIhoBY0uBOnCAA9MQ1m5EcL8N+ytP94ywa8ea2b+pvQvTXQAUTAd94uQUA8Vykkkqpaj30GFcK7Hv/NXuuorZtcsKybbA2RhadK8Yj/lBkAxPD/x/HYxBeWmSFr/5H7hXnP7yB9/xHkO3HihAB/BAIJCHINx0B3/Nu/f78EuDty5EjXwsOWgAa+tAUI77/nNS9XH33wATtr3/nuFifTnhbfahMovgo3IV6xTYDhA1CncWsA6nWvXtNrvorfQgI3nAQ4YQ5rzh+WsPYav8OSQ0F3IAkUAOBA4pucwgT+Pvaxjwnwx4UPE81+f/qnf1r9yZ/8iXr66afFTGKYHOtFk15EDbOuYdPO0pZTIzQBDmt/fbupLvx/l7HAVercuXm1AvRjFcE+zESTXGptlKgSaG5DuCMZx4sF/DT31RBdsiomZLXVptp++rLa7Cyo9nz/FCVc45/MGyiAAUkLR2mD2JocGvyjaalsihNqvJh9M4nHbFfioBxGA8REGPshbsI7ECTN4DjuXMCpUax7+LITh9WD9avd874DqlhGdCS1pGav9JVSbTwbAvwBH888hmyyAC26mn8+0Tja5J5+M6lhx/FCDSH6DMsN7LN5RIUVOOVnfzBJ9GQwqfuDgKCLZz4DNJ3tagMmxxK8isbxrzU0pqkEn/V5JK0RJZrTisZlBK/0Xao1n4U1V6dF8Cv9nKRN/HhBLWe+J1wfMngN9ySoDOqbgbmpi5UGNFed5V088jlyEXHljblGjWW2VRlBVaQIeI6a16PuxVSZ6Da14Omnk+BqTk3t1ivdivdPeQt9IY3HswvXDFuHIFguCmJSdb2VCPwjGR0ApN2Ae48dfqXw0tx0Q63vTEMDMckg06Wy/c5bEYD5zqF7G/7dfvvt6vnnnxetwM3NTXXt2jX5oyUMg94RMJybw5cNI2ngS6//eOvH33WP+t+/9ICqO4Bko6jqbJcD/bmFCXO2tqC2HR9iWc7WAJxDFGCdbkQAkB8LilRIoJBAIYFCApMhgf7d9WTwlRsX/PLHqLf33nuvLAQY+fb8+fPqPe95j/rgBz/YtwBIU/HW1pb6zGc+o/7qr/5K3X///eqJJ55QGxsbitp2t956q/qu7/ou9VM/9VPq6NGjachmyvsTP/ET6g//8A+l7MzMjAB/P/dzPyd1f/azn5XrXAQNM+mvhXoRNcy6hk1bLwDTgJn7982p+fma2tzsLeSGzWcYfVh5qctPbAqet4hAHJvz0CRC9FydBGiA/7aJSli878I8uH54WtUP1TxAhRtMsKlNRriE5AZtEM7LcAqffJcZvmglMKYTNQprCNBAUIVaW8npawoBK/2vAABAAElEQVQpftl4Vj2IEFJUl0b7L4ysmAhjQ07N00jtG4PAYgU7rp5ShHHHO7TN+uwMs9D+c23mqVFHzbbcxOcA/2wgHVlEo0+0+hAlWzSB+PbNC+2wG2+fY7yY4B+fKZkDyIJWFNbjyi7rn0uwkFXouCCwyK6neBOSc7Iu01S8C85mYa33mGcpnbgMqxHwzwW2GVTEN6BxnnoIxfSzQRoPCnAjAOmhGonglT4LG4uc/wIluyfNxd57p3sx5CDp3BBSvHcZczM/0FCb1k7d8W7f0Ocj6G+CpgT8+WHFNUdpVlL/UtTo3xq0/3SiTKmNt3NQX3H/UlOaWtVOG26rCN7KqrromQAHzH9x/ezyqnryhf1dkNoqmuvp/Fw4KsfAd2fOnFGnT58W4I9agbSKaTQa6qmnnpK/Q4cOCRB4+PBh0QrU6z29liWz1UpZfeeZ0+reFy5E8r6LL39TOxA+IrfrdGBhWj2/Qk3A/tSnARgwAdaTcn+5UVzRa3hTDnnUSxCQazm9nsuDZkGjkEAhgUIChQTSS+CGBgD/7M/+TL33ve8NRAUjaEewjn8f//jHBRhkIIy06YEHHlBvfOMbBfCzy66srKi/+7u/k7/f/u3fFpNc+t4bZvqZn/kZ9alPfUoAx5//+Z8PgI46EjDbPsykFwt68TDMuoZNO0tbuKg5dfyAevixnun1sPmMo4/lqFKI5jcHzZ82/Ng0luAsew7+9Hpr1B4JZOZlKdO7Op4jyFJrlogGCoFA8gbmMvnx0q0AnTTJKSefgNYAJPhXhc8/anMRwBw6qEMhsB3QnBx2KmWJmupiigt/bDC15pIrS9+1em8T23cP9BjlNiyVd2ACJ8qDQRlRw0rM79INg7Bq8FChu0GLXSIKN/gl122AZBwPNJ8k4EizuKGPizAuwV8F4EzF269LLgmK4IumC7rwPEYubGuNcwloEcxMAhaEsTWS63hOGLl6ENlHzQF5toHjWeaPKKIYb2ZAm9Tmv1G0HfeEPr9ncb4JASbLANsYEdwfTgEqTYz/OKA+UIAPj4tQIFP8CftcnkfHHOlyCWBSHFV/89npgoARU53JW9SxtBc06buxCtDbTNMw6905wJvhwj2DYGIXr4WguCYxHHcOIwBIxaujsW6grCB/HgDgo//lJqvEcE4X5oy6Q6rguuzAgQPyZ2oF0mrmypUr8kfFAGoE6rWr/gCsSX74B9+q/vO//ndwmRAuP+ZlIJDO0Z7FxdJcFQCgphL8vba5EHisZgsT4KCAirNCAoUECgkUEhiaBG5YAPArX/mKBLzgS57Of3/xF39RvfWtb1U8/8QnPiHmso899ph6xzveIWAgI+WmSevr613wj0Dg93zP96jXvva16uDBg+ry5csCxtEkl/l++Id/WLQCv/u7vztNFanyvv71rxdzB/o5sZN2flxoANqSCT/XC0D9RTg8Z/AOIwFPEgCouZvCBq4Gn2y1zQ7MMEtqZz/thKIXs7rs2H+x8RQABYyIU3puRpGyfEUW338RmyAhbP4T3EeZdwT4Ke0gGisUa8Xf3wjlSdCmC5AGuMrxBCAbtf8GTgT/CJSlJNTaQqEQBY/DtTl1pUFkx53mECkXYUkCN3cBVAhYx4jMeSSwx3Ep/voI9mEvKj71WG2aMZYHL2E0MH6r2NNTe08nmh+aY0cD2ewgraGh87p+KT3606NmGDXWJNqwK+MEXKsCrBy4L8LmAF7PayhhJaYj6UaJTTS0/AzCVob6BSiKqsS8x3EMoCdSC5B5MKZo5m8D/PVlPgwJE+a0PB4bfrTgcy7BSeyq+e6IA9tEsHbBIZ1DdATjKd/uc5ihKmHZF3X1Onz4WTRIm1qBjWW89x3p/JED6vmHrvSVc2SVS1MRAUBumtlU9z2xOJK4QVEagC7eCfSdO3dOnT17VoA/agW+9NJLql6viwWPLsO1sjkX7luaU69ZPqz+YdPhU0IX4u91dIJh8DM365Y3s7agMbi5M6cWZ72XLE2ndWqNOQiIBkL1h3DNVx6/WdZtedRb0Cgk4JKArAlHOee7mEh5jTwXqZDAoBK4YQHAn/3ZnxWwj86B//Iv/1Ldc889XVl9x3d8h3rZy16mqClHEPA3f/M31a/+6q927yc5IEBEM+Jf+ZVfUXfeeWdfkbe//e2KgN/3fd/3yVdFaug9/vjjmUCLPuIhF1zgH7NqDcBhA4BZQbOQ5oz1sl746IVQUmbGEQk4kjdseEotf2fla3BUAKyUYRLcdi1OuXOY5JeLBgMBKsnnc5/XRItKyEJenPbuKEqAMbKoQLuyBa3KMO2YKNKD3BMAcBACCcoSbIjTlokkQ+CPm23IMI3INc36deyKQ8zWTh5cVlcuugHAyhY1MlEj/he/Q1DIoJYSLwBKhllgFm4A9FGTD1pv1Nrhpn2o/vq0EAb5RZOr60GNMZKT4DuGCHoagLwYM+ANfjg2aqBPoKUNANTCW42cAx5SE9RwX5CU2hQ0c0Vbzmhr0rLdfJxnHCmdpBwEjEtJ/P5JdoiBQFE3gYlMgBnfAwCEkqYkWoCc/2QM4NnTkB8jbctYS1hRdxwmzO/Mhv5idGv5YOS/78x8BMLjhkPcfZNeLseocJDgIDJCiTP5jNPnnyvNrLoBwDLWsp2VqHBL/dSmoAHI1K6XVHvH2Eag8zvNkiqnQpn76Se9Mp9AA9BFi+sFmv3yb2dnRz333HPiIohAIBPX6rymA4vQb+CH3/Nm9YP/9o8j3/WVxjTCgPSAvFotfDTxzk5jXw8ANDQA2xjHHfyVMj3g0oTM//CdqT9863VwZmJFwUIChQQKCRQSmEgJ6LXaRDKXlam///u/V3/7t38rxX/8x388AP5pmh/+8IfVHXfcIae/8zu/o5pNc2Wtc4X/vuENb1Cf/OQnneCfLvWud71Lff/3f7+cPvnkk4paieNIGgAsTICTSz8rmDnOSMCB1hH4g/NpibhK599cSHJBWce1Rgcgxh5/9NEeBjOheRm1mbhopXagAD4BQfROSgSB0iyoQTN8+Q7gAyBQixoVjo1mr9bhHOWyWY5iDbKkE/3MibIDyCBfVzMS2bxmhDq1abhxGclF7T/BsdDf1PiUSLboSW/Ee8+BTS7qnNvpOhTEG1Curh9EAJ0FjDlu7MbQ71F8Bu6B6Sqi95rmorzPADWm9h+vxfpEY6aQxOejim4iEMjgFLknjCMG8KituUGN0PpQjqbKqZ73EGJRc0BIkcSXOYyT+P0jQQLyJi9ZMZbU5ThnYtzE9S/n47YBrtf3oVyq+Tax2EIz6ujIYuJu5aLP1iTzJk1oJUAHx3PKYWdVmepUgoPww0KKUpKXE5s/MEoAy01tX5NUGZqRJ2bhm8BK33LiCHzjYbJIkcoHPJDL9P/H4gReOzujW1skMQGOaxZ9ZtMN0Jve9KZAVloLUUHgc5/7nPrqV7+qDi1W1bkyVJ4jUmsz2PYp30zaVeQgA5B0DndvmSbAvDguLUDzo/ewAMBEH2y7kikOCgkUEigkUEggbwkE31Z5Ux8TvU9/+tPdmn/sx36se2weEOD5kR/5Ebm0uroqL3nzfl7HNDvWiSDgOJIGAIetAagXC+YCYhztzaPO7ABgvwl2HvwkphEC/E1BC7C0A9Mg3O/QT10YeOFvJBLXNwkZCQYSCES7uAHRYGCANR+QClyLO4nYidWhQblziLvivSiwuIbD6g/gXyYzA8iZmplZTH5trq6v7oRqQKxCa6Mvoe7jq9Ni8krgm9pS5bYG/rzc7C2ymDQRMNuBOysWMX3oJS0/lnwALQjIlS0tL7ahDxjBRbOfsw5nMTEk4EhrthTyjZMPTXg7AJWqCLAjGr9xBfz7VfAxkK9QXU+ObdEkzd9Efv9YgP3UUyzyRJx16slQjqBxHAAobDIIEvISN2vNpqiIBQZNmHcIftHEve/9hoc+oD0ZUxc/FpEWTd0JKo4KEKScJRp4UtFJW3uNCdP+0zlO0CmpkY7uW1CPPXjRuBJ/uIsJo7zgof02AEgb8DbNYEeU0poAJ2Xr7rvvFjNhav5xPXHx4kVFxYK3nYte37XwvqnU0Yl+akfYdd8M10S1qaM6q5ozgoDw4rgiAZvrd72m7zJZHBQSKCRQSKCQwA0hgdG9qUcors9//vNSG4Gv17zmNaE1v/nNb+7e+8IXvtA9zvNAmxSQ5rhepqPyAZgVNMtT3nnR0m0xF0NJaB88MK9mZ7ELGnUi6ALNvoDGH3nAddEEbHa0kkCs9t+Q97zDlQyATQ0G0ieVgIFYwMvmNQz0TMnRzr6SatK3VVa0JGV9o85ODRjT11ji+ilngE40DU26f42kjYG4fzq4YWX+Cj7ePLNmaKxgs77w5K468CA09J7cAe9T0HwLAn9mPSbgZV63jxnEY5sKGrDR5D4uDYBg0xrZuQ/+ufadovlnvfFt7b9Bnn32OYMvEHxMY2IaKhuMpwqDGfCZBt+zlxOiRBy/6RT6Q1nIE8y0K2nDcjKJ3z+WIwAV6DoIO+v0I+axNjNx56yMUx60yCIT8rUB/DWgJdsHwkUUpGYex88gSWssu8yO+ewmps+2GkON8wWfpy4giDFeojam1hCMEUnqNkHOBAEFyIwoLJqcZqPwvFThkiIqPf7QJXXTLO31vXQATlZbdBGSIu0iyEWp7DXaDADCMF3HF9fVCy8sp6A2WNb5WahM5pTMtd4ctPNuu+029Za3vEXdddddSrvXOXu4ppZg5h6Vqld7T2p910DtrUJHAAAuVk90r9Yqbbzbel9tmp3ecTfTCA5MOYxrzzKCZhZVFBLwJMDHeS/+Ff1XSGBACfTeVAMSmqTiDz/8sLBDtX76AAxLjAimky6jz/P6/eu//usuKW1y3L0wooNRawDSf0iUKeaImj1QNXrho32hJCVG04aR+gEk8AdwjxoLUwS4uHlhIhjD6zD5tc2e2ggCEprMDUVopj1yQ4OB1EjBNNCBdkKacWkDRdwmbR0uq9Y85KflvEdEkYZNBv5IPQw4DrFfSV0uhrHlGnbCVjq5tKToI6kMrbDlhwH8PaTUzDWAT+Cbm/Q4HuznwSIva8Ed7GHhngnPEfTIYEbM/o4rZ9MZ+TkGaA24qLGH7LLA9W2f9h+u9bUpTnhdiuEHjLI8vQo+HEqa4aX677AtXX+N4GuKQGydLYlOeZn+spYcxOFkNrHfP5ZGkwk4mSm1Ga9ZOGOj6PMyiRYgnxUJBoI5IXFKkdVJE+8/jntq7PYBj/7c5CwXctGe+81sFB8/cnQBQWoIEhAk6EzMZtC2sDJUQiBT2sNzK0kVfI0bfVnZhAxisLwOZHFLxQt4d9fJo+qpJ2OCWlj18nT3qIeut6Dp1q4b62vwc25pTV34xgFHqeFcWpjvfz9krclc6+n1Hz8E33zzzYpB9r79279dnT59Wr3peIwWICJi67TZsR5cfQO/RxcX1OG508YVfOQw/AA2xxQIZFgAoLn2KkyAA91enBQSKCRQSGDkEohAAkbOSy4V0qHvlSveoubEid7XNRdxftXT4BijgeWd6Dfk3nvvFbKvfOUruz4H864njp5u46h8AJIfcxERx98k3tcagOaiMCmfI/EDyE2NBv4IRpnabTR/pK8/fNk39gdd9k0/Td2LN/IB5EOtQAKfrTn4cIOCptYOjGw2QCadqI2xdbSMTZlLojrXCH/JRo894r3yNygHBLtSaboRaOZ4w8ZzGJJZKFnatBz3zzXU/q/tqqWnAPohArMAf9TGSZj6QC+jHIGZ7UOYv2DizRZRFiWYdXXbZsjcKDb+QwAPNYJuIQBANzqxxamtAdhrqJUx5SnlVWPfZNUGRD9XdiBsPLdM9C/HOS5OC7CMMlHgjRBL888Q+pskk/r9I6sElszousJSd0CmaYyXN5MGoDDCPiA/CYSCvppeS5CPdJktYVZmdyVqnjJ1rOmC15J8FGA+M0XNEWY+HrMrBBDEHFSBwpeYDAP3Ea3DQQBBECYI2EabTPHIMYFOawzUNlhZfHrq65cAPs2rFx5PD/6ReumwBwCa2n+8Tr6WpupqY71fa5v3h5HmZ/MDAM01qwYATZ5pScOP+L/8wfeo2XBcT3W2e6DoWgtfpULSUdA7uXQ+cNc0Ay5MgAOiKU4KCRQSKCRQSCBHCdxwAOD16/AY7idt+qrPXb8aHNvYoMfw/BJNfz/wgQ90gbBf+7Vfy494SkpaDgUAmFxwegFoLgqTlj55IvoLcVI6znwEXMKAP95j4A/8hW2CuUhv12Iee2tj4eRjr14kGAhfge1ZgIEwVaMZHk2XnMm/zGAfBP8UQYhJSQR9AZLoJL4P0a0EAjMnFPZ8pyWkQDAOe86wsZaQSmS2aT9ixRQ0vxZp5vt1pdae3BQfhTVq/KUA/nRFYZt7AsPbN0GGPshLbSe2rZsfMo81gdSVjPKX4B81/0L6npddwAhZdAGApqbGoM0g+DJN3iI2zK462J42fZX6SfyW4ni3WlK1a2EopxcBNlft3BCZar6y/Cb2+0fiqL/PHB9i4eOfOaFs1j4WX4Ae/hNbPZ8daqXFJZlDBmgPtUI59l3af0kDf8TxmOY+m0JAkJqwZR8QFFAQcpPnLV4kgeqoeSlRgvVVvr4tebGdjH6eJO3UW+p2aAFubIabp0bRKe/3BkB9LWh+S//C7S0ik6NLC3NBHgap2fzYqz8Au+jRqugdt93iuiXX2lxU+GmjVVczVbdM6APw+OJpBPvorccCAOCYNQApg2Fo6g2DppZ38VtIILUEOG3uxb/UDS0KFBIISqD35gle37Nn1ADUiQ5849L0tPcFkRG/8kwf+tCH1P333y8k3//+96t3vvOdeZJPRWtYIKfNhAbNeN1cTNn59sK5XgBmacdQTIAJ7jGQB7QvZAFlavxRoFgsMsgHfQBae4OAuGUjHQdkRREIUNvjJ5AhNfpacwADMQ204dfI3BhzA9tA0MRJDfbRBabYDZzJsc/gZlFAwGR7wUAHip+xJEokHItD1Pozmapf2lH7YOa7/3GAXPhGQ/NkgpQuM1ezXNQx5Wb2M/Oyn+vQ/OuCvMgjACABTuN5qPReL1FVjO5eDPhHRgT8C3nT92kMmo3NqRV8jth3VX6bC8HuAlUBSKjC919npid4PqeM8s3E/ocNuByb/1AbNNBZ5s2Mx+Q9NEXdCymUxu8fSRAwsvtoIPNfIQq59kQbwmnIZcyZMkSSaAEiYwXgXCxonmRMhLDDjyAM1MGu6AO5MU+l0mY26piSSdS4MMAhRc1x1AcIAn8Tk+oE40gHB6Hmuqvv6PsvaZeePXVQ7V7Ott7dRVTbyrwHANoagMLAdshEM4D8oorOzw1HA1Cv/8Lq/h/e/WYEhXJ3XGeqokrUfPbT4rRbJjQBLgNk29hZ0llhAtwDZcftA9Bcz3cZLA4KCRQSKCRQSOCGkID7zbSHmzYz0zM/aDR6L9OwJukgHbOGY+SwvEmvf+QjH1Ef//jHJfvrXvc69Xu/93tJiw4lnwYAh60BaC6asmjODaXxGYnqtmQCAE/k6AOHYIsG/rjCtoE/bnJg7ltGAJAkG4B2yGI0o5humGI0MexANmIijO8G9Be4g0AfjWWgakMARfIQnAYAA9sQDIJdaLLRZDnVHhaZBViJYwwbbtHYCVQaVyjdffItPtLQD889eU20aCo0JyXwNwhY4LPB50TLjuS28bg2l3FV9zPql6ifzG/VlxVQIKm8E/tBzH4j+oK3+oARgxHRSDLOh3lIbShq9pnRbF31TV8D+Oeb/pr3NShDc/65l4IdI6brkEfuKUK2aesix2L6q8dZAgK25qSwk2SiT0A7axaak1OzM1FCW6c3wLUP3vaVGVC+1I6kODwT96BgxAS3r8KEFwbkK6oWcimAIMar+BHER4VEkYY5p2Nu5/veTnHRf3X+chnvuJc2VHs7aQfqkt5vB/7/psBHawf+/xo9TTdq0YuW4lawD4Kl/3/23gRKkqM8F42utdeZ7p7unn2mexZJow1JaBshoQWQDJKQBBJCIAtxhbER2IDB2NK1jbiG44UL9+Bz/MxqDvjaiPcw8ODAM2AMumxCCANCIM1oNu2arWfp6emlln7f91dFVVRW7pVZ3T2T/0x2bhF/RPwRmRXx5b9EfxaHBiA/sOr5n1ON6Xvw4uGVTrdV+kB9aZVz+FrFICCk2UJ9vtht+AAszrMGYBwAoNb+03tHASY3EgkkEkgkkEggVgnUf6ViLaZ9zPv6Kg6OWaIfs97JSaoNKKXNZOWkhT+f+MQn1L333iscGGTkm9/8Zs3PYAtsW8qq2xY3AGhOGBY7AKjbEqYdQ8t6VWe+PjkO23lewJ/ch9YfTW/8kl//f/45+i15kaTD5J8L9dm+FPzA4fUYYLHe7hY2aCdZ112mNqCPijGCZgM/ax6gcnFr/RH4K6HeNJFkdFTWJ0PNsYiAP7NJ5E2TwekR7A1NM6bRpr9UK9JAoc7rsJbTt9u2Zx2z9PnnUSIX5Y5fBtA+UZ2y8IhzyBPAzUETkP1qBVelGtToAiDCMdBEDWMci3RGCCZh4NDfYOTPKgdkREROBU5NrB9w3PgTIEI/NxBkEEX/tKRFyDawL2B26otQYSd/gHy+QrcHY4UAGmvR5JsV91oCt302zVf7fSTi+4jvFgEEAWqm8T6WjxDsf/4gVcciP1QVelMY+3UQMDVTRj5/FT5j44ja9+S4mjoaUpW5GgBk5qhF8w517OkqqInx+gd4H81uOUkcGoBe4J+u9HtvvUJ+E/V5w36i/mbusgm6xrt90P4jpeaGZc8/pgbgfPsA1HPgWuWSg0QCiQQSCSQSOGEk0DpKscBEQQ3AZcuWqYMHD6pnnnnGtXaHDh1SGgBcu3ata1o/N7/whS+ou+++W5IyWth3vvMdNTREu7L5Ja0BSECUpm9xfX3TX06pNRcGOJtfKTWWrieBYTQAU1ggrYUW4BM79zUy9XlGYE+ABy606vPIem4sblKF5ui+9QTORwIcHMGqAusFruFqIAfPOTa4jsA2szQjvvKcOZ1gd9h2rKmoSTTbD6F7mUkvhOazr9wIHUyNEYlM6bbQ5kLabT2oZeNWVth7aAO1FanBQ/NlEseoLIArp7H8nQXANLsMrK2ADOVUBV1kPMhDYlTBS+ZG0rgOK8+w/avBLJMgj2hGmReNY2mfca4P2URrs/W9KPbkTbNNamhRI07GaJVx5zhK5zvP2i+4RPBD3k9Mi/tdB8tqck1azMLDo0hk5kxRySGQ379qdZp8/+E6+zSSOpFJC2OZJqnU6DTcnTkLEXfYbxloAhZ7LbUnuBWSGPiD3Bgkw0oE0iwlWZMs6HPWXQDMKs6XP1gQkK+Aj1IEAAv4QJVF0A9q9PvV/hsc6FG7f/aUtPvIQSDmqy0gng+JpIYqmoOzRxrzpmfn1Prew+rpPQM+uESTJJtJqXwuuiWMnuv5Bb7WrRxUp+WXqseKhr1vtWmVQCCVH5KhoaVqx76DDY3uy6TVj37wAzU8PAwz4PoawfQBWMA8ej5Iz939ymE+6piUmUggKgnIuzYqZm3is5h/29okoqQYHxLgVPuEo9NPP13atGPHDlUsVldzNq18/PHHa1cZ3asV+trXvqbuuOMO8X23cuVK9d3vfld5RSFupbwgeTUAyAmONnkOkj9I2laAsyDlxJ22lXZwzA0AQAtM0KhIiSkvXu82C2BqAXQA+KMJkFUzyW9Zucmy6jyM7UgZWhnYjpbhnwsbrmfhRDwDrRpG36RJ8UlBlCkdqDN6KPyKzQ7ilbgYwD+zczxmA1ysy+aw4KeGnS0LAf6qwKhZXgTHYC3AXwEKIyUEYyH4R0AoA+0wLuzj+mGiCCahcDE7ZPOM4WbN9BfpbJ8xqiv51XyKQE5WFowIm2VQDesNm3MBRmw7tpLYtn285ZLHppjQl0QbEGvnNLAI0XLC80dQaRaRuu1IBwLR9xgQJH8A7g8qmIS+HN3e4XkJWkBQv3/CH69fq7m5VMdeNEGr1HIfS9AhPsR+nwU8NxmARA3+ANmgsDJGuQTImN06LqII/FEDmoNLNvIcmcmS6j5QxO91SfU+V1AD22fUsl9Pqfzhkuo8VAIQ6O+3egX85c1OVx6WY9AAzKfxwxCQMjoAiEUDMIWPSHnMGQozIeY9Aeugk0ep/UeeGvjScz9djtv+XTdchkHYPIiLonpdyZmC30QrDWYr/sn379+vJg1zYRMAPBFNgLUc4lJC0PyTfSKBRAKJBBIJuEvAzzrCncMCvHvppZdKrajd9/Of/9yxhg888EDt3kte8pLacdADgn2ve93rBGyk9iE1/zZu3BiUTWzpNQDIArTGY1yF6a+GejIVVzlx8w3TDgJ/e/bsUT/+8Y9VPoOVrF/CYoaRe1OcSNoBf+TDoAsA/lJc+Pjla0lH7RG/pl8MNnJCE2QtwB8BTziqJ1gimn+h7dEWuLTwpqemFXvVXK9QBgS9mojyoUYkMoQdb008cYHlExApdmNP01vUKwVggMAfTT/j/EGipuHEOpjQUcPTpp+pWVdb+Iug7FpQqaf9nXivCvgHwMyPjORZ91iLi4aRXZWj7HA7/sY1FpVFv+fQrq6DFDrIxv8fL5uBQHhOysyAgz/8o5IhyN9qdYJksaZl1YL6/SMP7duugR+aajNsG5L4PfH7O+DIjxVBPwUCX5EnPwGhAiwSgnDCtientf8qOEq9mnhvWYHT+s0ARxyYC4Cokd7zLNRlLUTwnB+t+DHPj1/UUzYMqx3/9XQDl2Xdwcx153IwNe4uquJURpULjeAhPzApmL2yvu2ini5r57dWclANQJZ2wVnr1Ypysxzn4Cgxe7Typi6lq2qcRvU2r1qlzjvvPNEA7ClTFb1C3WYQkFJzPp0uzr2euwcBQv3Uxxp4y0+eJE0igUQCiQQSCcQjAT9riXhKjpHrjTfeWOP+2c9+tnZsHvDH/vOf/7xc6u/vV1deeaV52/cxwZ4bbrhBNOuWLl2qvvWtb6kzzjjDd/52JNQ+AFlWAgD6k7ie/OhJoVsuTpieeuop9ZOf/ETt2rVLgODhISAcXkRQrwb84VG0Ww0RiGEabDVwwouv5T6n5GWYy5RzaYA5/lY2JywASHkC9BJNR0aohHBmewAKwaTKVv4WWS6o02pXBlnMS5RgLNYgBqEm7T/ewAJdwL+IGissUdci1ms0hdT+ulKQfzuAPzaDmoZHRxnoxWH8E4wwlMWl/Q5JbQHTiGTlxIZlEiTz+4Nd4trYof66DEcNQHyEaPdije8bRkktOvVPtdI6EIhuAwOCZKG9HAeFfd/quvARC+z3j5mR0S5QSpDnXNfBce/0ockxQ/MN0SommIf3qW9CudofoOP482DG9wb7hqWy/00i+Nd4xbwb4DhAkwJwDZYUsu17ZhofBpuzFbrgB5CRknxQLptWE7vHm+TSn7exnXbhV16FACAocuZoM/AmvoUnGPUZz6IGeF14RXGLgTiiJD3X03M/v7zf/NLz7JPur1yeVc0qyisRAXhkZES9+MUvVq+48Npa/i4jCMgvH/mVfFD2E8ywxiCCAw0AZjIeX5AiKCthkUggkcDClsCTTz6p3vOe9yjGNKAy0eDgoGJw0w9/+MOq1bgCzP/lL39Zve1tbxOeAwMDKpvNihu3rVu3qvvuu0+98MILngK64oor8NuE1a2PzZPZSZTA73piUYnkwgsvVJddBtV80Gc+8xkBZqwN+MhHPqIee+wxufzOd75TBp2Z5vvf/35tMN15553mrdrxL3/5S3XttdcKqMYH4xvf+Ib8oNcSLJADUwOw1QfWq0lac05PprzSL9T7uh1cCDu1hdeffvppGV80Ny8U4KcHZjWjo6Pq6pdtdW4aeNLUl2YzCDeHFYv9RF4H+UgBKLRP4VyEvjOHBVc5n4b2F8vRV733JxwACJlTO8EE/ggbMNJvqdu5D7wlNY8pOG4wNgITmitAIJE5A/QiKkjgixolAYaKY/FkL0E9AL5VzHzBlUMemisC/NEPnGPu6G5M9ePDxxqU7WTazTpZgANH7ThUKxINowDNI/iXhYak3z6RCNCNCjq2pbm10TZDjBfpQ4xjwxqQpalIGyHQBJSBEOaFUGcnCuP3j7zY39bnQoqxabtT2V7XIwET+f4JqgWIivEjVIaagC6yc6w/fjOzeG8wKz8oNBDuRTamI5R1Qx39nuDl2f3CLEzi7YVkFyXbifVpo8Nq/AV8PbBQV4ePl4SRp2N5BciyBgCZg9yp0a2OVnx0Zqbb8yxGrQGoga+gAODNLz9H9c1an1jKoyLfyVKzg90V1QjAFO+qgdVqarYymE0T4MmpaUU3RVyLPPLII4o+y9vxYUbLQc+BWceoKDH9jUqSCZ/IJMBX7GLcIhOAM6Ovf/3r6uyzz1Yf/ehH1bZt2wTw43vo4YcfVu973/vUueeeq7j2DUN8py1fvly99rWvVR//+MeF5+HDh0WBZnx8XD344IPqAx/4gDr11FPVF7/4xTBFJHk8JHDCfuL52Mc+pmjWOzU1pa6++mqJzEstP57ff//96pOf/KSI5pRTThF020NOTbd37typrrnmGsUBS/rgBz+oqAH46KOPNqXVF/jFj1u7yQQA49YA1JMnPYlod1ujKk+3g/wI9FnPn3/+efk6q30q8j59Pq5bt07lcjnkgU8cOKiemTURlmrt+GPjpoGBvKkCA4HYT/6rXFx3XOCVCfqljYlpAHaincHy3erpWoMFcpOgFrqAURJNjR4uUWYWo78/i1iln8IsVpFnDsDJ9DJEUD0CQA6gA3mFYWWpksylGHyiTAUTDW5zTBPYsAE3rPmjOmcfH1/VgcUpWuXSMIIGDdpIzOiWPsBz1GpbUgA7soiY61KdpiJcI/8aqRvabFxv+yFBbDTQD6jREAhEVxRjLA0AsJwjnyCS0gwc9h79zJKckoTy+8dqgCH7vIlQWJRNCzSgmipTvyCmn9QApLWiE8BeT147yuB9LBqEeAf5pVXQ+Jp86riaxu+aAJgW7b/FHvjDlEMO/v7yExRqMxVzAFCJxfkQ3fKRPrXjwd3NTHAlHURzE+lrAUAs/v8IUs6l8aH0cAXwYjRjfuAs54y5h20NWrsYtQ9A/aE3KPBFUOvGs05V/7ytolCgW1WaoTwK6kgR9uqqUVtxBTQATTo+3Y8IwPtUt6EBuGx4RGXL0B/Eh+XnnntOtr6+PsWAhatgQhyXhp6eu5tzXrOuURwnQGAUUkx4JBKITwK/+MUv1K233iqYCa0I77nnHrGU1BjKpz71KbV9+3ZRgiIgyHdTEDp69KhiYFISsZrrrrtOnX/++aL9R9+o1AxkGUz3xje+US1ZskS98pWvdC2C+Z2sPl0znqQ3T1gAkMg0UePbb79dBtC9997b1MUE/6i1F3TgktEPEMFr3759NZ7vfve7a8dOB+9///tFpdXpflzXqVJLUIqmBO3SANSTiLjaFDdfc/KjJ4bc7927V+3evVtNT1e+6jLd6tWrFaM+U8aaGAl4zep+tXP3AX2psicgRYDBjniP2n4E/+zu+7jGBekczH3FNMq6YuRNn8TyqQXoaDLpk8+8JXMA/lgfglMz0Pxb9OAm2uI4lvwKHov2WQRuLCMATA7BJUKPO4wtLugr0XwNLgT+AGi0e3FeRB9T648gpysR9LRYaAkg6JoJN/ksebD2YuF1P4VXTBYBMoIU41f7j/V3GzvWV4dXXVu5T3CejaTGnBdR24+BQqxEsCEDU+BibwWEsN4Pc25+MAiSn6/3MH7/WAbHop3JJwGvIOPAq74M4hEJcaBA5BIR2Ef/1cpEviz8r86yuzw+MvX3dKpTSl1q5wN71PTmPnn0xMS9xgyyAZjlNp6NpL4OI9GQ9FVSc6I0NOi69toM8mpSRv7NFP2NhqXo6MNF+wnH3LTNx8nm6tSupPsLqnC82f8fLRSGMsfVoWd76mlhpl1m8AuPvq1lCHHQi6AmUZKeswYFAFmHt732UnX/fY+pQn0KqEr4EtMxN60mitMql+5Ss6V6P5gagMxfKjES8D6AgPV+X45ggldsHhMTOFqaUNlgYmJC/fa3vxVtHIKABAO5MI6SWpGDn3ok4J8fKSVpEgnMrwRoGUmwjx8avv3tbyua5Gq66qqr1ObNm0ULkCAgLSpprhuEuHZm7ATiIjpwq5mfilsE/G666SYJ0PSHf/iH6oknnsCHUOffPio7nXnmmSab5NhFAlFNA12KmL9b119/vajOE5wj2Nfd3a3o748o8d/+7d8qItybNm2avwq2sWStBagR97iK1pMnPYmIq5y4+ep2sBwG96AfgoceekjMxgn+8SVE4O/iiy+WF6EJ/um6rVs9qA9rey5SbF9fApTA118r4B8m23VzX5QCEAyzUAEUJXKww0KgVjnLgSzMLdeaTlGErMiabszTBbSZwKWY+kIzwbqIp9nYDINAxLgwaWvLKX/bARWgFhjLxZ6UmhrBPphbKCmkBJkXaeaLoB41n1wcz1jgU3uN2iqtVjFAa9QslCuOrfcB/nF9alM3TyAB8kpjgRsnpaA0kgsI/rE+VmDEqY6O76FqhnhbZ9SK2n+chcjmPUrsAoHUuHFiSH5RUQhWzBLK7x/rjMwM/mElqYa3aKzZXM+jBLhqvgDxzAcivIOzk8550rh/wciw6ntwXD35/T3q+DrYmaKPpe6mtqG88wOV7J04Ynl7F1hJwQ+APc/C759DhjIA05xP339bNi9XTz76nAMn/A5O1sEmx0TVG+Wussp0ldSsRftPbkNWqWP4Ywwqii8zhckO5yAxUU+3gbZFUIb+0Gt+/PXLtjOXVZevXtOQnIFAuibxJQo00NcIVloBwKxaLuky6bLKiXon3iNlRDiHSxk9z6SWDC1MeI3za4KC9EFO39PPPPNMLYqxMGrhj567m3PgFtglWRMJJBJYZBLgWpdKTqS77rqrAfzTTaFfwC1btsgpLS6pqRyELrnkElHSsgP/NB/GV3jNa14jp7S6JGaTUHQSqPw6RcdvwXGiZhbt17kFoSuuuMLV3wb9AnJbLEQAkLb7cWsA6smTnkwtFvlY66nbweu/+tWv5EsIjwn8rVixQo2OjqquLneVh7VroFplEifD1vkwrtHXn2gwmGkDHHPeLeZz+KIiZsPkx8UY/nMiron+AIOQpx9AtoVbMLZBquA/LReBUGhgna2gn2ZSwPqxuFj9/elGWPcRyp7g3TQUETJYlOfhMsqTNfq+PIexhmeCGn4lCJ7AEp30p0p+w81YGxT+nEPx+DAWTktRc9TJi5pMf5mB5sDeWeHHEEkBesZBAv4dD86Z4AC1sfyQp680yoACjZnkIwPQjlmaafukSiCQ5vQ0D2ZAkMISn0JwKy8kcBHW7x+rwj6xjeiKpvoZk27NaboHmUf26sbvCt3JEbyEol4gkncHtI9Llv4/bWSZ6vj1uHrqxzuE32wPQI/OSr+WGrEUee83j4ZA1WhOTIHzNzTgb2YzowBXMOYY8TftopjXt6xHHZ2wQYktxXR1ZdX+3+61XG08nRjHS2bICWpsTDu3urK4s/r/Yypx9VCNeGvm4lhmhPe4rAgWig9A3eb3vv5K9Z//6/OVuVj14tx+jCN8kOrvyam9h2kKjN9XzNOGMR83qTe7qnZKM+BZfIkrGhqDvElLJS6WqcxAFzQMPEeNwCNHjshGf4EEC6kVaAb+qzH2eZAAgD4FlSQ7MSSA17zTmmHBNjDmudlXv/rVWtPf/OY3147NA66R77jjDjENpnby9773PXG3ZqaJ4piu2/7t3/5NWBEEZPT0hKKRwAkPAEYjpsXPRU8I4gYA9VdDPYlYjJKjo2U6IdVENWgSHZaOjY2JJqm+57ZfbwEAm0wLMcETjb8WXuY0+ePiR4AvccTkUqOAq0hXAJB15g9nZedSaMy3fAB/rMEMJuHlrgiAgZibE5S9oXQRNKt9eoyRYm+HKuXnVOf4nErbu6ESH5fse4H5uEjGWKDWJYmLerFjRF5ekXO5E98fPgfHVkMDFlqIvoiauDYfLD2BsSpzAp422X0V7ZgIwsoehcx9mveZfChnv9p/zOet5YhEle5k8nhIa/+xHFOjy6s0ly6WgCAwoSx3+gM23IpyKcY2W2i/f1Vutr7/cI/PeNC62FbQvMhnFOB9VMgiwWd5nkKAZmmMA7g6U3PwbTeypEetO5ZWT31tV63NHB7HV3fJu4bPeQMoh/LM6N1mE1s+jnv8WypIs98steacCF0GLyS+aPOqQbVtzy7XtIfHj8Gv3xLlh2XH8llR5rOLAEwfq6ln7Eeo+AeEKTDB+agprijAeg4btL4jy/rUWb2D6lfTh2pZSxUX4aqnCl7zxnBPN4ZwozyWda1T+OYmxEAgh4/3qqJDZ9MkjyAffU4T/KMmIAFBWqowYic3RupkGs5ZzY/Z1SJcd3ruHlYOdsw5p+bWjrmAXfnJtUQCiQT8S+CHP/yhJKbiECOVO9Hll19eu/WjH/0oFgBQ+9lnQVG+k2oVP4kPEgDwJOn8xATYu6M18Ecff3Q8qolm44xEpGWor3vt164ZrCfhQqV6xnUFfTDloSXF9VJYkqzUvvDrzFtXwGeBjgCgNKCyKJU6BOTrs3j3ZD6BPzLhAjpdgOP06ZJoKxQJEsFP4glBMTWDvvOmEK8oi2id+YqfXhEXnxGOWeiaAkQC0AKtwVr3V8ciTQI7cCx+xpCeZpncEWyopY1Q+EVoBBH8CwIi2Zn+CqAt9fSunF+g0JtTNQXKzeKVEwb8Iwf6XnS0G6wWYe4868/+Q7/FuWCTCKcYP03RXM2K2hzbBgLR6TDG0hKAAAJFG0ITx0EAIogS1u+fFAOw3E7zS6rRQjNcmxAlXz77UKMIowXI90IWWsNnjQypF/7zSfX0bKnhPTE9BFNP+rVFY6wgd5y+RQmS813WDsoeLqg8An+40SlbVqjHrD6FbTKsWdWvtjsE/jCTl/F8L+vuVPuPN0epNdPxODVYVEX4/5srNgqkg88aPhh1HLYfTLxKU+ACXEywn6Okni6LKmiLzDXwFRQwM4t9z2svV3f881dq757yNF/MBZXL19u+srfZWf6q3o3qaOU7c80PYMGiAWiWw2O+mzk35XbaaaepZ599VsBABvrjB2xudE2jtQLpAskPaTnEtdiO8zfFT/uSNIkEEgm4S+Cxxx6TBHSR5hZsiO8dTTqPPo9q/8ADD9RYaZPj2gXLAbWgL7roIvGRSlddQ0NDAmAy0vBtt92mGA8hoboEEgCwLosT+kiDV3FrAOrJ02IzAaZ59K5du+SLqnUgjI6OBgb/yIMR+LLZtCogErDWuCEY1YNohm+54xL1zK4D6v/9Sgs+DQRkIQLkvnCwtsfvOdXiCS7W/LoxI1dhvO6XSdTpCPxB9aqi8eiPecU8moBUBYCi36kUfOhQa6XYhesxRyv0V8twqSJz5m9XPBYYhSXQBuwECHgQ2qro9xKAwQwt0NAPotFqCbLBsSKAtNb4AI85/StD5DBirUAET4TZMkYjyvFL1BjSz6OZR9rjn42ZtbVjiIUBWBg9Mwwhe8UML0Bmu/YHyN5y0soYYcUBKAfU1nMKBKIrVQsI0tcIVuj7fvZBTIIIihf7MHBaABztfP9JPYMNbT9Nq6URzUIOnoiI71O6YQhlOovn97fb96olAP9Moru7meEK0GPV/uMYinMccwxEKB6zWQ3HKXyY6tk3i99U95fPwaPeQB1fg9lj0Nbz+WWxHyCaHwAw0z+rpsab7bsZfbtAvwPQFnciypE+U+kjNkqKywdgK8DXmZtXqXUd3eopVUHzinNZ/G7CpyMDolRpeW+j+S8vr1+6Uf12EpqU+JHVkYCdNAA1H3PPhe3o6KgEo+NclubBDFjHwH/8oM2Ni2H6EORez9NNHvo4TgAwAf+0lJP9gpEAH83647lgquVaEaO+1P71ImoL+yUCZwcOHJDkXvkGBgZkbcyPDtREjprofouBWklnnXVWzeegUzl853HTxI8i3L72ta9J3IcvfelLnjx03pNhr5dmJ0NbT+o2agCQD2qcpCdPehIRZ1lR8KYJBYE/Tpo0LVu2TI2Njalf/vKXYlYRFsxMw0cCv8bvAdBHysMvz9v+26Xqqiu3yPkDHY/LPuwfMasB/sffAq+ptfxecHUQkAi0lQDqCJEJtuBcAhZqlzwE8Ec2UmUrDgBwqlwFqGi2mjpaAQNL8A+IwH1o4Ly00K7Vntfm2lBXBl6YWp4S89TOI1h0cwzAt1OxeR0j9W0CAXUrBLDGCfpStMuoYQNRh1kUUOPq+EoGLyEDXYCPPevuYLsbCEyg3PFsKAsA6qMGjUnAohXwj8zEB1dATVBPDUAyDiJXpg9AKWoty3MYIFM1KcdjCma+1DJ1IgH7rR8vnBLbXUf1/FAOjv6nMnh/oE6hCYPZaUzK8xGasUdGVtlnOz04VW7znQqtshTMecsh/GOWutPq+Ehede+r+7g7vgagE541VlPezboi1d8DfRrPnqW20K9+KgV59cLvXwc72oU2nbpCbXuqMo9wSaa2bBxRO3+02y1Jw72elPXHseG2nJT7EIwiX7YNAMLfAkbfzsKdILAuR6KvzzmAYPIxzjFVsBtxRQF2A8f81PD3X3Gh+u//8YAkZSCQ3umcKvfUgW1rABAmzGfy6th0j1rSfUzRBJgUBACUDPjD31Ka/3Kj6ZzWCqQbGy7quXV2dor5MBf3PLaSnrvrubz1fnKeSCCRwMKRwIUXXuhZGX6k9Ev0K6pJuw7T53Z7YgvEFaIOMMr311ve8pZacKMPfehDdsXLNb6zX/ayl6lXvepV6kUvepHiGp7t+K//+i/1iU98QoJ3Mno6/QkywAk/hCSE3+5ECCeHBPSDnACAlf6miS+BP9PXH79mbNiwQS1dulQScQJEvyp6QhRmpCwb6FHjfcfUbTefr6571dkNLMY2IGJBSOLrXABAAhFcO/h/vwcqkQt1mbpKgbEvh5rrFhL404zEhIsyciIBISr3M9NYvMLktYxVDcHAEs12XEAGJ5ZtvR4Q+AldN8ih0M8o09BQeK6k5qiUgx9dJxIzYCxubc1y2R/45ZEhS00VpOOxXyCwiLyTawAKhADfCE7YjgZWQCrh1KLm6xko5ISJmlzjhLbnjgDMIpoZklhlAQCD5EcmP2CnrZyClOOQVrT/OHTQfgFwHdK5XXYKBFLLg+eawERhqTfAUcsT4mBkeZ/aMQv1zRaIY9LuSWLf2g/WFgozs8bQwfLch9UCRN1mhnPQYCsAUCrDNDwFf6SVKaoEuDHexdQ0jKH6pnR8PSMNGUKcdD/vHvRDs5ylv0YP6u3Nq+d+5Rz11y67n4jCc6sK4sLBLgDIHDTWOvcDjEfgsTlYO7jRQDanDhWmAQK6p3PjYd5biBqArN8rLz1DffibP1SHcxXQr+NgSs0O1b86reiDQ2IbmplF0DgAgF05vBBABVgptEL5fF7ms2NjYwL8UUNn3759iho+O3bsUHSoPzIyIr4CuWDWv796vhsXAKjLaaVtSd5EAokE4pEA3w+a6ELAi/ieIWlf+V7p/d5/xzveoR5++GFJ/qY3vUldf/31jlm//OUviysEa4LLLrtM3X333er3fu/31Oc+9znRDnzXu96lmD6hBAA8acaA1gCM2wRYTxr0JGKhCZhfBWgOoVWcWT8CfgT+CACapL8Eh9UAJK8/+v0r1eCAve+V1QgSksulYaYRfKJXA/9Qhmi7EGxxo5CrJfEDSNYA4rxMlNyKD3yvReBPl6c1/fS5254yLWEj0T9Z9hgWNfhX6sJCVPwGhhSiW6Et3vNQHGmRe3P2EkymJ0bTAK4gn7qiTnNCgHxzaYyZGYxtmlg7gbBY1Nd8BTJ6NZLzY6W5SKiAPZUiZuA+aXI1FpAGGNBcuP0VJ9Nfpg5j/pvGOo1YRygi+AcH8TSrboUE/As4LAn++cki2mct1s+ubRVtINSA6kMh+lF4+mgAtfLS0BQsBTQxJn9WzYtonrpzBo4bw7aBBVAETs8RReSjnV71dLofx7uDzzKrHFYLkA2eXAdNqO0T6vhaQ/vP1C7DsxNb4A8nYcVwPX8Qfv8AUnvRmrWDavdTBz0Hw/rBXrVjZz14mRdfue9j7tExUlCFySy0tpthapr19jxThOk3Ppx5FDiwtFu94syN6v4ftmb5oIvp6a4sPPV5q3s9z9Pzvlb4ve7FZ6hP/voRYTFzoKyOb64/5HY+ACXhHD8IP92SBqBdnflbOjw8LBsX9wQCn3nmGdEQ1CZz9A/IoCGrVq1CoJlKT+q5vB3P5FoigUQCC0MC1GhbuXJlZJUxtYLpQsCLdJCOri78XkdEf/3Xf60+/elPC7cLLrhA/cM//IMrZ/pBdSK6RyCvBx98UHwDfuUrXxHNaPpGPdkp0QA8SUaABgDj1gDUkyc9iVgo4mW7CfzxC6imJUuWiKkvzSVMwEHfj6ItTuAfy0inU2r96JB6Ar6PgpIAgDoTF6BeAKCvJb9mWN/TyXcHeTstcqNeoArwB008fDBvlbUscJvXLPXGuRwJGAiwi5RC+zvHMSlG3bjgKQIQZMTK+SbBKOajGgBJqaxQwofC3GH0lYMgCB5nucA9CkfxkFc5DxNrau1VQdaGbFikUKNPzMiqC3zx82gAMROrUmpmEPnDoCLg42RmyXr40YhrqC/zhEX/MJ7yUBrzMvuzlmc95/gOrP3Henut1K0FRXhO7T/RysWzNEvz7ZDE59MTpMM46aCZdg6b0/vLqXxj3Nkl4e3ZJXglOA1+u0w215y0/5hUAFibPJFdCvMceRXO55jAP79p4TkOLHdkowuBoxt6xDcrR0iT9l8Evw1ezeB9z/Hlh4lDmvTxkuo6wMWV9zPQvRRmmgcANLvQ2Lpl6omfPeWDWyOTwqT3Ai+9rGBr/ks/g8W+lOo8iKAt7GuP/u7uzqm3vu0V6ocP7VTPzNY14hpr5P+sF/yiJP3RWs/7WuF91w1b1ed+/oiaQRULU2l1aBY20qoCWNqZALOsfKqyiA/jA9BvXbm437x5s9q4caPav3+/gIH8GE7FgG3btqnt27fXWEUhhxqz5CCRwEKWgMfv/UKuOsE/L199Qerf11cPUuTHrFdjCtrKMEhZdmlpsnvvvffKLQYZ+eY3vxnKB7/Jm4FM7rrrLvW+971PLjOwyBve8AYzyUl53OL09aSU2aJstH44TzYNQLb3N7/5jfrpT39aA/8oi7PPPluiA5mmD9aO1V9A9cTQej+K8zBmwPJbZSxoffmB815n2DYnA5CDDrxjJwJ/ABszxxFJMqIFnmj/RbDIFTCQJsE9aSywOlT+cFl1PY/IfodK4otMFj6xC8imAPZpyH614RbsEuRKbcCpEfsorgR6aIJJygDwyiISZO5oUXVCZrnxokpPYtFIcAb93kQY2wQMi4gcSc1LRok9tCmtZpbh5ypkfzqa/rLwkIBYGICAYHoU4B+rLYbTIfrfl/8/FhDD7IDaf9KH7HY7IJjl+iABkn2kI6isx6GP5PUkNsOyfhOLeih1hzFBN3m4af9J8SH6toG/x0kcGoAsUgBe7PnMhSI0fg6a8XzXUg4myM33Sism80Hq4zEEgrBqSMs29DwHv38+Xt6Dgz1q2676R8sGRtWTFN6XxX3HfHBrzn3scDX0bPMtuUIZpJcWlJ35L6P7kvKHKnuaAbtRL4KfkT7yN7epTCG41YOVd9Q+APVHaz3vs5YX5DwLM+erx0YlSxEh2o+XZhHIuvJA2wUBYcKl2dWSPioTYGHm8Ifg3vLly9X555+vXvrSl6qxsTGJkGn6CqMD/ieffFIVCq2DtQ7VSC4nEkgksMAkwI8EXBeTqCnsRvSdrwFAahC3Sl/4whfEZJd81q9fr77zne9I0KJW+TL/6aefXmND36gJxTLFT8S6ECXQLg1APXmKEzTzMRW1uQAAQABJREFUI1/6I2BYcgJ/OioQZXDmmWcqqhQzEpqd1p/JW38B1RND815Ux2Mbg/sBFO0/EwjBQt1rseJ13609BG5sgRq3TH7vxQD8sWi2Vy9E/VbFTzoNBhb7oDyNPsgdhU+854sqf6BYARoA8LSL4lrAB6o/QBxq5U1DG6q2/IMmSOYYAD6O0eo45dKHC3cuElPYsgi+kj8CMBDaI9kjMCGDqaadFmuhu0MdBvhHsDE0Ya3ppvUm5r9hmVPrxSdx4U+z31Y1/6Q4iCszBXDrGM78V0Gy+gYA0XfmgtBnMx2TEfDVEcWL9K/ZAhEg9hvpVLQFCTz6JTtQ2shbwqNfisDahRqpjibg9UfHKDniwxgAXqkhxw2s9OWZC/B81FqHrtKjA9gJhFQ9429FG7EIXYdavSI6IPiX9ol/rVg74Pl96YyNy9W+JwOa/lbbcvgAXiAu432uH+/mDAKATDRr26UKZZUfx0ew6g8R3+tu1FMFAIeXL1Vvu2Wra7lufHivM58RCwqvdEHu6zmrnsMGyWuX9t23XoGPmRjMANuWznaqQfhozOJ4COa2djTSMyaXWwkCYsfX6xrNf0899VRxkE+NG038eM459Pe//3316KOPKgbMC0vm74jXvDtsGUm+RAKJBKKRgAbL6CuUfvCd6PHH6+4ctmzZ4pTM13VG6r3jjjvEBQG1Gr/73e9GqtmYvHeau4HTq4ROAgm0GwAkaMYf/XY/dPRxwq+Wzz33XG3xygnO6OiofPEMUh89EdQTwziGydiGocBs5zKWlRsWXJ7kI4kTjxQWK9mJkiosifB1wcUc/OzF5cxdTPP0wtGpYS1eJ5jBCMlYAgHYqpi75sYBclB7DRqDRYBWGvBosSj77C30qT3DkFcx/kow55xGgJA8TIJpLg2JVJhhx2WhHrFyFRfoXZFRXOU6FvVpLCZJ9KvG4CuU4XEAizNDSOFnfEtumz9cf3loIoXR5JOSUC9qx/oBg2R80OxXy8WmqkEuiT9DyCqDtvEZKsCv/JzPx9MNDA1Sh6BpaUovWn8Ahcoh/PJZy/MMBKIzYJxlGFQCz2OtrwkyYat8Kagk5Njke6PMIefQTxyfNP1taUyyOIqi7haMVxoodvNflBbnBwQBAPG7QcCuHMRVm4EhsXsI9GqK67dC82/aswIRU+e+WdGG9sO2qyurnnjSPfIvXYzsefgpP+xs08zOFFVfPqcmHExyGQDEyf9fBx6gJRN4WKokGoAEEx3e1xoAZPKbbrlIffeB36rfHuIXjOAUtf8/1kB/6NUffoPXqjFH/5Ju9eKBEfXQsf0qdSitlvbmFP85zUHXLtmk9uI3omYCXDIehkbWsZyx3fwwrolmhS+88IIAANQE4ka3OdT04QKdZnUJJRJIJHDiSeDSSy9VP/jBD0S77+c//7m66KKLbBtJU1pNL3nJS/Rh4D3Bvte97nXyrqH2ITX/6KYgSmIUYE30dZpQfV2WyOIEl0C7TIDNyZOeULVDtHRESv8ldPRJ9V6Cj1Rl5lcJhklfsWKF48TLqX66LXG2YxQ+AGnC45dk0WZJ31EFT9x5+C/Djg8DYhDEMKnxzLzjcozFf2qmauoL4KK1WjmXI1qSzrcjv0Ogr9idVoWBDACsFLSzyqIZSO3A3GFAhDSj5uIoQmrV/1iEVRFWlMH0MpgFA7SrtZSLQUsn8zRNzSDIw9QMIBNqRKURjXkSpsUtg3/gRy0rS/Espk7UDqyfBT5yDOBgcKLmWxYLuxooatwLdYh1YQ3IAgNqVubgIixNa76a4J05+9YABItWZGPWQMy9qya/ZYyTKCgIH/qfzABwq20ATulqII13ED9wsEYErfh+deonilbAP8v7N0xb2AdOpqzShdGIyL1qdezGPV2Yu3ju+X6SsRZQC1A33TT9pRpcaJ+bYerPPLoiYfNb8mUm4AJhHIPOJ41tXq6mAdC50fKuvJqd9s/TjtcS6wdFI1HHSFHNHLFHcEsAZy8aXVtLLe8kl77WJsA6w9/97W2qM6QpcG9Xs0ai5ht2rz/06nlfWD5mvvfecgWQxTk1jUAgvV1p5eT/j3lW9q5Sswhx35WvfLEqVoNxmPziPtYyYDnUArryyivFaoaB8khHjx4VlzrUCuSCmkH1gpAT+BmER5I2kUCUEuB7azFuUcrAyuvGG2+sXfrsZz9bOzYPuC7+/Oc/L5cYhIPvijD04x//WN1www0SlIjvmW9961vqjDPOCMPKMQ+1GP/pn/6pdp9uDxLiXDehk0IC7dYApFDNyURcQmaUIqop/+QnP5EvlHwpMSw5TRouvvhi+VIZdkKn88UJAOY7s2rlKucIRla5NQBbAFA6EMWPpjeeaxXPBNaSGs+ZPQdTzSbyyxeTYAH+YPrJRbffbE3l+bjABXQc5r8+ipYkGgycBRjIgCGMKNy1r6h6noWpMExe0/Sd5LJQ8ltOrEL0XQlLQiz8ZwbT6shGtF2v0Rw6mxph3AQErIKjzHPw7CyCjNCG0CGjpUjHU4Is2NyoVW04RgJ2IwJfBOecQCW3vE737DShKKksAECW5QrwcbKL4eebAnaBPHt81mHSTa07vaX1xwPcK0ZgPsv6B/XBx2ASrJ8mgn0lRJctYcyJpi7Gm9sCNRK/f9XCXYFj1KvVoa/b6LaPUwOQ5ep3sG+zXWNcsp9MP4/kEXAoujW97fcYTKvnBfj989mx/Cj47D73wB+bx4bVzl8803JbUi4+3tKDs7YBQFgofbS+8RUXqJ7++gPtZgZsagAyf3dPp7rnbVdT9Y6ngShqDUD+Bul5nrb8CFQhh8Sb1g+rjZleNYWuzMEfqZP/P2bn2JiEL42u7CzG+pwqtFkDkHXQ5n6sC+e/lAU1Abdu3Sobj3mN6Z566in1ox/9SNzs0OLGa76vx77es7yEEgkkElh4EqDSzGWXXSYV+8xnPiPra2stP/KRj4iLAF5/5zvfKT5EzTT8SMBnndudd95p3qod//KXv1TXXnutaBoSo/jGN74hvvlrCXwcfO9731OHD8O3jgPRh+lb3vKWWl2vv/560WJ2SH5SXU50uE+S7j7RAEA+1JyA0CxBTzxyuZwaHR1VVO/V4F0r3at5aP6t8HLLuwF+AJ995pBbktq9OUQOFsKENQXwz89i3lz01hiFOMhAi62IhX0pgPmefFnD4scOtAhRBV9ZZOGJH52FQAIGwiG4gtsfalCmof3YCW0AyoUmiQQIuQ8TECHuBXwr8qPp3tGxDCJEQhNyL8eq/SKPvZQGCEizy6n+lDq6CT9JEWhYEenxMv1lGnl+Whgqbs8fTdyzUJCwMyfNQKO2nEWjUTb7UfqSj3b18XaUPQFNlweagGcO2obFHppS23BBNwRpLuvFseqHtDYnTbtpjtyB9gsB9KMGHilK03wCevjvn1gvqN3NYV8x80VWvCf8yCMqv39SWfQRP4I4kcjc6WaU1ymPObyL4npXgj9VemtagDz3IJ2CoKwmvjedtCV1mjj28kxGwRi/1b3w+5ei/bhP2nzaSvXb3c7BP3LZtDq2Z9zX2PUqMmf/asZrBr5c+4pqdofRGVVmBDQ74J/1RZtWq6uve5H6yv9+UO6IGbDdewd3e2yi9l525enqkv/4tfrxHue22tU/6gjA+t3FsvS8z67cMNfe8aqXqHd//d9VqVyCll89wqYdr2JhEOWPq87crCqEAEbteAa55gaCUjuHGz+uE/B7+umnFaOEMhAAt2w2q1avXi2La73eCFJ2kjaRQCKBhSOBj33sY4pmvfSnf/XVV0tkXmr58fz+++9Xn/zkJ6Wyp5xyinrPe94TuOI7d+5U11xzTQ28++AHPyjvF/obdaKRkRHFzaTPfe5z6tWvfrVsV1xxhbyf6KqA7yaaL7Oe2vyXedmuhCoSSADAk2Qk6B/kdkUBplj1ZCJKEfPLIyce3PTXSk48GDGIk48ov95qXnG0w5QJIwH/4IHt5iXb4wpIgEUENWwI/pkLc1yT1YDTYs7pum1JzhepBTiVh9qMB78U7l9x3gY1AFD2a//xG2eGMdwJYhoYQ/GOLGtgIMCZGhiIqLjqIMEagoEV34F+NZsiW6A61rjFG1jwTw+n1Wxfh+p/AlEQXSzVjq7LqKkVgHM8xpXfGnma/oIRwbuIimuqloB/1MazKwALu+xxpWaW8pmtAFDyLBPcM4Ah6V887pXnnhXG4tSH9ixhhuwk0kI7sYCxZoKKgYEUMjPfMzi1krl41u3V5rSSt2r6S5PvAnxFRkbgO4dFdQc0VfwSwT/WSdfTT77I/P5VC3P1/cc0EYrIs30xlyUgLZ8zjNu5ZhypXj0DhOJwE41M3uWHLpf3Rp3Bwj3qegHvPrqACEAT0+6qxadBs2zbj3YG4OictL8LDkSPNWsbzg2VVGk6g2es+flK40PgUK5LnqPb3/QS9dX7H1JztESgpi/6jO81K1k1APX99993s7r5jn9QEz4AYp0nag1A8yOvnvfpslrdX37+JjX81bwqTZTVipWQtQshNi/u7lCMBDyfJsBuMtDz7XXr1sninXNx+grkR/k9e/bIRj9e9BXIBXfUgKqL+JJbiQQSCUQkgXPPPVd98YtfVLfffruY/t97771NnAn+UWuvr8/9w0ZTRlygj8F9++offt797nfbJWu49v73v1/dd999Ddd4QrDvX//1X2Vrulm9cNZZZwlwOTY25pTkpLueAIAnSZebPgAJaMX1o2zyNSdVrYqZvKjtxwAfGvijE2JOQmiWEIdDYt2W2AFAn5GARfuPZpME/ywCTR2fVWU4DQ+jSWZh5XpKDaM13T3qmSkgGDaUxiT+qhdvUu98/UtUDr6FvvLtR2xSxXepBpLGV0QknDUYSE0tmommoVGRPwrtwMPQsBSzxAoYKNFvbRZTUonmdVkkdYuaCQM+jJ+RFxPonv1EuerEteXBM3Kq2BthY1CEl+mv1KCxKvVKBTkigIf+M0Fb+nvMwre9E8gkWoFYI9sGSDAebDnkWhobIs1gYV2pGNfXNUIiI0vtMg/o4y4FbUAGCNH+1FzNgxtyV0/AnMXZlWEH/NVYQC6M4MpxLZqOvEFGGgysJQx/QFP63ARAxSVg7PSMWNnj/URXBH4/ErDKUfn9k6oQDHMDtNifdsK2tiOic6e+jYg9BiBAWgxgAtemVl8Df1YCxGbzsOY6AMd+AG8ki4ekQqhRCx2SO1zAez3Yi2Z0bEjtfN7ZpGlkuE/t+OnuyNo8h+fBllbNOvr/o+uGU5cMiN8mgkVnnbdePfLQbunDDtyrAbgG415EwbWjLLQZP3TPjeqdH/oK8vn7HbDTJrTj7feaOVfV8z6/ef2ku/1iaEm+8KirD0Dy6U6vEnYMBFLAnLfdpOXgBgDqOvH3bWBgQDZGD6bvbYKBVDI4ePCgbHTHw/k5P87zOKFEAgtOAvzR4baYqE31pbnsI488IlpzBPq4Bqel3aZNm9Qtt9yi3vGOdygG2ZxP+tM//VN1zjnniJkyNf3279+vxsfH5X2zfPlydf7556ubb75Z3XTTTZEqCM1nm6MqOwEAo5LkAuejNQBZTf5Aa0Aw6mpz8sSJAReHejLRShnkwYkFgT9+YSRxckLgj18Y4wD+dH31JCiKdmiedns/kYD1+94O/OuYLWKhBNM2btpE2CwowgXl+eeuU39yzyvVXf/j/1EvGBH8Muj3l1+wSf3RrS9RGSzyKTMCtYP97f1x8LuwN8UT2XEDMmPhqjvQcpmn1JYSU+BOLnhpJsyIwvAdN4kFAHiyTQVEFZ5dgo6sLpCEXYT9alOtaC8BCJhcm1Uz8O3Xv2NWTPpmAX6On563XSyGLhyC8aUxhHQCrEUgQwbfKAK0JaUQwITad26GpRmkYYoUgMMyND99EZPZJQWr2rAjW01IK1lwjYAjzYGLeBTdTJZ11oY9wQ8WgP+iMQmsYI4LfLYX49KtndT+6jAAv2KnXQMaSvN9Qk2jLpjSs5HZYwABoWXql2iOLCBg1SzZLV+Ufv9YDrUy3SAOfsDw3xK3mlfvQTOcshJgxa5gXnPAf3xw95VEtACpuQqTcEb3biKOrepF+YCjg8RI3ZtSt++CjH0UpysXsGSOsW5E/XV7RuxYpvkhz4X6IaQj+K2PiqYnppXCb4+VOoYLauZo3b+feZ8m/heNrRErD34gvfOtl6o/BgBIYlAyOyDPTWvvzLPXqesu2Ki+/osKD7Msu+Pe7mjBJPMjr5732ZUb9trt116g/r+Pb1Mr+tw1AAc71yr8nKju/MyC1QC0kwFBgbGxMTU6OirAH4FAavcwOB9N/bgNDw8rahU5fRiz45tcSySQSGB+JUDruo9+9KOyBakJzXHNj8TWvPQLyK1VYqBPbu9617taZXXS5U8AwJOky9sFAFKcnEAR/GkFOOOEjH5G9uzZoxjoQ/Pl10SCfzRDiJv0l2BzchhHmUuXdqtly3owcQJy4EK2DrZpDjxVAUY74DQaaykbCrmCsXC68Pz16r//+XVy9e6bt6q/+ux3ZTJ39YWb1dtv2QrgD/7s0O9FLDg5yeM2iLa1kzLHLKbRHoU3SMZeeHUOLvcb+NRztH4EGVKbLQ/zofxRAEY4L8OUTkyGexbf65uafgfOyqsstGKmR4AQARiMksT016WfdFnUhIMoIyEGAgG+geAu8JlF8M+FMbXWNCBA7aaWiW2wawdkYAKDaazxfQGjZoXAIDNZlqjMHbCDpfksQcQyfVaCv26HmaXpWAOAeE9pP4BNaYJeQL06EUyHEaNpnpshsIR6MiiBX6qYArMRdsKrcInU7x9Zoji34B+4bd+XvB6SMkATJJgGmBO4lQJ0kyEuKTMkb9/ZWA7KtNUCNHAs1sXUEuR41VX1XVbECQl6h4q2Dln3PjWFjwzBWjCyfInavmuf47jcsmm52vWjXZG28sg4XlqrmgG11BL4/9vXfJ2FlwDmv/yczbUF3po1A2po9VJ14Nkj8jHS6NZaXZ1MgHWCd73nWvXTN/1fap+P4BdRawCaczw979P1imLP34RXbN7sqQG4omeD2o1x3wUNwIVqAuwmD7ZzaGhItunp6ZpWII8p4zhk61af5F4igUQCiQQSCdhLYPGtIO3bkVz1kICp8Tc56Q40ebDyvK1/5M1JlWemagLmoT+R3bt3y9dDXiY/DfzxS2O7qJV2BK3jKPwAugGATssImv6KJhMKJABoRwHXIHYs1NaLx9Sf/dmravcuedF6dfdrL1avvOQ0RbNfrfHHBBoA4X750JJantgPCFi4gCqy2HUSZOyVC1eA1JkLaMhYQAvs2QRqYCxGIihFc9np5VQPi7gzCOph80OBNeFcmLJMP+AfWdA0WJPUgaCMBsn0jSj2FK1FvHJK+Wv+tYPKBbM7qMWTRaAS+tdjfxWyAPCwJ8kItPCucDD+oqNNM+woNXNzMJXPzlTK0v1IrUoG+CghqI4fookiIxULmGmTIWq/fyzCS/uP/WX2gU21gl3C2KI5JjU1STXAVo8BjNsy8R0A2BzD/iQnrIL9QaPm0hUzYKu5PBnpoSS/U9VngVqLgf1VBquVv9SWZ8RfJgS8ePq4SnMQBaTB5X3q+aP27jW6OrNq/+N7A3L0Tn7syLTKr+1WM4bJKT8qlvBbU3FA2siDWh1d6Ywa6q/8tvO3n/O2G1/3YvXp//Wf0BSG0Lgxv0G9PfZgok7C+cL//NCt6s3v+d9wg4HfBxfq6XLn5ZLV9pb5sToODUAW+vpLz1Ndne7z19H+TWr3fmoAIvoyoqi3m7QcopBBZ2en2rhxo9qwYYPau3dvWz7at1teSXmLXwJcP+k11GJpzWKr72KR68lWz9jmfCebIBd6e00NwLgBQD150JMJP7LhpPL5559XDz74oHr88ccF/OOEkMDf1q1bxedAO8E/1jlMO/y01S4NIwEHJW36W8vHxR4RFis1zsOtd93Pwe/SSzY2gH/MwL657tItKA+aN9D6M1W9eY+m2eyvkaGl7vwjvEtH84ud2HvUOCFgUoK5HP3nlXMAYbiINxZUUQIq7ZIZhyZ90Yn5KMZIpATevjWGuK6yeUzC1oeaTV6af8IbZnv0naWJEgislaczh91LoSwYG9fYesM5aya1Qz0JwNB3XxH3ZxG4g2OQzzU3XwRGtZTo+KK9JaEvVmYi+lfMHzFkWJUny8oer0TZNtO7HdP8usPG/xm5R+r3j5UAUzftP0lSExjPWqcMNFJloVAXV40pi5LiAPCWoc1FX6QF9FERuIpNzIdavrAHfKexGg3j3cA3eK8WtRrjpSFd2EIjyNfBl1ZAyu+dUrmZ4J2Z70yrx3c5A3ybVg2oCQ8rgYBVrSVf1l23ARbwv/O4Kkzag1UZaDGvzffKh1l+JKU1Brdrfucsla2aL0s04Br3ykFnZ8YzMNyatUPqzuvOs+RsPvUCE5tzuF8xP1b7fse5s2y66wX+MUNffomanO5S9AFYRICjdpOes+u5bxTlU540/6W/wIQSCSQSSCSQSGBhSIDLgIROAgmYjjoXEgBI4IhfB3/605+qxx57TNFUgBOGVatWCfDHKEPz5Ty4nRqAjAQciPCFXZv+6nyy5OCX9yYKvhgRFuiby196ivqT9/1OA0dOlumP0Q3444KA8svhS/6S3vriooFRlCdoNkELT/KRxJNHhAlYHT+An7XIxaYByHbSdFkAJ2tjIjgn+Ov3qyi1xvziWJ5VwzOSg2m2aL14JM4jIIe1YPp8XBDEVwRnAwRqAPaVutMIHgIAsBfaW9A2nYNmloA4SCdj1q3SkImp/VeygNduWd3u8fnu3lesA4uWxGwC/QHSt6IvwiDgvxwicdPUWd4fqHvUfv9YF5qm02TZieRWyNe0LU8Ao/p50HvbdNWLlIME7OCHB5h4z8LUm4AgAxIZOJ0bC/d7kDV9nUoTjf7RTebY0h845jXwh6UVKQSxCUKZiYLqQiCnMDQ40glNevucq1f2qyd+usf+ZgRXl+aqLlUw/rufPa6KG+bg/89ey44yOWfFioZSNRB46VWnynWruxK6B0nhAeC8gZsJuDUwwskb73ip2tjr/sWgp8senLTy8nuugS+2Iy4A0G9dpmf7KwCgg0WHXz5h0mk5RAkAWusx3/K11ic5TySQSCCRwMkogQQAPEl6nRMbDQIyCEicpCcPbpM8An+M1vPQQw+p3/zmNxKYhHVauXKluvjiixWjitGEYD6JMiO5tSOq+gUFAFNTddNfsw52X94rqy4zlY9j9M/LrjxN/fF7rq4lphwqPv6aNf7Y59T408BfLRMOhgagXhIzcfHutsBm8XqxGXNVXNlzoU8NGy8NP1cm5LGITIBp2ifgX1y/NgT0Amh/arNRLxn7uZ+GKWoG4A73roRnJ20AHzptJH4ANbMo9jJAwYh7PjDUOuUG8EaAwKoGJ8EcAa1syiTgxKwkpilF8RrH+6hrH/3+ac7Cvl6QcZqDv0xfHwOQp8ygFGhLFtpyjMDdAXCj5I49VEsKsIMQPMcHqhEZKI2qsT3CkB3ALSDJIh39TvmIdiAAwSL6seTS715F6DHD6NREFXVPsno133/4gBXkWfYqs+X7AbA8as51Pz+DdumW+S89g+BOR6DB6kSpI1NQtne+75TP7/WujsrLuXPftMpMwaR39ZyanbAH2di6q87YZMv6zW+5XMadzEPwzGrq7s6JlYCeQ9CvsxsQ+OG/uU1lCw5oKJjG5QNQz111vedjXyoNqS4EASngN6PdFDcAmIB/7e7RpLxEAokEEgnYSyCuJZl9acnVeZWANgOOWwNQA2d6MmE2msDfgQMH1MMPP6x+/etfK10Xhusm8MdoPl1dUa/AzBr4P9aTQbt2+OfiL+XyFUsA0NpPuK0cxPQXiw076oafoJYJfXT1K05Xf/SulwsrPWkn+GfKgpM5DfwR/NP9bi1/WdwAILUhudaorzesVZi3c1bJCvhRw8pq0hu0govJBJhRQEOsif2JBAKmbzXfS277x8ZfWdZUGHfU/iMxEIgbMRKvHcIjfs4Ms2A3HrHeYzP0RmFyZmAVKu9jTS7abNzjtInw7jD9MBL00ZpdTWkDXMiPQ0vPDuS1qQQByDz8BIr/Ox9lzCLCtjQNL5HjK1DhKJE4lC/+9TzGnRXX9FFtxyT0s6ffhQLGsnEtkABaosFHlwTwB6nNhfFzFcjNnfDQVatXSkyOq+4NfJvxt9CeIFn9aE9qfj1PT6m0qDLqK/73m09bqSYm7V8iq4fzat/ucf/MQqTM4Hmm9mJuvFKHIn6j7Pz/kXUavkAvOG2dbSlLlnapjVtWVLRPDcCScxvOFzj/0xvnE4wSy43H5ofWfswZ/vhNl2OA1ceJWWDUUYD1vMZpDmOWHfdxrmM5NABnVfEE1QCMW34J/0QCgSTAV8xi3AI1MkmcSKBZAgkA2CyTE/ZKuwBAO+CMk77x8XH185//XD3yyCNqYoIrYiW+QS688EJ1xhln1DQUF0oH6MmgOTGNq26cHI9uGPJmT7CrGvXXTMzfry3nrVN//uGbzcuVdSB4+yb00+9cc6Z6+zuukgm51vjTE2TW0y/wp8tc1g/1kbgIDReNEdR7IRBrEQfgZ23bYtIAtEeKrC0Kdx7E9JclEIwJ8DS4VipHc9PqsCMI6UYMUuFE8+rvjNXSGytI4VgFBPBK5EwzVg/5mdp/ZFfT7OJJSErDGT5lbUt8t9k8+6wHg4U4AQgNvGDeXASwZQahaLjf4omn7z/yt8q8hTJlrOl3vtm3LfA0s9bMhQEEFbsBCNJcmNqBPsyFRQsQ2staS47VE+1g7EWL2wMoNevRjmOMCl/FdMFsNlsI34kHHAJ/9Pbk1MSuylzJV0VCJppBIJDu547LMCwh4E8RGv1ONNjZJXMAp/t3vvWlIjXTvUEv3ICYFgIaBOSe8ytqA1q1Aq+59lx13qpB22J6uu3Nk20T+7io53h67uojS2xJ+rKrEQRkcUYBjk0oCeNEAokEEgkkEohUAovIkCzSdp+UzDQA2C4TYA0aHTp0SKL6Hj58uCb3oaEhNTY2pvr6+mrXFtqBngzqSaoGBOOq54aNI+q3jz7nyt7J9PfG371Y3f4Hl2MSXYRWHnx4hfl6jMn4ddeere56y6W1L/Jsuwb99N7OzNet0ssGet1ut3SP2i76K0b45Vf4Ksj6mhWABovpxyo8R++cUuYieXMLNhNXx1SBKW+J1VOI+W8E9eG4y0zV+abttNOqt9OINqsBj3qO+hFNg6lZ1XaSgVQtlcWbVeA9ypcbj/0Q3xUACDVRO0w0XfWFEHuCQl0HS66Ag6ANZt2r5VC7kubAs0vwgGpAzK4O1DJiO5EmhaAg5bx+o9glDngN8nAbG8INdXerXpASO2YbO0sA2RgfQhE7Kw+8iJq+czmMAVRBtB7pl9MCGM8xbQZpqo0S7WBq/6GO8wqEOwkZAXG8KHt4RuUn2GjdKq8cjfc3bl6utj9zsPFi9Wx0aIl6Ytch23tRXnx2x3551smzcCp8QB6zB9ho5jw62O9a9Flnr1VLl/WowzBbVvkKkNgDIJPEORQ3Am564xxDk7Yy4LPIQGJ/9Ve3qFve/HF1HCbSJkVtAqznqnHP8cw2OB0Pda1XR+ZOHACQ/avnkU5tTq4nEkgkkEggkUB7JdD4q9respPS2iyB3t4KEKPNbuMqXk+ipqam1C9+8QvZNPg3ODiozj//fHX22WcvaPCPstHt4LH+QszjuGhszF0D0M70N42J8fv+9jUC/rFeuVxGrVtvfDX3uybBJO3V179I/be7XiLgn54QcyJOoiwI/DEgiykXuenxZ9lATBqAMJ2svcDqawiP2rR+m0Cf6cMvCpPeILUSs8qQi80g5YRKSyAFC39qxKWnsdE3nt8xGKRA9Hcg01/yJhgRRV3wrOQZ+MOor4BkDqC7mP8aaa2HJmhmvRfLOZ8V4hrcsxF8iHRjcK1B249pfJKAhUZaaoS1Sl0HbPz+WZm61JHgG0FAOy1BYYO+lGi5HLcYHI6ahtYyfZ57+v4DH9P8tyxgpEuDPMrNIEpywyAnq/DsPEprvi1AN+RI4LdkNRdmcowzDYazWowKTlpIgT8qNar+xfhwo9RMSXXvRWCaFl4spdqPWGNJo2sH1faHnmy8GNNZYab+BaOwDlGwj9m7I8lMl9TFY2s9a/GqG8/Be6T+3Fmj9nIOQYBPb5SfBoq453yLGoEp4Id//kfX4N0NXlXiqyrqICB6fqc/+uqy5mO/um+DmAAXnCLCxFgpPe+LSw6tPCcxNjthnUggkUAigZNOAotEj+Sk65dYGqw1AOMGAPUkgia/mvr7+9WGDRsU94uFTKBLTxDjrPvYRpdIwDamv30wrf2bT9+hRlYubagWNQp27zpQvaZX9g1JGk8w4b7pxnPVG954gYB/nKTpiZqeqLcyIVzWH0MQEKzLzEijvrWUGlse6myO2n6MbDpPtGDMf9EHolFH8ITgGvcWmcQVrTio6S+rJXW11C/MqQCb9fVyjQWvl6xDHYvgFH38uQAENT+AMEWNlYhlcNPE4nSR1T7kfX1JJ/O1xzvElK8U1SIAmGd0Xg/T6krdWJpzrRl0IjuJCKSIaGylNEyzZSxVb6QNrU5r2sDnlCkDXriQyAnV4jOtn+vsfoLLZUTNhSY3A6hU/eO5sJFbHU3gH7mjDpWdHLf7j4B9ALj40aQI01KpC+pDbT+Ip9I2/LaxDxYkuTy3NC/veXoa+HnzuPLbllWr+9XOJ/FbbSknhT6fena8Bc5+a9CcrgCfmA2otJGEv7nXnHeaccX+8JbXX6T+788/CEsEaH/ht7LbwWRXz7E4v+AcS28EAUk8f9F569WVW1ar721/Xq5l8Vzo+YlciOCPnt/p+kTAMjSLtUvG1C+PIhDLXPsfCj13b2W+59TwqPvMqZzkeiKBIBLgb9J8/kYGqatOu9jqq+ud7BeWBBy+PS6sSia1iUYCGgCMywT42LFj4t9v3759tQovXbpUnXPOOeq8885bVOAfG2BOgvTEqNawGA7W4Is/owHakdX0d9MZq9Qnvnp3E/jHvBs3jdRZeK1NMNF+7WvOVbe94XzJoydpnAjTZw81/kw51Bn7P4ojCjBNMBua1s5FbnVx4l8C0aaclwAglC9BPvqBg1YfgRKawFLDiQALQayG/qg2OaRPfHeBsaygayNO8gRxcGfteReLfh34w5rWTtsrfxSpLIt7az6ee/kQtMvj+xr7Tm/MxI7Srxndp5CnTIR5PwRZwd+aaWcIXsySgimr+PDzld9u5DVmpGZcBr4ETUrBNFtrkeousovUbOYJckzeWsx2+egrlAE1ilDMJ9AnzwpBYFpNEuTAWMvCtDR7pKwy8IEowT3sGFWvifaf5b5Ihn2/AEhrBxLQLOfxEQVWplI1/InlPRFFm12GVjf8/mVKLgl8lN83iAGgB5+RfvWynDr8/KRxpX2HhbyzXkBvOqtGBrzdttANyXkXj1W0AFF1+jL0Iv2xkVqBnHNwLqK1At/zJ69SA9VxnMF1a9AQL95e9/X8biEAgFnIeHK6F3PBKL9GeEmgcl/LodU5n7/SklSJBBIJJBJIJDBfEnCbn85XnZJyY5JAXCbA1Ch89NFH1UMPPSQRfnX1CTgS+KPZ72IkczKovxDH2Y5sNg3z3WVNRVhNf3/nlherv/7k78Ikt+Jfx5ph0+Y6AOi69gOQdcvN56rX33ZB7Ys6J9009eUW1SQw6ijAjO7Z9OJybahVQq2dRwIktVCFMnxoxUpkXwXZCGIQ7BPAT4N9BI18ViHyhT3K1aBNEBmIhmJra3UpLgdApmnsVSvS5OsN2itpi082pzqn4esuciJLc2P7q5UXrTcCtwH60ql+HbMw06WWoya8V0r2LsR0Cvc9gK+u/a2ZVdoVQFNf+mMkdcyyb7C3JIzMHBvisAv+Qb+IBQS5n1mCPXCUOWpJshJ83qqVqT0z1DRGoA2a/POdlzkOUBRgYBYmzSkGlYGchAgU4nqTpmD1tt9ntcKsfX8JCEqTAXoSEKRcihg3JWpDtq8ariVR29uOcgemVe64/T279HbXlkKDf9uu+sdSnaa7K60Obqv7S9bX27Evdc6pWQBwTrSqr+JGxum+ef2uP7hCpatjtKfH/wtBA4Fm0BDy/R/3XA9AsaTwSNgGDTHLDnqs53dRzXmClm9NP1sYVNlI1ZGtJdifxw0A6g/M9qUnVxMJJBJIJJBIoF0ScP6lb1cNknLaJgGtARiVCTA1Cffs2aNeeOGFWhsIMjKwx/PPP1/7ilu7ucgO2g0AUjyjG4bVrp3765LCBFpH/U3hq/of/uW16tKXn16/b3O0fnSZaBIW6YNHrypt0r3+1vPVa28+T+5wYsbJLzez3TbZAl/qX9KFtSkWeVFoznFhDdCigcC3taVYAzfvEy6s2RYX2XozCZkC5c45aImG5CggkTbhFSCCwyY0s8aM4q+w8VJLZwJcVYGNIIyiAG2pgZWFma8T0Y+ZSdljOPM5RgJrNJoFWY+1fPSe99mhHLKsI/e8FpLSxxFoaKok4GYKiFahN61KNBvUxHJbMGfuHAc4F0SzymdjmIymwHNz4E/wE/VkVc0uElZ8b7b4jHUY2n8Es8oEtoiBsABrfS3PmzwzzKSJQCDFSzCF7z5WD/wVTX757kOf6ki6Ogv3NeCPjVwEJHAg2sm2lmEuzOZmD9MHJGUHoUErve2E31wrcfx3MzBNU0daU7qfr4av3vHt9bmTTj2AZ+cAgm3MB81uSKnCcXttPWrjnbqq/nHRq34rV/arVWsG1JMHJ1RXlz1PLx6ci3AjQLfplJXq5stOU9/92S7RDGRea9AQzmPCzF808BUmr1cbQt2fG1a57PxpAC4YOYQSXpIpkUAigUQCiQS8JJAAgF4SOoHuawCwVRNgBvfQwJ/21dLd3S0+/oaHh9Vzzz0nAKCeVC1WEZqToHa1ZQMAwP80BKZNf7v7OtWHPnG7WmOjIWgkl8NsloFAljUCiZZEt7/hAnUDHXVjwhwX8KeLTGMCPwBth4OHWjdpsjWDC7DA5dqb2F0razdZv7fIQ8sm0J4Vx3/tKyxQXp2Y9cbaUkw29Z7XYqKaNlMU/FlfC8jmiy3bx80KvPjKXE0E2eePuAtKTGixcJ/LVkADapz5LZQgh6IWXQvAmdSUfLDJOJcL1fOwmn5AYTKTAPxoLgsANIWIFR0dbF+ljTRjLXVVjnVxxRYiGmcmy9CsYiMCUIB+5XiktiXbYVsK3ocMBDLbiqtaMKb2XxHafQT+KkF7HNqDMS1ktKEErIQAZRM1AIGV2nfgGsGyJu0/Zq42sAYENjFcyBeqoCe0HwmkZ8V8u6xKUP8qdENYLQK0fls+Z30eEYyi59kZPF9Gh/llZqTLw8x251PNkX/XrOhV+361t0XuRkEBD2cB5ju9s9JTZXXZlrFAHG/93a3qw//z31UngpO1QpyLcXvL779MZcGLx1prj3NQzmMkaEg1XdAPmZoX8y0E6kqtVLnMEQE62bZ2EOWo57k0w04okcBJIQH+TlZ/KxdNexdbfReNYE+uiiZv+ZOov1s1AZ6ZmRHgjwCfBv66urrU2NiYWr58uUzCKE49idKTicUqYv01mZNDPUGMuy1jG+qRgLXp73qY9H7o47erfKd/r/r0AyiahDZzxztuv0i9+oYXxQ78mbKiH8BWAcAOLtxNpvp4Hn4MCaBFCm7ptjjtq+Afb5dpA+WHqhMbAfsIABFswDWfuf2U4JkmMhmh3gwcEKbuUZj/ivmoVfPUpvX0A0jgh/7lgoIENG0uwwwyFFX7WuflqfQ3QVN90ceekTtrgB/A1g50IEEmtKiS28Ks2IPr5gIVIGY5b/uUepZOcJ/af4HJUien/DS/JYBkahCbVdf56HorLABIuZOKMPH1FLx+Hi3iYvTcOfr8s6scmZtAIE3HLfmZhFQD/nSlKpcXx1/2KfuLY+kYBCVjEHMLPIPpoziHFqeAz5BVnNqBAgDy3Vvti56np2DW6nPAuUh61dolasezdBBaJ7r0KO2b8hw29RzRH810Oy8J8ng+LzljLFChl19xmvrUJ74PbbZogDUCf2++60qpg56Xca/no/oa555My7monse5VVzPVZlnIdDS/FrVnd+vimhbtk2gpDnHXShyWAh9kdQhkUAigUQCJ6IEnH/tT8TWnuRt0hqAQU2A+WX1ySefVM8++2wNCOvs7FSjo6NqxYoVTSYXJwoAyOHCiZCeVLZj+IxWAUBOQzumCurK689Wd//ZKwMXvWHTsJr7ljXbnHr5y9aqi7eulIkx/fy1i1qOBMx1rgM2UFvotqsxKIcO+ttmeKzBP6w7uSB10gAkQCpBOeB3jllKna3Ep2xdmKiCIzgRlHvNRDloRqQXv26QXWgCqJWd8JebfuWopJjzmd7kSs00woaBiEIWQddzSXtxzQ+n1Awi7U7SnBclw+yW2n0V4BILdjJwYSKAGgAYk3yD02YmHmPAdh2omHtab3meE5yhvSj3DsRXB4EiapAylUVkDbnsgrk0JLCcNPAic+dq1HMyE6tshzcQ7PLzUmM6qo5WwbE68+pRtWJSHQPEakq3wC5AObMmQwFD5U0rrajUlP2M/xRdCib5sWoHoiy+V+mLMb93SuVmjHq0ILcXbLTht4wOq20/3NkC19azzvQ5LwmGMl0qmwkO5F1x+akqY2NK3WptOTfT8zOtvaaBQPImqGcCgTq9Xbka/NJzV7s07by2ontUded+ogrQOG0XAKhBULYzSjmYfeL4UaOdwk3KSiSQSCCRQCIB5fxrnwjnhJOABgD9mgAXCgUB/p555pka8MeosKOjo2rlypVNwJ8WGCdaJD2p0tcX454TIe1nph317+7Oq6GhXjXx/BH1e/e8Sr3s2rMCFavBytGxajARLpaqdOXlK9QZZ/Spbdu2qd27d6s1a9aotWvXSrRfnSau/TKYALdCtqa/mmF1oatP27J3ACMjL9sC/olTei74AUoRbNKAH/cmQFqEGWEZkUXnkyLT/oOsGX04FLGf6o9AKBY0CbXDaeyYUUsRahuVoBjGs2eX1nrN6kPQer/hXI957Nk8wXeo5dmQyHKCRKkZLCiPFSvmvEDwKua8zFUF/CxZ3E6LvZCK2UaAcMUe1xo4sssfguZh2D4mV8rDoWgB/wBUEvwz+9GsulmxhoAm5g3jWIu/dsmh7Np984B9Rm1SFyyFIJgnBiigp8m48bghv4t8GnPN75kJ/klN0EmMKE3NP0eqAqBxaQdSKzY1XVJdhyN4maARy1d2qecONw72keE+teOnexybGPsNvBtK6bIq5J0H5cbBgVDVeMMdl6h9zzdqO4Zi5JBJzzc5V9PzHw1mEXDS13jMNNx0Hs1Sp7de1/fbvV+3dJPqys+IBmC7ytYyYHlxmAAn4F+7ejIpJ5AE+NvY9IMeiEP7Ey+2+rZfQkmJPiSQAIA+hHSiJNEAoJcGIIG/p59+WjY9KWBEtvXr16tVq1Z5fh3kBIvEvPz6t5h/+PWEsJ1g5sUXjamXXX26Gt203PfQ05Nc7inztesGVRp+kqiNRHr73Veoyy7boJ566inxz0itzl27dolJN8Fc9q0eH5Uc0f5dNtgTmqGj6S85oq3zRizbCUWIolLgL0UAtZjTGhRY9Pc9iQipRSAERDMIPNjUgVFD55siAQDRjLCmv2y/aA62IIgUtCndAn9YWdO8NM+1rk2fWNNazwWwYb9Z/Y6ZCXW3Yi+YE8+BS4gPQTMdjzF4OlD/DLY0AkYwIrHk0TBY5cSay9e5mF8yMINB0t9O2mhGOuthBr7FCLLGQTXwD/2iwT+vkqQf8B4FStBQpaZ8jc1vSOt4Aibix9Jj5jXH+40YURNLMfH2K++myjexm/cLUkUbmVKr1BUANGvO5w7/2XNRaQemoB3bvbcAtjaVM8v2eVwS/2qNndsP3kcKbiinT+Z+knFsEzzW++rYSMO33oofTKr9F/aI9riV1Tljq6yXfJ3zo+boxmFfaVtNxPma3vScyNRA48dcbkxDkItzUx7r+Z2eu7Zaj1bzD3WPqHwG/i4xh24X6bk+y6NMEkokkEggkUAigRNXAh7T0BO34Sdjy7x8AHJiRG0/gkQ8JtFMlODQ6tWrPYE/LVNzEsWJlXmu0yyWvZ4I6QliO+p9191X+i5GT3K512ArJ7U5TOaHRxCNef8x9a4/erl6+SsqkYNPP/10tWnTphrAS7CXpt3choaGpK8HBgZkYuy7Ej4ShjYB5jqcq3gnwuIlmmWZUwH216VM1stZYcI+o9+rVfBP/H0ZgBBlUQkQUFm1yV8gFtScoXYgQRiaFaYYIXS+KYI1hAB4bv3v0cYGLSiPtE230Qe5owHliIU1NTPDAIAs39UPIKuCTcZe9VgCujCjJoJ+8NWVg5+0DMZACBxSc3LdF6y+/1BuIYT2H8H9zoOBDZ+b64byrW8CDpsyNf9wYAVIXeWCm1nEKyr0VYoh5xq18rIBI4LZcz48L5SRxjYQiK6Ih/afJDMqzufAONVcFsxe6ubwvqDpdnY6ZO2DaAfy2QHYR8AvhWA+HJt09ZCZQhCciIxlVq8dUE8daPQPsIX+en+0O56+0CAfTEkF9OMowH/zo+wcHwYEJeHLontvUa3+9lG1/6IeNbXcGKh4p6xfmlLHjh1Teh4ZT4Wj4cp5mzl3I7hlzuF4zI+glAPnpxr80nmiqUV4LqxXZ7rvhNEA1ONN78NLJsmZSCCRQCKBRAJRSCABAKOQ4iLhoTW8rBqAnPxo4I+AEIlfR9etWydmokHNAcxJFCdaixkA1HXXE8SF0tWUq95M4I8TLG4C3I4NqTcgCt+VV25pqDa1OTdu3KhGR0dFG5CAL8fEgQMHZOvr6xMgkIFdzL5sYBLwJBQAyIUKFmGu5HHbNW+LN8UPoAHOtciunh0LUcEzyNuq4WNpr+ARlBOvExSoEobAvJNEP22lFlizhjb9ZblQnmhFDBlEo/WteaTbyQJbEL6tH0B2K/tYlyHIlnFevc5Ivfmj8KOn1X5rGXTGaPYlADVli/afoEtBnwUM8s6DqC/bEzHJ48A64sDkz+t+iAD6XBUArAveT06HNKyHT/CPHOj7LnO8uY81d0/tP4JZOjH3fhtu5mnTsVTNAfxjFYrwZaqORKAJxecS/1mU1g7s4IfOKtDXQS1rpoEmXhr+dzsAAjLt6Gkr1M6nx1mVlqlrCaL8GABgFwJ77X98X8t8hQHBPmoQcy+/BTjG/0bQpSIDXaBoljNAh/HO4oeDFf/nmDpyal6Nn4n64jeoC1q6xcnD6ic/+YniB0K6DhkeHo5sfqDrE8eecxhuer7EvZ1WIMumrHg/qnlPK+3pzi6ZNwCwccy00ookbyKBRAKJBBIJLEQJJADgQuyVmOqkAUDtA5Cgzxe+8AV12mmnyddQFkvAi5M7bmGDRGjQjPwInIXlw/zzTXoiyEnhQiA9eaVcTeCPdWNdCdZq+d9z73U45hLGnpiOfgCp3Unwj4FeDh06pCYmJtSjjz6qduzYIeOA91vtw6EQJsCupr9sEvqEoMdcLi41PHu56auxaNVo8A/mP+airFamAfLpa3Z70XhiWiuAaJc4pmsESNGI0NxbMf1loeJnLWTpNKHOHgueuVWzZwHviP5yQU7x8VD2AHQASMzRCT+vGVWjmXIOwB9NfdtBTb7/UGgRgFVQyh2BP0L4royGGssvwQcmtWJrYKhRiIF1GFfxOsGrcnqwQ832QpOWr5RGlg1pA52gW6jZOYc6+aVK9Fn85thVls+VV90sQyGWd5Xfxrikk2p6tIUBOMpoQEocBLowC3oL70b57eBYIWCKZ55BcVLTcLFQBf/IMqoItkPDvWr7rr3gWG/w5tUDatuPdweteQXkM8E+vjPIuWG8oJx6UQ1lSGoCfw5BPZitf9uM6txfVPsu7lErMp3iIoRzRs4RuNEfNOcGnEPweKET50fczDkULV34AVTPiTkO+BGc6Tg/4n6+aEmuf14AQLa7cRzNlwSSchMJxC8B/jbKHCv+oiIrYbHVN7KGJ4wilUACAEYqzoXNTJtuEDz6u7/7O/WP//iP6uDBg+pDH/qQOuusswTsodZfq2CPBqAojYWmORe0h3Rb5rsd5qTVC/jTbXQD/3Qa7jnZ49d8bkePHhUgcO/evWp6elo98cQT4iuQE32Oja4uaASEoAFqPgQhLHKtZntm9vTELCKzFlShP8TCg+ZQ8I/YMkWNCWPxIes4B/CP9Q3yw08toVYBqbAySkNjhKZ7YUmitbYiXz2pC1mFLEx/g44QPpetAq4yGYVS0hwOaEqcguldChpKBENxVc0M1iVKgDw3AeBvyqLtVU8S+VEJmEE5ZxEqgIhyPpi00jDpDGxe7dYao0pFWC4SQKP2qHFZ8FQrCw6xmaVKzcK8kWa3toCbNVOQcw4Jgn+GNaXv7HwB2oBentp/bAayNpD1vOHm/JxIldhBZic5VIWRzyvPgEOCVi/jN3AO790SfhdKPegs/v7MQhMQwT8O7I8mgMXwqn61dzvCFldp9cqlavuDe/Sp854fH/mbRcCP7xj5keBQNQRnHjtzkjsid5r8+gC3OsdLMAk+ol71B2eorVu3CvBHa5F9+/apmZkZmRswoNjIyIjMH/v74c0wQF08qhrLbYJ6fFezDZzfsB2kzs5OxfpzrsWNcz7O/7jNBxA40DUoUYBjEYINUz3H1XNemyTJpUQCiQQSCSQSOEEkkACAJ0hH+mkGTT9p1smvnh/84AclC6/xq+4ll1wSWTRYcwKhJxV+6rcQ0+iJHyeE80WUITe/wF8r9VyyZImAwZs3b5Yv4/QNqL+SMzAMJ/r0Cbl0KVbNPoh1fuGFF0SbMA2NixK1V7wIScRRvk26FPwx5Q7PyAKXnGrBMWzSNl1C2enjMO0Cj8IwAMkWFyqy/GJ7otCyg5xEMi7gn7RHEjW1zPYCA1KU5uENT/PJrgNlNTVCNaoQhDa2ZPqLIgmShO1e+tDL0o9fQIoCbJ3DeMofBvigXzfV/iYOlJkEANhfuUDgL4tzAwIIWNtwyYtW339gwyANQYiaVmL6GySTz7QEKAnkcPzYQZJ6TMwiJtF0P8AeRsrWF32W4TsZuqoG/gUTkRRRBoiZ1ubculC+b/zwqo4bna0JENQ35mkv1WM7/LQFyQR0FhC8TRXGO73cmZHt2Tn4A+zNAICs+gXE+PVZ7Vplu7tzavue/bVzHuSPFxWf9xoR2OO59tfHc2xNgFoL49X091cr1+UgjzH41vf+jrry1edIqsHBQcWNHwc5NyAYSF96/GDIjR+ZqRHIwGJBXce4VCPSW0eOHFHbtm1T3JM4Xx0dHZV681w+5PAAxLkPN84DNRCo54SVFPH9XdY9NG8agHG1qmksx1VQwjeRQCKBRAKJBFwlMA/LQ9f6JDdjkADBI5r6/sVf/IVM0lgEJ2c33XST+su//EsBdKIs1vyRn0/gLIo26cnefLTDCfijfNl/rJuuXxRtNXnwa/gpp5yiNmzYIBN9mslw0q8n+vxSTiCQWoNmf2senETTrJhmxHQcTurtzqgjx7xXcbamv/DLlB+fgTaUgewQePMDvqEuBP5SWHBpUKCIRRyBglaJC2v8b41QP+GBxZYrGMHFYICSKpGAg+QIwNwhKcGz7v0lCUQSVr4ETVqudViXYZBxJwC4oCSLxpB4Z0NZaDiBWyFWg4LA896BDxAcu/lDRZWHPzSCEcUuFBiFJqsU5v2HYHKTph8Ai2LA4B8M+hHYt6JX9SCnMsRB34R24B9FWYSp5/SyDlXsppAp2BgJBQr4xxlWyKIkEIgFAPSj/RdjqyJhLU8XZRJALkVomOYmNSoeSTX8M8Hzp7UDJRPGvAYD+XvkB1wdRaCPXz/xQq3MMzYtVzv+zxMVrT5+XJr4VuoAAEAASURBVKRQ+H5HWQ1kPW+4GezEzt+fIwfU6fSzVqs/+8gtqndJd1Myzg/oR3hsbEzt379fgorRLJi/9Y8//rho1q1atUpANW150sSkzReo6cf5yHPPPVcrmUAlg6KxPZq09p855+MxN/YPgUANBuo8cexX9a5WswU4Am0Tcb5JYtuiJBNQjZJvwiuRQCKBRAKJBMJLIAEAw8tuwefkhOVLX/qSuu+++9Rjjz1Wqy+1uD75yU+qq666qnYtygM9SdIAVpS8281LA2x6ctSO8tlvLE9POPWiQMu1HZNP3U4CjQT66BOSE336CeSX88OHD8vW3d0tpsGc7OuJ4/j4uEy09Rd28lqxYoVaveKIOrLDw+E5gLkG01/IIgfgLw3fTAKOGQuishf4gQVVCiZc6cnZRp6ojwTw0I1sZU+NDQJ3IUkmx8zu4quxxlpWzrUzz4NWfOB5MrdJQOC2ex/AKdSTYEsYatn0l4WifFmUh+gWmtPWtO+CNIBlGWMzSFadltpAduAf4WEGJCF1HoR/MoJXGC8Z+L8s5XGXvrzaQMWe5nJE6zEAmJadgN8/mP9GTfTfR5NzAf9s2E8OILbDIBIFqGsrdRRzVYqLqG1Ikui3U0Zmvmt8jmkrKNWBd6HvzEaRUR9K18izEowzg6LwXal/C4Pljjg1xlA5j86F94kS68TfLBftQLri2PPEXpU5MAkfg/gIhfQ7d483t6XF94dTK0XmfEfwN8arDLSnE0nf/CfXqFfc9GInlrXrnB/RqoQbwT9aCTz//PMyf+ExN2oMUitwvoKGcB7FD5i7du2SerHytHQ49dRTxeS31pjqgf6wynx6M0EsagRyfsaxyPkR93qeaOXVyvlo/yb1m317WmERKK+e4+p5XKDMSeJEAotZAvKSXMwNSOqeSCC4BBIAMLjMFnwOTla+9rWviXbfI488IvXlBOW2225T//Iv/yK+Tzgpi5M4ieCEQk8q4iwrTt56MsSJYNzEMigvPdnUix3uWQ9ucUw0/bSL5XKST/CYwB6BQPrQofNsfvHfuXOnTPCnpqbET5DmyUk/NQUYWXj4AX55dwEA8SNcA60gi+yRWWjuAfDgooWbheacAECMf/HfBG3DFBfNduR03S6tyzXxs+dy3+2W9DMBCZ+ghGj/uDG03HNsuyVdFKdcBFPzL119TEpWP3F+CmH/eyuIenLiGLIZLp75aH6XnfBOZk3Bfmw54jF4ELSpATfGmEgfB6iqkR/zMUCaNMytS2W8MwhIxEglukWz9inqXAig/cdgJflD0b9Hy5BJCZFiCR43fDyoymMaCkylvir4weIpQ1OOEctNxjD4tzomGPxijuan1cEcSPvP+tqznkfcZl/sMF7kXRdG9hjrlGfQd6CverWSCH1jpx2YnoSrCfxmksrHZlTh6LRqmGyHekEFr6h0ew4le31gYt8US+qULSvUvX9/m1rSDxv5gERNvy1btii6D6GWHc2D6V6GHwS5MVCIDjrWjqAhfC/TCoHmvpyXkOjyhvWj5p9+rpyayTkPN87LyEuDgUzPc+an+bNOF/X8rCvbrQa6ep2qF/l1PVfXc94oC/CSdZRlJbwSCSQSSCSQSMBbAg1zEu/kizsFQYu///u/V9/4xjfkyyQnIQQnXve616m3v/3titpMYYmTAwIhDz30kGw/+9nPFME3ThBI3/ve99QVV1whx3H/ueGGG9TXv/51KYY/vLfeeqt6//vfL188aQrMuuqoZ3HVhZMikp5UxFVO3Hx1OyizuIi8TeBPl8O+m2/gT9dF71knmv9y4xjil3VO9Bk5zzStsfvCvmzAfVFB81GOmvQxBPg4igUUC0V5TlTOVsaYeb+Djtuh8ZeGNpobVcxj3VL4vMdiuHhyqacdJy4gZDFsAD126cxrFS0e84r7cQ1MdU/W+l0AZ10HIHfDXLEcQgOQwIlzb/uvZiiQAP2RDxH4Q2rFSgfoR7uWzAFATRPJIi9jLEk0UlpmVQVDjbsaSEhGBAEx1suloirDxD4uKtho/4nZol/t1+oYaX5iW6sx38rUDlM4sOv3AsZhEQE+5gBeUoT0pch0orlYlWlrNWjMLSAk+IcZ/42cqmfS2Wwf3zG2KWwvNowRpLCe22aK8SLft9T+pFlzoS8cWM2PCql6DI0YaxuCNQEhtDE1A/N2gn/orzmA0qT08RAORUNUwZpF4sfkIHCvdxN8DuYxQO5479XqlbdeaGUT+JxacQwYRqsBmgVTC5DWAzTB5YdCauLFHTSE2ojbt2+XIHdsAOcttGQYGxsL7JtQzwE5F+NcTW/yGw7e+pxzOKbVc7bAgrPJsLJ3xOZqPJf0XD0OAFDXmP2QUCKBRAKJBBIJzL8E4lsxzH/bGmpAQOz222+XKKf6BgGMhx9+WLZPf/rTAgzSH0gY+ud//md15513hskaeZ6Xv/zlAgC+5jWvUR/4wAfUmWeeWSujp6dHTUxMyJfZ2sUYDvQkgpOjxUy6HXpyFGVbKJvFAvzZtZuTObv6My2jCXOiz0k3tU2ZdsgNAMTiKTONAB+HKgE+7Mozr81hUWNqAHbAF5MAf7P+xhsXaFGQTGfJKsC8VhYOXv7+7CoXsMptAQCx8O2CT7eMZY0bWAMQIFfN/NWu7X6v+ev+Jm402cxUgkE23fO6YBOo1StL430AeB2CTOGyZYGUnqI/RQM242KegKBJyJMioAw/l+VuLPgjJmr/URvNSkUCbz6pcxzPpwEQ+8zmmoxdLeAfmi6m45bUNAueWZaCOXq9njwSEBUyFA29+i1L7uCnonmKNpZDBCZ3Kk203lBXATe9gByTifVdYT0308Z8LOAfA9ZgnKbxkScNM/bpQarzBRM+3ylxmI+31HwC9zD/TeF3h78pKbiq0O4lREsMH6T429Ru4u+jouafm4z5zpieVWObR9RffuIOtdTt9zlEA9jf/O3nRv/B/FDIwCHWoCEECukihMBhq8QPkZx3EHTUAB2tEOjPuJUP/LpeBPi4adCPe10O03A+xE0DgTq9zh90352r+yYMmjdoetabpOe8QfN7ped4SCiRQCKBRAKJBBaGBFr/xV0Y7XCtxS9+8QvRgqMZAM0U7rnnHnXllVeKWcD999+vPvWpT8nXwmuvvVbAQJorBiVzEpDNZiWSKicjv/71r4Oyajn9W9/6VnXZZZepc889t4kX299OAFBPKpoqskgucAJHihLINCePphg4QWJ5nAjrcs37C+WYE/jdu3c3TLIJLDNgCPubWoH8An/w4EHZOOYIBA4sddCwxTqkc98UAECudL0niXMAz0o5LCCxyKHJX8cEgoNg4WXAJBVRgVUWvo86EYGxd0mnGhjsUUMjS9SKVf1qEhP3f/v3inl8q3KtaRT5YCTviTDgH3gH1eJphwlwJ0w67RblpSAagOhDBkyIggSECcoIC+H8EVQiBFX6M0RGnQWgAQEDWRxZxj61Wa3PgzjyZ5RQK/HdQRQaQIRE5bXwsib3e06p2Gr/AfhoCgjiwDSD4A25qg9DhySBLwv4B59/bHIHxo712ef9qWFo/uG+smCivCTAGsA6AQGtmQPXBhlQYAoAchnBxaMk9mUKoFmQDwzUSGYbTQr67jDztnJcA//048VxiY0aw9MD6B8nNw42hQrYCy3deSfIl30ioB+eA5G1gH9wV1GtnlzDcWoetP9EphkXgJXAX6GosjAv/933XqOue+PFsYuUQTb4cZ1zBLoOIUBHP8KcJ9A/9RNPPCFmuQQDOZcISnwPE1xkkA/Ou0nkQz9/y5YtC8rOM70J7On5nJ7r8l2ur/GYgBq3hTynY4P1HDcuANBTqEmCRALzIAFa1gS1rpmHajYUudjq21D55GTBSOCkAADf+c53CthHYOXb3/622rp1a60DrrrqKvEJ8r73vU9AwI985CPqvvvuq933e3D66aeLefEFF1ygzjnnHIkqRj7zAQBysmUH/rEtenJF3yxxkp5E6ElRnGXFyVtP2vTkqJWy9KSQe05YZdEPhtyznIUO/HFiTTN6Any6X3U0QH7B17JiQBD6/GFagoCc5P/mN79Rzx20d/CWPTyjMpNYjXPVxMU45OFEBP/KAP+6e/Oquyev0lMFtWT9kFo21KtGVi5Vq9YMqLVjw9BqGIbfQefV+PYdeyMDAMU8r2nJ3dwCBnOQgCEu7WvOVb3CRRv+B6G4NQAZjTYHzR47avIX9/+zdx4AdlV1/j8zb3rKTGYmCSnADC0NEpDqClIElAURCzbAxvpXkUXXsvZFURdEQdHVdW2sddUtltVdXff/R1ddRUUwhIQQOoRAEtIzvfx/n/Pe7819d2597742c3/JnXvfvaf+znnv/s73/IpXoty9pEx/KU4X4AHVTXtEgI2iAn9ISVlTUv/5Oq0y5w1AA5QucpsMzkdcZyTIR4H2HzcF9CZYiA0EwmcXNWL6Lv8xtQw1/XPl9fqI1pWX9p8FGb0yuO5h1o/2X5LEjCPaL78VzB0v/G6oF3BJNgp8JBxGTLXraJsdRy6KIelek7gYG/f/uSmmVJvHBgI5ID0uRfuPkmBapUnmaTOafx51A1K1SrTt0Q7Rgu/wGsHpjZ1oEc0rUX/N+8OcnqSsd9DkA/QD/Cv4xrvAP21Eg0SutyC+3ijz2bKZYB+Af34kG3QNg8Nm+ZGLzLVfeLXpXhh/s9uv6Cj3kRGQFTjYiAYIfPLJJw2BNbjmQGMQILC3tzcvUwSVjZkxfv4oD0KOwrUP/gZVJgnKX+oz6tBDZTy7MZQrmL5xkIa2qbxXar1J56eNkMruSZeflpdyIOVAyoGUA7XDAR/xuHYaWGpL8Mn3y1/+0hZz5ZVXFoB/Wvbb3/52c+utt9qdyFtuucW8733vE80hl9qAJvY5n3LKKYaj1kkBwEr5AEwCOKsmT1UYUsCrmLaoUOgE/hACVRCsdeCPviOYP/zww/nddZxp9/f3ewrZ9Itddw7AP4BAIgO2NU9fCRKlt/VpMfuFsfIYnKtJAL4xWTy5yYJ/rU3mnHNXmb94w1lm7rzizWN6e5Jzrs0Cd3rPCltvwT8W8cWAfxQlFRQsOguL9/xUTgCw+YBodQVo42A2GolkmBMx/aUyFOYiVepIJBo8LQccn2NclqT9J/Va0NFnPljgwAcYDGsiUUb5IlkQtsgyqIM5PTbHA5wRwGMsSvAPaQOaXl6BOcL6EPTcgpIK/nl88YY6idIqwCWFBMxDWK8goP0O09W4E0gqaZK9tHGUluLmpX0hxBjaNnr0MyRrwWMvEK4gQdIfZI6wORBYr2zmoD3cJJFxh7sCNNYcbQPYthGWHffKegm4J6BfBhNfrzEQbVxr9uvRCBsZ2ON+OW5l/f2JOO/3fefFOjQsfkYnzCv+6nzzoteeXo5mxCoTSxs2zgnKgXyAjIFcqkFD2FzUoCHIG27CogfNwaeeeir/iPSAf17p84nKdOEEAakCuckp/3JdzqAhpXZL26oyb6nlkZ93pHOzO4ky0zJSDqQcSDmQcqB0Dsx4APD73/9+nkuvfe1r89fOC17cr3rVq6xpMGYJBOw4//zznUlmzDXmmFC5AUAVIkoBzmqB6cwNSIWjOG0ijx4qBAGOQZQLyKzlxym3UmlpO2Y1mPviwBsCrMScFyffXIcR823NmjXW/GfLAw8Z84NHprLI4qp920B+3bzg0C7z/htebDpam80N7/ueefiBHfm0DRLwY46Y8H7gw5eYFSuX5O8Xe9E5v112uhtFSE9AO4mFIQssHzAn/8zveYROeC4+Q/LZmQYYhMlxgtQ0MGEw/fUrFd9raF+FkjQtKdNf6vIKAhHYBhkzAn9EaKl3MWSMO6Yy52mnHc+AvJ7af9qKCA0GXGwQDcJxQHe0googC7R5jKPVlougkUbE3yZvpd8iWpPNMiYAEHPLBgySrrlpVLTwFJz00/5z5skOAWMiZUp5E7AqAn9tGTKWzQL+jXVIhqh5nJVHvJ6Qn9lMDD56/lZ48Cpi9fGTWb5k53loZhkA/rU/PW4GxSQ4LFqtBbVj8CK0fq8E8rswzcTXI53V8BP/f75DL98Rfe97ZE/sVqC/P95LYu5rROtvmWj9ffCLrzE9i+cnVncSBSEHadAQwD98BWImjN9ATHoJHLJ48WKrFdjZ2WllKjYjOVQuIyjZypUrReu/shqNXv1XmY6zyn+cmQuQ3kM2Jg2ysm4Ge5VXqXsqq6vsnnS9KvsmXW5aXsqBkjjA17KS78eSGpvLXG/tTaLPaRmJcyB8BZ94lZUt8Fe/+pWtEM23E0880bfyM888M//s17/+9YwFAFUDEM2scpIKESpUlLOucpatwpwKmlHqUmGPvusCQIUfygM4U/5EKa/SaWgzZjkI3uyyQ7QbIb2vry+2diz5ibh97OqVpqP9V2ZgMOvwrU3AP6utJAv60553hHn1lc82Cxf22Lqu/9xl5jM3/Kf539s2m3ndHeaUM48xb7rmXAvaUV6pBPjXvaDD7NhZ+vfALgDBEf1wFnlZ47OjFHPReR2tZmBv/BCYAEEW1CiVYbn8mWEBbcWk03fRK+miRkBN0vTXakgyBkENc/HABv4o0vcg8ldss1EBRqy2I6hTQDsbxJ+lfAlcrY3/EWClCRBQ+DLZ6jc5vcuFlZ7afzKPRyNo/2UGRUMU09UEaUyklUkif6v2pKtsAL9hQCThr605QPsvn1X60zQgGIlgIo2Ck3AAuAWNj80rY9myj/QypnJgqjuOQjJjmzCh9RYHAPRczNgJK3/K0L6C7ubAv7gm9fi2bN89aYbnyqZZLnpuQbm5D+OtMr7iU7Ic5Gvi665M5oyNzh0W3ANeS7+yLiLchSTz2foEBeD3Gtcx+R0ZHLLW4y9/6/nmJa9/djKVlqkUZCS1HHAHDUEe4UArUE1qaQafCfBBVGGVscrUvKKKRW7icMuEWhgyIoemQy7kuhpUThPgWhybavA4rTPlQMqBlAO1wgFE3RlNOBiGcEAcpLHE7qGS5tHPM+msAGCqARhtVBWoiwJkuoU8hB4VfBDq6gH427lzp911V4CY9i9btsz09/dbYTsa1/xT9XR1WACwec+I+M0aNz1HdJoLX3y4mTO32axfv97WgYYhfgTf8cGLzX+duN6sWrvcHNaXvCPv3u65iQCA9BatGws6uLvOYjF3Ty4loTtB+Oc2MXueICBEEYQTfuMRwbWIoqxWTIeYdHpqGDkKRHMslMC4iuuSZ9FWqy5CtfnMMhj4ICueJG8ELbh8+QpaeS3U84myF5lhAVgDFoEAj2FjkC9SAAiilE4IMDPRHv11b01oPbT/7CQP0ShtEECMyNBxhiPfXp+LccE4rC9C6QcRf90EJETQDwVComj/YZbfIm7DrOaoNBbgjzlpQUDwUr91OOCf5COt/Y2Xyhtln6RpUErMiHm0REe2PgrdjSzyswUkY+T1mhtJjoVvU+Q71VyKP02ZV5gNj8tv3eh8b8Aa3uqmmm874jyQsbQmvmjyRcEVpY+N0j77uxqlHoKcFPnbHVi8DOhkk/CIw03SJ8x9zcioWXLEQnOtaP0tWtLlTlXTnwH2NGgIJr5o+yGTAAwqoQ2I3D5/fm1pNGr7nGcF9ZAnkRM5nDKl8x5pqgEE0gZIZV5n+9PrlAMpB1IOpByYWRyIviKow34jLABoQPgGCaIFCxbYABkEx8AXyUylSpkAq8CjQkW98jNqP3QnVxcn9QT8MTaY3mBus3fv3vxQLVmyxEbt6+jwid6bTxn9onfBHLN9217TIeDfX/zNheb85x1n68RPoJr94NAb7UOAxzPPX5EI8OjVwh4xKU6KiOQ6BfXlSgXxy2FMnDDJnOzwWLCFNKL/kAXmvs1PFQWoJOUHEFCnY4csfCMskkMjAAszEvfjFRNMbBKgoRTfdNbnVsi42cfSV8ACC8pEAP/wJxaq/Qc4CLAblSQ9EaEbDo6Ir7rpvrTcxTDEntp/3BcAJpBkzreJOWdcDbCgMjEp16AyXkE/4MTgIkmUA2QtZyJo/1kNUMFJ8hqygIDy9WSOA+5RzjQtT+mfBf8EhHRzAo1Lvm8tYhY8KT4y0YQdaxfAygtIDeqw65kN6OK6F/jRb2pw393owIJiPBS+EPCjZFBfxjAjPMzsGDNDPYypHE7CrDY3Rs7bsa6lrVFMfKeVST75fvJbH5UmMe/EOWmC1NLWbIZpA9qFTpL2AfqZoRFhW4N58dXnmldcdbYzRd1do5VGkA/dkHR2AFnl9ttvt1qDyPcLFy7Mb7g609XaNTKlHgr8ITcqqYYjadg0RpZUOVTTlOOsgGS5AECVicvR9rTMlAMpB1IOpByIx4EZDQBqVDBYosBXEHvQjgMA9BI2gvLV0zPVACx3H1WIUKGinnjkbKv2AwENYc0tiPkBfwg7CG8q6DnLrKXrffv2WeCPaL1KCNI40i6HP52OlmZz4mGLzbu/cJFpb8uCEezkr1271pobE2EYv4MIwRpxGP8/aAUmvdOfZCAQCU/pSay3Ee0bRiZNRkzGxkWBYaItOgh43NFLzMYNWz3LjnIzESBGFpto/kVd3CtY49c+a/o7td7xSxb9vpSVBdiiZQGQA6QpluxiLcoQCt8szwD+IgIvjSHaf7SZYDiTgIpxOiBtaBA0q2n/iBmbFwwCTqBl5aXlJ3VOYIIZQC17J0xz1l1oQKroj8Q1nzWvJUeDmGt71T7ULe11aLlG0f4TNTLTsifbDgtqswDPjRMAEwCvHTu5bUFAmC1pmtH88wD/siVN/eX3H7PdRvnegzQDigMGKkg5lTL8yrZHktGEKOSlAUg+7sv/5Am+JAH+OVsmmnNokQ6Jcteka84RYKixiDkW2cTX2Q695rsswUr8eKvJpp2lH7opOO1ZETd6Fs8zO/fIS8QN/uXMfRvE3n/R4b0S4fc15pBDFxRRQ21kQdbCFyAbgWqaijyyYsUKK8s/8cQT9jmWLMguHGFBQ2qjZ1OtcMqG9BdZkrMS1wQNsb8lFdAKVFldZV5tR3pOOTCTOcBveuzf9SozpN7aW2V2pdX7cGBGA4BOc4EoUcHwUwap3zMfntX1bQUAUxPgaMPoBPwQyPSzU2BDQOOAKiWsRWu9fyqAbjT+0LpTQgsWsxscapeL3v3m80ybBPnwovb2divgH3HEERYEBAwk+Ij6/6F9AIG9vb15fnuVE/VeohqAVKoggl7nFtw2YIGAJ00DYwIeZMxwRACwf3m32bTxCbtqj7r4d/c9sqmaO6N+lj517JQomDEc7wdpAMKLhvFie6ONKjyjdZX7+hU+8Pok/WnZGxM8c5WTBYRC+iDjbcHXyA0D1JCOuDWeXHXbj2i6OeeaVxqve7RFFpJN+wAB5Tvo0TaWn6MdXjCbANcOkM2r+MyQ8DYgMrRXnqB7FvzLaRx6af6Rd1hiWo07tBItwOX981JQFX7/tJdWmCajc0jlIfVbsE+eTQiIR8APvgfOZAWFenyw7wXJ3ySAFfzBRDi2v0AZpwkJ5hL5O2iZ4NEYv/seSSPfknnYhNlvskputvpJAc9a9+dMgudNIe74AWwWX6SRCOCOKL5RTXy9Cg2I9OuVvOAe3zHMgOX3oFTqX3mIefAhsWhx/kYAGInGX4No/jUIKPiCN5xlrnjLeaVWVdX8gHlYASCjQAQJQS7BIkDlLA1EhuUCFjs7duwoCBpyyCGHWKsfNhc1T1U7FVK5goHIlXq4tQIB6OhLubQCUwAwZJDSxykHUg6kHJhBHJjRACA7gkrspIWRRjoFiJippACgClfl6qcTKCtXHZUoV/tBXSqYISipcKbCJWd2TjmceSrRxjh1AG4/+OCDhh10JTTrELC7u7vLLiz7gX/aFs4I/H19fTboCP5/0AREmxdTIA7mMAFJMFEuZbc6UQ1AGg56ouvU3HpPwb/mAVmgyT8Wo22ygBti4RZAnfPazN6dB82kLGBLoVJNgNHCaYq62M41dNwHKAJoaR4WgzjlUSkdc+SNYpasyYk6XEpk2lDtP+ljHJNfbRfnKNp/zvRFX4vfsKZ9owICyuvfCSZIgeNoqXlp/8k81Oi6XvXSZ2v66/WwiHt8OwDK5AfJN+LvmGjVjc4XcEXSKEXS/pO2NgsA6CTm0DRzXylW/QJabb6Y4J+zfK55R1jz4iL8BdKOSAAgwLAP8f3zf+qTKeh2DvzLePhkDMoW65nMxYxsGmSeFpPgbvnhEB7aeRHUE2mXNfEF9BPXBVOzI1bNNjFagw34/IufNZ+DQB0NIjMUS0zvleIH12qCiwa9JcYZc1+J7kvbemSziAi/Sw9P3lduse2Om49NaYA/ddvD9+XQQw+1bkiQCdzEcw0aglyDxiDWA6Ojo2bbtm32QGuQMgAES5EV3HWX67MTCORd45Q1qZN7rGU0XZLyZgoAlmtU03JTDqQcSDlQexyY0QCg04QxismrgmJRzIVrbyijtUj7pn2Nlit+KhW2VKiIX0Jt5NB+0BoFiJ0tqxfgD6ER4A8hWcFLgDRMfWs5gh4gH8I7wB9AIIsD5i6BetBgRLjniKLh6xw3rgkCkiRZP4CAJyzOhCz4J8AJmn8NOadxJ518uDntgtXm41+8zbdq/Df1zGmX/k6ZZfsmDnlQiglw16BELxyMv7r3iwLcun1MtLVAMkIaHecxrOaIskKXcSkt8EeuHlbkXiRjndVGlOc+Sbyyca9xKKL2nxbAHPNrh6YJOkvk0Kb9Y2ZsLuOR1YUDdBvz0f6z4Biahz7UJpGh8d2WBFnwD60+6lNNSlfB+AUc6i0E/5gGZjpOUJCT3762nOmv84EFAJ039FqagL9Ino+JK1RMXQHySyXKKPAXSPCQOWIu6gOexwkE4ts6y6BSW57LL3wk6ElZwT9tKvNcjnbxQTrULb9JogEo0Mi0cbAmvgIWssniywMtM+ws/YsU6TesHJ4TqGOkuC9HW0eLaL51mo3rH5OQt1kLFbGJNWZAgD/ZRGqQ78iFrznDvPYdz43SkppMg4nvQw89ZN/vKpsA7BHdV+XVsIazaX/00UdbeYZNQ7QC8RHIxuHGjRvNfffdZwOLISsk6dM4rF3FPtdNZORP3XjmrPzRe8jXpCWdU1aNW6+z7FLKcder7eU+snJKKQdSDqQcSDlQGxyY0QAgGoAIEpgUAHwEEQCDgmIICTOVVAMwNQGOP8JOMLNegD92w9WXnraf7wXAH+BaPQhltBHtRA6+o/SHHX76BqhJhED6glmQzu8oo5u0BmBewwZshoVoDvyzIJwsKM9+zirz1ndfYEbFX9OX//l2s2uPSw0p1+hjj1xs7tkwpaEZpS9+aYo1AV7T22Meu/Mpv2J97xNEwct/XJMERZhs5KFv1qIexDH/bT4gY1JULdlMNH2alpiWp0BVkYscq/2XA+K0yMBzABgXmM/5EBDwoICyAgJOyiJyvEMWaF7lytwdneO/eGveL37/BAxKgiz4h+Yf7QBQ9cCfSTNA0A8Xr6No/2GK6wWK+2nKtogJauuBbM8yogk70iUmeGLyqoB+En227xKxN24kKjVAoGhhjoP1OMZiMgTYzLcjYBisqXM+YWkXFvyL4RagtNqyuSebGwW8lTFoF39pIrk2MTdkjjSKpl8G0I+JkQTJfI8V6TesTgEAsyGi/L9DXkUsXNIpKq6j5v5N24yZJ+gzWuNo/I1mvxQLJLIvEX4PlUi/9UiAQ7zHt2zZYrXa6IO6ASnWzQdgGLIAB/6NAQJxIQLIiEsRDtYEyPjF1lFpXqu2n4J+TrCOtiDXOYFATR+nnSobkidJAFDbUA9yprY1Pc8yDljBrs76HPCer7OepM2tIgdmNAAIX1evXm1++ctfWm0hhAD8Z3jRvffem7+9atWq/PVMu1CARMHOcvVPhQinYFGuuspRrgpbzh1MtM6INodGGhpnCFq1SvAd4ZeddXWiTZv7+/ttH2q57UE8Zf7yncZkmf5xAARi+sOBUA8QiL/AMKGzO8EowLbNLEAtaCELSNbyoj2X9Y01aS5+0QnmyjedbZM1N2XMC8491tz6L7+b1tWVRywyGwT8i7dUnFZM/oYfsJFP4HGxdtki89Bvi2wD4I2bhCfNAqIQDTVJEIJqoi76MQV0m326mxn+WQbVAcrY9HKrWJNfrc9q/8UB/yTjpIBfifBS5mJXc5MZFcBpB+a/XoSw6WUWLLcJctG6OxnkhWommD/UxfdIACavX9ihhXLX1R7yhmn/USZBPLzIax6h7de6byp1RnxXtu4SAKpTwDp8WQoOE/YbM5U7/Mpq0wkQiKkv/RlvEzBQvJigUYsGIPd8RihfeOCcsEzKJy36IjMYzydo0RV5ZGRzAf9/aOc1CfBsv3swxQUGe2SNdgvwL2ak30gFi4aWEV+CUekI8fe3bcuTZmBAfBbMF/BP/PyZoay5L79BF1z+Z+bKd12Q6PyL2rYk0qGdh8wNSAchL+L7F9ceSckmuDZZs2aN1STE5QmyAqbCzqAhAIFLly4tyoIgCT7EKcMJ7CGfIuNxVlKZ1W4o5DQCo/LSKaer7K7lpueUAykHUg6kHJh5HPBGw2ZQP08//XQLAAJ43XHHHebUU0/17N0vfvGL/P1nPetZ+euZdqEmFeXWAFTBwymg1AMvVYjiDPhHP9hNZqca4ZHdajTQEBw5vHzTVLOftBsgDM049XsJ6N3Xl/WpN1OEO8BMtBjpF2PDzj7fcUyEOTD/BwgkgrDORfe4tIgGVOf8drN3nzjlSoDsOhTNP1loNwr4p+Zxl736z8xLLzutoIaLzl5j/umHf5R13ZSK0yIBJB++f3voIr+goJAPFtiQeRx1gbzikB7z6O+fjN2GtpYms2JZt7l353Sz5TaJIDypGy8JgRC221Nrn2AuSP8BqQQyC04X8jRnxT2VCpBKLPssCFRC0dZkMeZmgtWyFFAzCdq/X/wBMj7aBtd8GXME2SioT/rfTnTogpvFfxhvyUUflvp9wT8B37yiTIdp/6GB1SJYg98wuQFAtPxaPUyFG2UStO4WjUiCjwhYiVZgIiCgaJA6G0c7m4ayB1qnAIGc3e2cxu2AKdHAuDormZY5/EZmSIC3cHfK4QWVkALglfFpIRqukO2ygGKTgPMCENqz/QzDJEGD36jb7FN/ZD4XFel3qgT/qxYBAAU4jUKrj19uNt3+gIA70kdxUzEi7yfMfaHOxfPNBz7/atO/4pAoRdVcGgLz4bqDd7YSABwbehqET+8ndUZG06AhgH8AgcgItAV5jkjDyAnIcwQNqQdCpuFwy6vadjZ9AfX4bYoSNKScAKD+PupZ25ieUw6kHEg5kHKgehyY8QDgJZdcYq6//nrL4VtvvdUTAOQl+rWvfc2mIQLq2WefXb0RKXPNldYABESDv34gTJm7G7l4tyCFsKIHvmgQIBEcEVwB1hAa0a4jMh3Pqh04Bj5j6kK7ACoheM6Oel9fX80BlZEHJiQhgCZamYwDQj1AIJEB8f2zYcMGK+DDA557gbWYAScFAFqey2oUDZImAQKhN1x9tvnzi4+3184/8+e2mfPOWGH+/f/eY283S6TI0YNDos3oWiRmi3FmjXVt179S5KSsP8Po8N5Os2vD02YihqbK/Dmt5gjJ9/C9T5lN2x4z+w/FVlFUlnLUdFCA9IZMHnYI1FDSTBHPFnyLkLZlj2gsEdK1BLKawE4elmjyq01pHBL/kAq86c0oZ4AOghzI71SpBLA5JP7nLMnvSPP+cQG4pLMCoqBpNWE1A6fXY/3+TeHXJTVjTKYNGqL4z/QD/0YlNpdXIBL7FQkxkQ0L/uIE1ojW27bbHyqD50QEHm/JmkZbk+ASx4Fow9O0S3McpW1RtVcDv18l/pbAF0yoq0lo/DUfFGBD5r+SvRKwDB+sxjUfbZcVHPQ458HBUiL9akMCzh3d7WZwa7DNdLOAhEccvchs/M39ZsGiedbL4b6d+7O/nTK/znv5qeYN778oke98QFPL8giAiXczMpOCTYBtK1asqBjoxvcWCwEOv6AhaA0CBAII1sNmqRMIZODgLbIsxDuLPkcJGqJjQr566DftTCnlQMqBlAMpB4rnwIwHAE855RRzxhlnWC3AL3/5y+bVr361eeYzn1nAsZtuuskGFeDmW97ylmlAwc9//vM8KEj+f/zHfyzIX08fnACgCgjlaL9TiEC4qFUA0A/4gye0GdCIMxpnmJ6idQYQiE9JTE+55kBg7OvrMwiQlSTGcMeOHRb400A3CH0AXpjUlGtXvZJ9jFIXfV64cKE9AP/Q0gQQJXALu/xoRMITwEAnWNsjWncPPLQjShWR0hAxMiMBHXDO/nYx0TrjnJW++S589grzIwEAWaQulIisTz2Z1WhxZ5ha6rqfRPsMUBYGAC7qnGPGHjpohiMG/VjYNccsFb9U99+zzWx8dMpO0kaR1WbJgrxJfKgZATfzZFfk+U8lXdgFv4x7EKFdZ0GL4GRBRdhn1vdfri4bWZR+hNQdWqgkwITWD/gJzE/d8t1Pog0j88VHGYCKlNe8F40+MXEVIAXTU8xg20R7ckJs2fFNN9wpWiftjeI7cMK0iBZWEjQmUgj+3SC0u9CgdRNphhdIGg+ehwbIkH617HWXWPhZTeUbRaOvfZdUU/h42ieeN40IQCqA1Mh80bIRH4hFa5hKGaEVTmuBzw0P3mnKQHBQE/mc0XSMGw3cp6jib8vvCeC07Yf8vhIIxsufo7MCO46Sz/5WOB/ItWUV88k52M7PHnPNVUTkjwdFy3ZuV6sZ2OONoHb1zDFz2jLm/rseMatP6Teb//SYGR/NBg6Zv3C+ef/fX2GOXL00cn21klDlE4Jw6MYkMgkBO3Clwnu7GqRBQ5CRCBqCPIdZMibJ99xzTz5oCJuL9RQ0BFnVLdPCX72nsjjyObxXudwJAOq9aoxLWmfKgUpzgPdJKe/GSreX+uqtvdXgUVpnOAdmPAAIC2655RaDWS8CyPnnn2/e+973WkCPz9/+9rfNF77wBcspNL3e/va3h3PNJ4UbGLzrrrvyKX/yk5/YYAV6A5MHzJMrTWoCjEAAOEJAiHKQU4igrloj2oRwiuCjQKgKo7QdswkniKntR3hl7Pr7+62pLbvazCOESA58z6ERWAkH02i6YU6D4KqEuTJCbT0IrdrmpM+Y/x577LF5P4EI904n4IC1jBEaCEkGArF+qQYFAMw0mg9c9wLzDFnIeRE78mhCABwfuXyOmCKNm21bvQOCeOWPew+QonCVW1jC/I5WM3fnhNmZM6krfFr4afmi+aarsdk6pt80IUiJi5zmom1Spo2A6Uhjl3vBzXGk9r/EFFFWL/4Jck/a9mW1IEITBiTg98FGLgZISMDkV6vCTLwo7T8tIIEzwCYAINS8T8C/3Ng0inkxoLEFKOUZQEuLgIEton1FBFZLsJ8BFTCmWBqXOvKRb2WeegE6vD2s3z8PsIDmhgXIaBJtvbCZgpYdfv06xII9jnBt/QKKSTB+ATOAuaJOGZcbQdp/xfLVMx/MKoIARZtE+6+qBDh9IAf+5RoyIW4H0KAtlrK/RYyZs4SpD/bKJpI/nJ3XZPGYj86S3NcLD11gHtnzpPu2WXJYlzmwY79pFeuTBWLiu/EPD2fTSPlnv+Qk8+YPvqBqQNm0xsa4wYbk5s2brUY+2ZCreO/29fX5+uKOUXwiSZHxMEHmcAYN0eBpbCQiywEEVkKmS6JT8JlDQT+VdbVsZF4nEEhaBQAVGNS06TnlQMqBlAMpB2YmB2YFAHjCCSeY73znO+byyy+3L3kAQDcB/v34xz+2vsPcz6J+fu1rX+ub9GMf+1jBMzQJqwEAqgYgjcFnWrkAQCd4psJFAQOq+EEFoDjAn7u59A9tMgRDNPAefvhhO7eIJs0BnxF2AeQQsJIkAD+APwBAJbTfACYV4NX7s/nM3EbToL+/3+AE3A3WYu7fJsEPkqCGMdE0GxgzBPj425suNSs8tDWYdywoODQwy5nPWGy+/m8PJtEEzzJYs/aKE/mnRr21C9uam8zSkVbzxJNTc2laQbL4PmpZj2gAjZsHN20326clmLox3pad65kBAditLtnUs/wVK2u7mM7fiX0B2DIesndhtYUkcEPJRBEARCBRMRf9QXVbcM1hyhiU1vNZAl2z4J8AeE2Af47+oeWYEUDOq4r8PdLbsRSwkJsMfQwwEA0u688PngKuemA5FD+4WBL6lBuq/Sf9aI7g4hMAsG2nDK9FfaTSGITGZKtEpx2dI+yQKL6NYumpm0mhxSSp/SeVBbU/6Fm+nfJdbxEt0JGurGjIHK0N8E/ciORwZ23rZIvMC++fNU1S0tnOczsf5E9+XmQv8h+ZuyS0iXPX1OrxO9HSIcE8XHTIYXPN/p27TUdnm3lw4xP5p3PFNcX7PnuFOWbt8vy9erkAPMMVCRtvdvNEGr5o0SL7Lq7ljUkNGoLMgLxA+9ncVb/CaA0i79Vr0BCAQJXF+X1SgJBr9RftlNuTnm+RfxOTrjgtL+VAyoGUAykHpnFgVgCA9Pr5z3++Wb9+vdUGBOjj5Y5ZJ6DJpZdeaq6++upZoTXlBgB7enqmTYokbjgFCRU6kii3lDKSAP7c9QPuoVGGgAvwB7iDwAi4unHjRgvUKVDo5YPOXV7QZ3bUEay3b5+CYLq7u+0crhfn1UH9K9cztDkZA3z7wDvGCBB1z5495uD+Ke3JouuXRTzgX2trk7nps680hx3eW1AUgrY7MAtzAU1NFhQDw7eb70pAkCSJ9egz1h1q/upNzzHf/d8N5hu3TWkjaz0ZAVWOaZtvHt44NZ/0GWfxMmdWHrbQDO8aNI/ePeW03ZnGfT1OwAgBc1pE827SafrrSAgQkV9AO+5HvpTyx3Mmo755ZEwwX/VaiPvm8Xig7XSCYx7JYt+y2n+iKVoSeYAMccoDgBuZl7HgH2Cfk1e2v2i0hZEFPiQrSQHw5MKCgdynez5txCUjQTTsc8A/Ac28uDHULbMQ34AeRJVB2n+AD20Rv97UkDUT9a7Lo/qCWyxu8dOHX0C0YKMGB0lc+y9oyIKe0RvhV+tu8V86kAUALfhnTZsLulrxD5ibo5Hqpgmf3xd3unJ8zs8S4dnUj1m2jfmW2kmVSynz49GHd7KPkMXJBfg/evViM3TgoNm+b9Qc2J2NrIJvw2PP6DfXfOSF1pqgHG0vV5n6nkNGAQSE2JBkc71ccmY5+sK6oK+vz27gOoOGAAZq0BDMl3l314vchZyqhwJ/Cs7CQwKiQKRBTub3jOtSyVlHqWWl+VMOlIUD/GDnf7TLUkPyhdZbe5PnQFpiAhyYNQAgvEIj6+abb7ZHHN6dddZZ+Z3MoHz18LJzAoDljATsFB6qDQD6AX8IOYBDKhgFjW3YM8oCjOMAqANk0oAhaOtpwBC3D7qwcnmO4IkPO3allditBryuJ8Fa216tM2MEWMsB+McYPfK42AeWQgJeNA+MmjntLebTX3y1WSjO25X4PcAsnPFnDCHVHO2TBQZzD7ripaeYOzc8ZrY8WLovQumiOWndYQL8nWMWiJ8+qHueRE/woOO7F5otd07NKU3SJMDgqkMXmt1b95oH7tyqt0PPGsW1bcfYVNRfr1wlCi8EXbDaP15l5+4BZDQ0lLiAkfGzwBbaZ/I/ScLc1E+rLWo9RDt1Bq+Imk/TjXQK+CfgSoa2eGgiMpdiCcY5HlmeMcZobAEIMgw8g49CFvwDKKYCeOwD/g0TadcvAjHlhEgvTfKVs0CmrTX8j213tonhiT1SkNX6BRSwanSegIBiNstvjh+hZZnovIKXfpVxnzHxI8lLpOzWp0fNqJiEMz+zfg39MlTmfkbASDs/PaqbLBVA9ygziVv5MYDfwtcsTZqhfQKytDdnb0nb77tfAi4RBb5JItiIb4HFy+aYZ77wSLOkv9PccccdFjxj0worAueGahJtTLoMrBEw91U/xLzbkE/wu+uUBZOut5zl8d3F7JcDWRmlAWQwwE3OHPUaNATZhEPdkdA3iHHjHn1nznHU6/iVc26kZaccSDmQcqDeORAiQtd799L2uzngBABVWHOnSeKzChCAb+w4VoOoV+unPRyQtq1cwg273mvWrLECMGanCFfqg04DhgBGhwUMwUcjwCH5FVxm/BCsMfnV/lSDt/VeJ+a/HO0dC82//jgr/Mbuk6zt0PzrFIDtc19+jZnXOQW0oTmAtgABSSDGCo2B/v7+aYFZMBt+19Xnmze/5zvil9PDDjJCw5jaJ59wuHnbG58jWglT7SBrjwTrcNNJSw8x991e2O828am1Ylm3eeKBp83mPz7mzhL6GbAmM4hvTRyMu5KjJSl++5rk+ci8RjPcnfU750oV6SOaWrqs9sqQGZYAFdIGjJCLJl2450CrosvxyNgocyYJ3382cIfwohiawPeeAHNoqnmBf5TJcw2OUUwdikZZkJJmCsJmwb+5FCwTRHjsB/6NiWn+6PxcOo/KKS5I+69BACwi9cYi28ZYOTwTE3G6QTRgR+eKFo20Qz55prO8TXJ+0f4AsgCn13OZQ21o/ol/x4yAtsMCBtvIxl5pK3gPP592fvrVKdNDNer8ktTafTsXZDqM2++tXLQ0mwVi7nvZm55tznzuGms9gIyANQHy2aZNm+x7BLNTwMBaM6FlY4sAH06rBNqJdjuadDOF4DuajASBY1OPMcJnoDNoCGAn73hnkLFa7j9jxtghZ0K0m/6prInMivwMAIisjAxTDBiYyqm1PAvStqUcSDkwWzmQAoCzbOQxPUQwY5evnBqAsBVhQbXvKslmBf5UkFEBhHMldzU12h2gD7vFaJxhakF0Wg60BQEC0eLTNsIndUANeAj/IPzZIZyhDeBMax+mf4rmwLJlhea6cQpqOjhqFsvi7TNferUIz9nFDgsCgD+nf0Y0DgFtgxZvy5d2mTe9+gzzqS/cNtWEkAU9CcFRTn1Gn3mraPwBRHpRtwsAPGH5YrP5N4/nYYnOOa2mr7fTPHLvU2bTtvjAn9aJ6WPLXgHnxDQvGxAlC/gB/GXEwk1hkAlReBm2UTU0Z/QzWlPjrXhd86eju7rNY3sCfBr6Z80+UfAP5paBMmh+JQH8SBmTkxNF/R6MzhHtDtZ9Hpp/+S4LwDIpPz+JcEEKYTqPd0ihub43iImx10himjzUI38C+B+o/Sfj1yy4eyLtzjMj3kWj2EFjBm/9AgJ0ymdnewAolQ/xSvZP7QvwaRYGgLnt5KsAUe27ZJBlTjaL306oAZS2ykT0bjYTAlsi/bCawJK2HqlzQYd52ZWnm3MvXieaV9kNEdVQZ+MIkAkrAt08RB5AGw2AzS0zVLr/yCVsTiLT6AYvAdBWrFhRkh/tSvcjbn3Ijxo0BDcijBGAIDIbfqA5amWM/PoGsHzvvfdakJk09Km/v9/OK2RLxlZlZ56rDI88r2BgCgTCmZRSDqQcSDlQvxxIAcD6HbuiW44WGQAgfurKSQgWCEYKYpWzLsp2A39aX6WBP61Xz5hVqB9AhEWEZgR8QCIONAYBAtHqQ9sPIVKDRADWIpyxs1yM0KVtSM/eHJjT0SLgXbOY6GZ9FrlTLexsMjv2TtfKy4jZ7+GiLffJz19umpszFkzH1JfxVWKRBvAXpump6Z979irzh7seNb/63QN6y3cBzBr+mScdYd76hrNlwRUcDcNpArxm6ULzwG+32nIXLphjlszpMA9s3GY2PbovX2exFwT+aJfFQ9PQqNUk8ivHmr/6PQy5jwbhRKu/9uAxC7vMYxuKBP/KDPzRtVX9i8z9Ef0phrAiC+QEIiTeJYwLVk1ABT/fevlcFigCNSqdKGVMwD+ts0FAG0/wT9INLAoG/yyOJSCyH2XE9BdNtthUBC+D6uC90yJ+AcfEL+B4s4Cd8jNiN28ABMuhuhYyVNO6B/j3NBNBFNH2OH7jfHwuBvU1yWeAo5imT2uvRyVEAs6ILFNP1Cbvm4tfcYp5gRztsvniRUSyX716tQ2cweYhQBPadhqQgs0kNQ8u1bewV/1+9wCG2Lxkk0s1x9icRDsOP8h2fvtlnmH38f/HQd91jNjg1THSoCFoBlZyjPzYjCyOKxnmkgJ8AM603xkMELkdWVoPTaufkedJwxFVJp1N88KP/+n92uQAG2ehm2c11vR6a2+NsS9tTo4DKQA4C6cCgBMmJuXWAERAgMoNAAYBfwgoAHBRBZVyTgfagAYfDqThP0AfpqLsyN5zzz0FVdPmvr4+CxwqHwsSpB8S40Bv91zz2Nbd08o7fFmneXL9NrGhLTShzQyOmVVHLDI3fuaVFkjftOk+G+RDBWUAPyIJouEZhxCS3/J/zjL33v+U2bnrgGfWRknzZ6f0C/B3jkSa9l48ujOqBuCRixaYbX/cbpYvnG+6GpvN/Zu2mb0TRYJl7krkczMaZRINOYwy3lhrWLbcc39YoEW02Z5+YE/EclzJKgD+UeOBpwURSpAmZT7EEQbH5Sd5oikj0WojNkKwOGtnGTG5VzJwqfF20dZqzo4dAA8acV40tFAqDNJKlEyB2n+Ymhe7r+XdJK9mxrqnfgHH5GcE8DvxwB9xWsNg0E/hE+AfQGSTBKFgmCEeW5+N9lPl/zRIu5rFFDnqUOicqnxL49fYKPP6vIuPNy993bOs2W+UEgCO2BxkAxFgCfAGmQHZDZ97bDohUwAGIteVk9Bup07850LIM/39/bZ9s1lGcQYNcY4RgK0zaAhjFHUzMMlxRC5Bk5S2sPEPMVfQ1vSTURhbDgX9OKt8Q342qDlIo0Ag1ymlHEg5kHIg5UB9cCAFAOtjnBJtpfoBLLcGoAoECA/lIKdw4iwfIIW6awX4c7aNa9qH4IXJDGY0AIFOkJTngIT14Pzb3bd6/NwrZrxuAHDp4k6zd8NTZsJlqtk0Mm76l3WY516y2Nx+++1Wi1bnN1oZaPyVogkxb26beeebzzXv/sj3rbWe8hPg7/TTjjTXvP6syMCf5p0n5smHi2Zcj2CcPfPnmgc3bTfecX81RxFnALSIQTe8InpGqZGopEHmv0fMnWcee6IIALBC4N/qIxabLeunB12J0nffNMxPAU2iEKa1ky0C/vHWl/kUhcDpoqX0L41ovxMtucWhtBWQh984Nw11kW76fWc6C1D5af/JOLaI6W+xy1Db12isdDYp0jV+ARsPiNN9icsD6GY1ACPljJ4oChBs0wj/LfiHRuLomMk4Xs9WQ9NjbKK3ooSUopHYvF/AvxhjMFGjgUDcXDj1zGPM5W880yw7vMf9KNJnvi9YCHAgt2EpgNYZIAzXHMgUgEyYoKrsFanwkESARoBH1KeEfMIml1NzTJ/N1rNzjDRoyNatW+0YwTsONAYZIzTvkhwjP54D2mLui7kyhEyMK5moFiW0UduJnIOcqvIO5akMTt8BAhUM5FlKKQdSDqQcSDlQuxxIAcDaHZuytaxSAKDuCjvBrSQ6pUIHZ3YldTHJGWGlVoE/7Ttt3rFjh3nggQfyUfNoO7uyCI7wC4Ee4RFBGw0ATIJSKg8Henuy0XK19AUSRGP80T1m+OCImZw/pWWHoe26YxebM56z0Aq+GuCDOYdQj2Ctc17LKua8bs0yc+nFzzA/+undZlQAR4C/qwH+cn4G45bJ3OrfnTEb/vBo3KzT0ytY5nqCJlpUpKhYE2CCAky0eauuHdrTaR7fFBP8077Q9jITVezb7q3VWVLVEZuOW7cJAf/wvxgV/LPtAjS0pqvFtZLI0BNtOUhOAB4bFMOD3yOiHTfWEd6ZIO2/RlFuKUm7lOpjgE9xOSK6mqYFEFCCg+BbsaS2elUeoe340GzbI+bXUv+EoJAtg4WZJqpl/lsE+GdZIFp19CB85ngxrPz3Vhy7zLzq6rPNqrXLE6sM+Q3tLd43aHahFQgoqC5FAOUAeDA9LSUQB/IVfgfZpFSXJMghK1eutAG0EuvQDCzIGTQEk2nGCHkBII4DTcpyBg0BtEW3xfcqAABAAElEQVQ7FBlSCd+FgLbFzgkFA93yt5bPHEF2Rd5ABufslM81XXpOOZByIOVAyoHqcyAFAKs/BhVvgZqK1JsJsFvwQMDQox6APwYaIR3BTHdkuYemH8I8PmN0Rx/BGx87CPgc+JMDCGSXnz6nlBwHesQEWKmtrdl0Do6bp3aIKpGQjbQq53bRNHn2SYeaE0/rsoFc7MPcH+Ylfh0x62aM0ABkPpZCr7r0FAkQmTEvvvD4fICRUsrr7SoEOUPLUnDMK6HX/GNKet33yG+jn1J+xPRaxKRLG1Pvc54z0GB2BbXZmZhrTRuzDe5ion5G++++P01p0ETNF5bOaZbll9aCf+I3MTb4R4HwJ45KlqMR42LyS2RoS8JvP/BvTEDJkS75voSMBUCPb+RfCyA5Ki/i0scquYiS/LPw290qJsqjspswJkdmSLrtnzzWk9BhEh617ZZxEI0/TMGJ/Osm/b1z3y/rZ5kbzeLzj3bFJuHnpPw2N4wXkzl2bZEzLD2s21z+pjPNqc8+RqZ1UiNcWD0gCxtPgH28ewCZiOyKHzpkDPy9oWmG+XBc01NMWQGpVEYENEK7HRCpXP0p7N3M+MSGIEAffEMbjzECEHQGDUGrk3FMQrbjfcDmMeOvoC1jD2iL9mES5AQCqQ/Qz/ke4hoAknTMFa51zZFE/WkZKQcS5YAVLBItsfyF0eaUUg6UyIEUACyRgfWYvVIagAqCAJCUQn7AH2VSB35ytK5S6ilnXgA/hDIAQCWAIoA/p3CEUN/Xl/X9R0AJzIPxEYjfHw524AGZKmVCom2dyWd8AEKNAjD1dbSZR9ZP7ZqzuGwRIfaZx/eYNce32cUVcw2BnXFgXAH/8IuEgH/33XdbsygWXQj+jGcx1NSUMZe/5JRisnrmWblmqfn5f2/yfBZ4U/oeiaKmk8IosVH8AE5IMIqoRERQG/HTI8M6iWp8/x3bPJ743JIFiqUYbfYpKdJtzLd3P5kFlCNliJGoUeaJrMB8cyj4Z81+AwBU3wJ4AJYd8yd8gmaJ3z8F9TDfRgPOTTbib284+Ee+IO0//P41lioUS/NYvFYC4GgW4G9cfhrGJHB3kwQtmc4ZN6cifA7qv4B/mWHGQPgo7G7Z6+2IsxoagAT8KNYtAFyZkKjgjbkIxhG4VNYkXd1zbGTf5zx/rfz2e2srJ90A5ivgEQd+59R6AJBJNxCjmp6iSXjfffdZf4O0k7J5lx1xxBFFv8uS7m89lgcfGQMOgm6gmcc4AdZiDcKB1iBgLmBhMUFDkEEw91XLBMoAtEUOKcdvmsrcgJxuGZ0xYi5+9rOfNV/60pfMr3/9aysvaZ56HMO0zSkHUg6kHJhJHChudTqTODAL+1IpAFDNIYs1AUaocO4wIsSoIIMgAbiiddTqMALeYerLzrwSgjqCWdCOLP3TgCGAhgCBnBHuNmzYYMHEUkEmbc9sP/fkTIDXSFTf+3/zUAE7mptF8++UbnPc8dmAHgjnLIbQ1oQAcTkUCFQNDBZRaGAgfDNO1faVtEoAwHJS3MABLPjD/L052wuANJFlufO2mdvWYrZtngLVCx66Pyjwx335LakUETCmHNp/tB+Ar120+0aGp4OAYHZETCbgRymaXYxtHJ91AExjc6ZAPSL+eoF/4FWDBP2IAEyS1k/7j6AiAGolE3OCOVKhqZHBtBptQFHObZIAOnF4PK2vAJfTbuZuOMA/+Ng4JM77fQBdjdLsV1TS9zMDElFUxq8UmhBNaZlxpRRRcl4i+77glafa6L7tElm+WsR7CTNP3lFsIKJxxsYU7ycO3ku8kwCanO8kwEJMfbE8UG0ufAkCVqm8WK0+zbR60abs7+83fX190wK7MD5sFGtglyiuX7AUwUcjYK+SuiQpBkjUMuKckVc5FAj82c9+Zt71rndZ2ZdyPvGJT5ibb77Zmh+rDB+n/DRtyoGUAykHUg4ky4EUAEyWn3VRmgp0at5RrkYrOFcMAEgeNS2oR+CP3U+AP6dQhikGwB/mvFGJvpOeA/APbTNMSNg5VpAJYR6BzynQRy0/TWcMGoDrjhQTzV9sMXPnt5sFvXNMq/gjaxETveWru8yRR3dZ5+uMnVNb08k7wNy1a9faXW8WUer8m/HiMxqbaAzGNcVy1lHK9WF9vTZ4yMGDhOqNQBYI8YUUphcg8zQOxfIDKG2ZECDWi47pWmA2PzK18PFKY+/RHyhmO7OZiv+LVumubeXR/tNW9S6ab554rDCKtQX/xF8i4F3JoI4MbdTpQH0F4F9QxN8eMd+M6HPOV/tPGtayTzlRf2e0FpvFNaSNECwYFr75iiI/DM0B/lHupFTYMuSXWADjkAjMRbXNJxM+PZtEK7FUqoTptl8bNbLvy6483aD9VyuE7MVmFUCS0/QUc0wNPMbGFbIDciCyCs8gNNHwMQgAmFL5OIBs5xfYBfmBA7kC2c7L4gOwDdmCjUaVsbu6uqy5bxTgMOmeARwj7wD8/fjHP7bFA0Bec8015n3ve59pbW1Nusq0vJQDKQdSDqQcKJIDKQBYJOPqOZuCGGinlZOKAQDDgD+EJnYaa5XYjUXAxrxDd9IBXAGPEPZof7GEUHfsscfashC0FGRCO5DPCPuATDq+xdYz2/IdtnyBuebN55g573meeXzrowXRDhGo0ajgHIXQwGDxhAYG44MGBmAtoC0HkZ8ZIxZXpcyFKG1xpgGIWrF6ifnj7x923g6+jor6UErMr2Qckz9MBIle66Z+iWy8eX0E8E8zlvDd0yLinlf3Lzab/zRlUh43f5T0c+YRnmaKsuCfDAjAXRJvePgW6mBO6pJko2j+qUZfQMTf4fliAqvBQaaa7nkFROSn/dc0INX5aLN5FlaDN/nqtEg/xkRxDLNgNAMTIeF/xppeZ0vDLBtNWj+yQFqFAMBG0QrFrD8JmmyK+eOTRKVSxmlnHWMue0PxkX0TakZgMbxjAJE4eI/xTkI2QU5BQ5BDCXkNlyQATrUsY2l7Z9IZGRG5ATmRTWPGyBk0xK25iTsYfDRisg2hVYi2JkHjKilX6BgAIn/qU58yN910k5V3uH/eeeeZT37yk2bVqlWaLD2nHKhJDkQQb2qy3WmjUg6UwoEklgel1J/mrQIHKqUBqEIkO5Vh5Af8Icxg6ktZWl5YWdV4jgkNIByH9heNPARqgLkkhTLKdYJM7AIj0D/xxBP2AFwCZAJsSrLeavC1EnU2yNv/wNDTZtOWx/Jjx3eEBVOxQB073319WV+OmAUzL9DEwFk7B1oWjBFzQ4HycvcVM+BIAKAAf4LlRA6IaiEF+Z7GIXwARqWFXXPN9gFxlOYgqmveK78r/niGI7Vcki5eEwvzF/EpI2DKzq0xIxMXUU+zAxzll9ZG3RUGFRX0w6f+MDNg2Gs1/xT8E80zv6AfBMAYnRsdtPHT/msA4BLgLFGq8Bxxtr1JFLAIzjEuQCARjeM0xb2AgTdZv4vZGgD3JiQoS0MAuFgp/38No6L5dyAbLdTZ/6KvBQCclH9eZuZFlxmQcaVE9H3Vm88yK49bHpCq9h6hgcXGFCDRPffcY/3WuluJHMGGFe+nlCrPAWQBtDIx08Zkmw1EQFrV3GRzGbBPtTWR76rpo5FN7v/4j/+wWn+0DaI9mPy++MUvrmmZvfKjm9aYciDlQMqB2uFACgDWzlhUrCWVAgAV2FDzBK8OApbxXLXlFLDiTH6OWgb+aDsAHFp4GnUNAQ1BGyGunG13gkxolwEyodVJBD8OzE0BmTD1KWc7vMa1Hu55jV3SoC18Z8GFCQ9OuhkjHH6zY75p0ybr7weNCw7mTTmJQCBxCADCfi9B24IoOpaTLyWqBmCLfP93j0w3W163bLHZEjHwR+PAmIBi0zUI840p0wXaf/feFa79NyLBMvDRB2jTIMAZvuAAdKxPOLkOY++45IHy4J9AIUmCf7ZwGkEFHqTgX97PoCwKLfjkMW8A84a7pTCPZx5FW9zWU/tP6mgW098w3niVWcv3MmIC3CD+DG2E4DggYHYK2K5NA//k7niLfIfheQDfo5pjl8K/BvH92bp7zM5vzJHtnAloU9S6JgQEzEjZ5aRlEtn3iqvOMiefcXRdbqzxvuP9A1Cjm5RYFfDOQ6NMn5OGjS+AnCQi05ZzTGZq2ci/WB1wsNkLEIiciYyp4B+yBWPU19dX8QAtyAWYHr/zne80P/3pT+0wADC//e1vN+9+97ttsLqZOjZpv1IOpBxIOTATOJACgDNhFGP2QU1E1XwgZvbIyYMAQDfwp4XWC/BH+zGnQQhSgcwZwVf7rv0q5xlBUP39IMgjwBMwRKPSYpaKoAggWcl2lbPPpZTN2KEtydih8QABpvb399vd93LwiHmNRiYH3zuEedqA5ijtAEBGG5Bx0u9nKX30ynvMyiUCBAs4lAOMvNLYeyLcxyLpW1yK6gNw1bKFZsP9TxYUP6+t2Ty8Ycp0reCh60PT/lHRehIfghIMo5LUlGk0T7n88nnVj2bWuCNghk0D/+W/BQDtOQsMTkpiON2g4wNQKJ8f2rnHjLSLFhS4mqSxpp5FjIlX+/L3pCKqdRcrt8R/ndTt8OXnC/5J2gGCfrgLyVcy/cJP+69RvraJmco6qmU84Gk1yfoFFIXX8VZpBYAgTA4hTcNct5qXjvRo/qlZNv3zo7L7/5Pfndbdo/lozczvSblnx1g1R/0aF3LfmgGPFetAMbjwLgkS9fIrzzDPuWityVTJ3Di4hcFPAWvQQseMFO0+CLAGk1E2png38R5S82D8F+smIpqAbE4hXxQb0T64denTIA4wdmwcYhasG8zIe8gwHMgNyBLIDoxTJXz/Ib9g6ovJr8pPF1xwgQ3yAVjJfEop5UDdcADBhqOeqN7aW0+8nUVtTQHAWTTY2lXVACw3AIigAiGoKHHNTrPVLNKbcq4X4I92I4wB2iAoQ/QTTTsOgKRqETxk554D8A/hEMGfduIvBkffqm02Gx0y60KIKHsaAKcaY8f3D784mIfj64fdfUBkFmAcjB9zKWkTbiJV9h+1yDxwXzTwzM5jBI0QgT4IVPD7LkTVABwanG4r3Cuhb7cPT/2meNYhvzMt+8ZN45gslELa75m/xJur+hdF0v4ba5PFkrt9fJb/AHoQxo15Yjyk/1kNQQRXY/bivL8jY1PJR0t+UIjoGto8nCxxzl3nqss9cJ1oE872HINNtnFp/yQAU478Iv7yeHCR1BAD6LFN8/o5FdCouVxxVehKjh+5LlXlRDOIDIxPQKIqh/o5lDZ7gn+CezvB2SD+F6RLutcW/JsefZh+ZiSC9SQbE83x5oeziZPkHfKb9c6U0a/bJJrvJZdJZN+Xn2za2surnR29VfFS4keOdz9uJyDed319ffZwbnSpNQHvHcA/3klsJvKeJL8zMm25Nqji9Wzmp0Y+h/eMA+QcO4BcZAc2EQEGVXZAYxAZrxxWH8ju//7v/241/AAdof7+fgsGXnLJJVaGtzfTPykHUg6kHEg5UPMcSAHAmh+i5BuoAKCCIMnXkC1RBUwAvyDgD8FG/fyVqy2llgt4hOkmgrACpwBu+GtBCKo1QA3zX6+otJj/oCHIjjHCvs6FUvlT6/nRiNyyZYsFRmkrY4dGJKba1Ro7NRVnHNSEm7ml2hfs5qMRiAkx35EkCD+AgQAgAFNcigHqaNFRfAD2zG039z+RXfxovsUdzeaph4Ys2KX3pp3HAf8EbEAbCu0/MbG1FAHMnFZWETeaREvoyUcKo/J6FQOnx0VzLxbJvDWAOpKpABikEAFZrCaY4HRWS9CebUIHvyQ/7MixhGxKfvBJHjSUTGqejDkwZqWTrVPtbwiK+LugECjUOoPOftp/RM2dqjWohCKewZcivgJF1BQpC1qO49JZq9UpPPcjgn24Nf/Aaq32nyOTA7913M1els0HoHzvWvaO+ZvoyrxtZG4JqE8bLBDJPI9Bi/p7ze44AYECys6I9u75lxxvLn3ts2oqsm9Ak6c9YkOJDT9AIiW0/fBri0WAH/Fe9ItMS1kcmAUDMpUa1MyvDbP9PoCeymm6UQ6v0dhU34zIbc6gIQC2uH9BW5AD2QL5BvkUE+9SiDYARBLd97//+79tUZT513/91/aYLTJkKTxM86YcSDmQcqDWOJACgLU2IhVoj+7gKpBVriqdACBCDYIEAibEuR6AP9oKeATwh1NmJQA0NLiChGlNW82zMyotwjs7t05tMwRLACh2jnVsqtnepOtGAwLgT3fRKZ+FEGNXK4Ir3xOEdcysnCbctB1n7cw9FlwI86VqmAIA/uh7dwayedrSOwA4s1jJtAyBxduHUTQAl3fNN3t2TQX/4KcDv29B1RFgoOWAgH8SZdSa0Ept1r8e5/BmJZJidd8isymC77/xVgE7koy6KkCs7aMFCB1cYvzkv9OkWD87UgXwVVKREHDGxUdAP6hBQFdrruphIjk8R8yEFYS1qcP/2Ho8tP8wL0YzrlwUBJCVq86wcjPCWsHIrF/HBkFpnWNG3syQRNTNjYOWBf/yfv/0Jufc+9d5S68TnYv5QgX8EzC+KSD6MH4vQXTpF378JsUXJtqAcdqz4+kDpkWAuwnmYQn0zLNXmMveeKZZemh3CaVULysbrbznAf/UZBR5D7AI4C4OKcjEuxKrB0AmZEbkIQ5AIA1YUW7/tXHaXa9pkY8J+IGptprWAvgxdlgFeBGyg46BV9AQrECQ8ZAfirEoQAa58cYbzd/93d/lXd1cfPHFVuuPeTETZUYvPqf3Ug6kHEg5MNM4kAKAM21EI/RHgY9yAoAIokpoACLQoGlVT8AfAhXgC8KuEqYVCD4Kour9Wj+rjzvAPoR5tAAZf7QaOTo7Oy0QSP9mglCHdiuLIDTrlFgAHXXUUbaveq+WzvBdTbgRvBkj2s93h3mIVgAgIWNYLPBcVCCQICaxag8AFfyyYtJIsIKgRf7WJwu1/45d3GMe/NMOvyJNZnDcNMnRKCCggn8knihCQ9G3kpAHRP59ZEs0E+vY2n8hdfs+Znzkv69JseAvTnAwr0XoW2D2AUNvScaRwA7jog047gIAx8RycrQTdCefWnMFnj21/2SBDABcVgJEFRCq1n4D8QvYIJbe8GWS706OnZnBCdM8BNxXSBOinell7qtzoDA13xG5U4bvSdNBifjr0b6C+l3NZ/4B4BPZd6JFGibAXhiNCfC37LAFoh1c+JsRlk+fr1633Fzx5rPNimOX6a26O7N5hKaWynW885FVAIhKmc9YZ+gGFKbEAIG4FsEMlfcS7lDQUicNlgcpxecA73vGTk21Afb6+/vtuz6K9j/jq0FD0BRUf47IDowVB3I/cyGKP0fk9+9973vmve99b16LFNnpk5/8pLnwwgtLmk/xuZPmSDlQPg5Ydyqud1D5akumZCujJVNUWsos5kAKAM7CwVcAEN9wvOijCBhR2UR5emi57Gz+4Q9/sJpXfX19FXFUHLW9XukwpUCwBRhTqnXwSNsZdmZMVNsMU1N2iDEZAexcv369BZYAmBASVYMzrMxaeo52IwsStCCYdxCmtJg+9fT01FJTA9tCm4899lgLWLLgoj9odHDNAVCrmpuBBbke9i6cZxYummd2bPdxopbjWUE27iniUPBAPvjdd6fz+IwW4LiPBlyP+CvcvXfK/98CMQfeutnfrLZJtP4yAhpYzT+XMJcHGbkfD4fyaHXwraVdLWL+OxCcSJ4C5LjNM0MzlSMB4yf/FRgqMCtm3AGbGH6rgsbZJp9qidxvkXECoDKiheY0IwZUGuqVPzHnCEV5Rf5tOijAEA/LTdRR5nlSTBdoEibB8JUmNgv45wWuWZDQ53tlx9qje+Xw/5cZGDctB50zwq/X0we1YWzcNO8R7V+Zg2OdbWZsfmvoPJq3cG5sAHB5X4+5/E0S2ff0o+oW1GCzC60xlVcAgwB6AP9K1Rh3jhjlIgdxIDvyTgJoIoAIvug42EgECETLXuU/ZxnpdSEH4J2aaqu8ApiKvFKs6S4b7bg26evrs3OCcWITG2AYkBHZFlkIWenkk08uaBBt2LRpk43u+/Of/9w+QwuRyL5E+FUT5IJM6YeUAykHUg6kHKg7DqQAYN0NWekNVgCQkhAek9BmU9CPM0IEwiL1IIQCWCBsoM3EgUkCwgk7lrVECLUIY2jIKSHQsvOJ0DuTiPFhHDgA/9A2w/wEHtx7772WDwjyHPVg3gM4Rh840DiF0JJj7DTSYT2OH4sAFgP9/f12gYUJN2Oku/rFaG5iBrxj++ZE2KHAUezC5DciI/iejXTqkbmzucnsMVMA4OFtc82WIUGA3CTlNOPvT7TQGsWHmBdukwcA3XkT/tzSnDEHno4CeGASG67VlHDzohcnPAVhKtAKFODP3nOWIukw78zkuoxGpxLJBwj6Ib8zcclL+48AF01T1uBxi5xR6ZnrDWJS6zb7pZN8HwN9+fmMR9LfEb6LzfujfRfcc6RhWObUvsGsP0vpU39vi1l77nLz27t2ma3b/CdBY2t0cXZB71yJ7Hu6OefC+ozsy1jzznP7ikNOwWQ0CZmOOvyIdyvvJYAm5AbeS2ixIUtwAEiqaWqxQJZf3TPhPjIygCnuSQABoWJNtf34AQCL7MMB+IccjmzLvPnnf/5nq823bt06c+WVV5rLL7/canTecMMN5nOf+1zefPxFL3qR+cQnPmH6RF5HZkwp5UDKgZQDKQdmBgfEVzjSfkqziQNoSAHMQQggCAjFkgJ+GtnXKSQggGA+wj2AP7TN1DyF+gAAESwwe3TmK7YtxebDTAJB2qk1BngJeARAVs22FdunYvIBBiPIs6vPuEKMIdqABKNwAsfFlF+OPLSTcWNOqyCtwTXQdJxpWgj8XAP+AXSy0FJiQcYYRTHv+fEP7jJf+Mz/06xTZ14FcrjFfPuCQPjncJGN3OmnbeRKm/8odcwRgK/3zw8zv390CmzX59TS3dJm9hwYsreOOaTHPC6mv9Nql7EnuABAldX80wIcZ9o+tEi0h4RsP+K21eaM9mft0UvMpjsfD02Mn7mhHnHU58HP0MxJJ2C8+arLgZafNS3hOkI9h3a0m52PTM1BsowsaLYm1wOi+TchPg7jEmM07o5RIG1s2ZPVfotbXjHpG4eFD2Uwhy2mLdPyCC8yYlIL+Jf9XspY2Yvs/CYqc9i8atsxnB1zR+Ej8zJmbG50AM2RddolfjhbdwkoP+2Jz42RcdOQM/NtHBwxjfulfZJ03Sl95s0feoGZv0C0f+WdxLvpzvXbzS9/s90MiKm/m448vMdsvf1R9+2Cz+25yL7Pr+PIvrwDAHKQ3dhYhfj9x/SzWvIKbeJ9BMgEIKjLCmQntNV5N7FZNVtkqYJJ5/oAn9DE0/c3MrKaapdbXgH8Y+5cc8015r/+67/yLcM/IAdyFASIjLnv8573vHTM8lxKL2YKB1gzoNwAnfCC95vWjtpSRgnj8/DAHnPnDz5ik/Gby2ZLSikH4nIgBQDjcmwGpEdAw8wAuuuuu+wubjHdAvQLAv7cJqQIhU6zU60TYKmvry/RaKdadtAZwAhQkoWFAl4I0ghj8Ge2CqssKnhBwhcF1eBjsWanQWNQ7DPmEqAyGptoxEHMN+YRiw2E6plOmG4DBAIIKtFvhAGEGz/Niwfv327+6o3f0CxTZ+GpjR47dSd/NdngDSxMEAWWZ1FJ6uid126u/dvnm8/9/Hbziwen2q5FrFjSa+5/ZKf92CTAwHJBhHY85TJZBmQQs1+0zmz0Wc3sOk8I4DfcI47ohMoJALaJ9lG7BKc4cCA8QsVoR6MZmxMZHnH1qMiPwvcsyAdg5LgusrhjFs83j23IjpGziKOOX2b+sG+ngEnF9W9cvrZu89/MoACAEvm3UkSgkZr87ZdxU/BPeZHD/uxH/P5F0eRr3Skgm0Nbk8zDC5rMeJuA0iUSmpqtu3Im4VHLGstq7jYeHDaZgVGz7tR+c/V1F5vu3kJ/cryjkR82b37Q/Md/bTF/ukdQYQfNEXBv7MHdnuC1jez7Qons+5r6jexLV93gUVxfcQ52le2SDVWnDzqtCLcWvJeQrdyyoaaZyWfkKsxv4Y0SG3ZoUlbayoLv0k9/+lPzmc98xvzqV7/KW03QLlyP3HTTTea8886rzd9BZV56TjlQJAcKAMCL6xQA/GEKABY5/Gm2HAdm/io5HeppHHBqcqH1FZfcwJ8ulpwaf147maRTs1PAC8A3/NagFajRTvFrhuZWOQEc2g+4Rf3siEIzWWss7vgqLxgLdosBmZgnTrNTgLZqaBsA/OHsHO0HfDVCzCsWFv39/RUXpOPyNsn06vRbNTcxKWI+M68ZMxZajCELLycd3t9r0IQZHMhqjzifxbm24EMc8E80zA5f2mVed9XxZssD95jmiSkTX2e9bY1TQMTaJYvM5juecD4WU1/xDyb+xQD+3EBGQUL5MA0QAfyK02Z3gT6fVxzWazbeObWw80lmQci4EXH9yvK8T//oYk6jL+/Dzw6WZ47YNw/raTOPCvjnBfvuGh8WcNPrSXg1NNEN/hkBqpoqCP6Ft7JKKWRcm8TnX2P2dZVvhHI60O9fPnXuQjM57geaDTvSBV7KWLXujgf+NQvAPyYAYMvBEbP2uOWewJ/WiUzBJhTH8cevMf/723vMd/71bvPk9qym8EH5PetZ0GYO7s5+1nx/ds5KG9l3yfIFeqvuzhoIit94pSVLllgrBb+NHk1X6bPTBx0yAxoqyHuYCG/cuNG+uwG+eGez4TrTCbAN0A/wT+VNgqWsXLmyagHJAJJvu+02C/5heoxPQGQ9NlM3bNhgnvvc55o1a9aYq666ylxxxRXTZIiZPmZp/1IOpBxIOTDTOZBqAM70EfboHwKJ7sD+7Gc/M6eeeqpHqum33MCfpgCAAbBDQPcC/jSd1xkQB7AC4UPNRigL4ZADYTIpot/s/GDuq6Yz1NXXl9UaU54kVd9MKYdxAahlnBDklXAIDcDEQqQSvENoBfjTSHm0g7rR2JwNCwnlu98ZbU3mNwsuFoxK+IVinBDyFay/9l3/au664xFNkj3L98MDG7DPLH4k328nYco62ToF1jmfTbsW8G/lkT3m/Iuzmsc837B7xHx7/eMFSVubMqZJQI6hkXHTO7/DjD8yKN/VKXO/zMEx06TBPgTkCiN87Y3Oa84nsz4LEwYA21qbTevwhBk4EA6ojolZ7Oj8iDzLt9rjQqKeTgF9uec5kM9vDG0qx8NATNCRzlk7prGTMpZ5cqQjOMVwt/wpkr9e2n8tezB3zddWkQsLLPt+EyrShMJKfMA/TQTfbdTfiHxvke9dRrQclbgaPES0ZCPm13zOM2bIlz5rlVk4f64APUOyOTNkDoo2LJsMA2LWOzQ0Kr9JY/YYHR0TzfIJMyYbcXPaWszKw7rNG//6udM0/pzl+10PSiTaf/7X35rv/2ijGZKNgWXyfX/6oay28FGrFpnXveU8s3Jt1tTLr4xavo/MwmYlppnIX1C1waNi+AX4x3sJOY8+KbGJiJzHO0rfTfpsJpyRVfCprJuVBGVB4w8AtBr9hfff+ta3zAc+8IG85cDq1avNLbfcYp797GebH/7wh9YHIOCgEhuIr3rVq8zVV19tQUu9n55TDtQrB1INwHodubTdSXIgBQCT5GYdlYUWIJpD3//+980555wT2HKEBoRPBeg0MQIMwA9HXOBPy9DzkAjyCLr8MKugS5kAPIAXTq1FzRP1TLsRPDEXpR6IsimXI8lIeVHbVK/pvMxO4R9mt5ielsOUBQ1Rds+dpq74jcRHo1u7rV75mmS7+b5iHg1gqwsPyuc7xHznO/Wdr//WfFuOApJ8Djyn4JGFCwAIHCAB2nWTzYI+hBC+5dYd222efd4SmxKNFcbugb0j5u3f+ElB7uMOXWw2PvCUvbd2Qa954N4pE+HmfaJdJFpG1t/f1BqyIL/7w+gc8W02Z0rRHdDSJOzfbZ34/tsYwfcfbRvqygjP/Ljsbn3us/Av75uPa7A/BqQlZjk+xSdxm+EYXiDtKZK3dMft+69xSDTKXJbfSbQ1rAzMWBvtRAlLWYHngH8Dovk3hYEXVGr5FsHvnzNTy55RkxHAWmlC8Gj1k6n3Yp2lEX/1yjPMReceGytbkol37z5o/v5L/8888dAOM7h1nzntvGXm8GM67fuI9xJHrWnKBfUfmSVr7rw57+KCdyvgEb/f1QCPgtob9RmbVGjDAQaqLEZeNhMBAqP4sI1aVzXT0Tc2K3kPK9E/NiurIW8yn/70pz/ZSL6//W32vQ+QDBD4l3/5l9M22tHUJBjIV7/61bwM8fGPf9y84x3v0O6k55QDdcuBFACs26FLG54gB1IAMEFm1lNRmNGg1cVu4EUXXeTZ9EoAf+6KVYsJMFC19EhDe/v6+mKZTCD00EfAIw0+guDMYqC/v3+a0ONuS/rZnwPwkzHCJIl5AgGqYr4NGIhAXyohRKP5QB2MJYQjcRZBOKxOKZgD8AwNBIBAFpNKLCQH9reaz3/qf/WWRZSsb7ipO9Ou3H4ArdlgUzAA2ChtOP1ZYrJ38kKrJUzUSBZCzJVNW3eY1/z9vxXUs2pxr7nvsZ1m1dKF5pE7n8oCkjK/WvaJya+AXxb8y06Fgnx+H0bmF/o2SxoA7GhrFoBmXDSdwtXU0HAbEV9rviS8ihqQYxIQEOVoByDrW26ZHzAcw50CBjcVD0hO0/6TsW57WvjhC0mXsVMCMmcmiu9LYi2T+RAK/gECxwxs07x31DQNTQGA46KVOtwtGoDFkLTxFecfb/7i5X9WTO7E8zz26NNmnnznt2593IIv+m7ivY8MUQ/BKNi0IYouri4g2s7GDTJLOV2jJD4YAQXybuKdBBCo/SQ5m8lqHlzKpm9A1WV9xHxza2ziqgNz32psVqoM8OEPf9h86Utfystql112mSHiL/JaEJiM5ubXv/5185WvfMX6DMSKIKWUA/XOAScA+IyL6tMH4B9/9BE7DPyGsqZNKeVAXA4ErEbiFpWmrycO4PdD/e+5210N4E/bwO4ogi6COlp7gBdO/3MAP30CBDrNGTWv84xQCfC3b9++/G0ESwCI1Fw0z5KiLxDOV61aZXe0eQFxAN7qdTGArTaGcvBjhyCtCzjqm21RmZUfxZ4R7DGt4mBRCT/5TgGsT5hBix3JOqx4CtH24uVywYXLzZErFtjvM99bp/ZDz7xCkLizo9VseXynaWnKmIOPHchCP2Lq2rpPbIKlnY2itRQXliEISDnp6OXi+++uxyNVMSbBPyyhxScaXdZ/IRqNgsUAbtq2hvBUK7K9AgRMwJpYyyz2PDJX2lEC+McUdPv+a9lbJfAPJjBMU/gYdypPgH8HRfMvoB0WgC9mfrvm2IT44SuWzjrhyJoB/+jDoYdlAYquruxGkWqb4RKB4GccADHIF4sXL66I64qovOW9x4YX71Dd8MJElui+SWyoRW1HJdLxblJ/0Gwm0mc2+rD+UBmC9xabRdXwNVwMD5A5MfdFXoVwX8NmZbUCysHLr33ta+aDH/xgfgPwuOOOM5/+9KfNmWeeGQj8af/5ruAHkCOllAMpB1IOpByYORxIAcCZM5axeqK7q6oZR+Yg4A+NHfXzF6uiIhOzE8yuBjuUmH4CCAHmodHEAYAJoIAQT9uU8BMH8Ldr1y69ZXf+Mb0gT0rJcgBtMnjLWCDAA9jiSFoDhrD7zTNMdoN2mmmVCv/4aFRn2QjRlI/Zk3Ock+3FzC+NuY+vH3jJ7ieLrJ6FbWZnzoF+1qY0mA8NgjopXmjP8tmP2gQQetFL+sxxxx9h6/QC3bvntBVkP6J3gVm/50lz3OKF5t4/PmEaRsYlOIAgZQKCWM2/gtTRPkwPAhItX5RUc9pbzIObp0y8gvLAL/yuNYkfNBuYA2YWUFbXTflb8MjvA+BQlQHAURlC63/Or40R7hPAwklE/W0cc/PHmaLM1zI2ADBhv1dla4WAwVbzLwj8k1fepItvUdvjtm4uFrxdc9gi84G/fG7UaiuejncTm4loz7HZyQaIBqMg6BhadsgYHNU0D2auaZAIQEAI+WzFihV2o7PijKtwhfQVDTk2+Nig4t2EXIoMx8HYqHkwY1prhLzDXFIXJfxuVFNjk/l0xx13WHPd3//+95ZdyGHXXnutBfJqkYe1NqZpe1IOpBxIOTDTOVCkCDnT2TLz+6cAILuVAH96OBc+CDKVBv7cnKcNgHxolDnNGdFoIloZYB/CFqahAEcI+krsICNU8iyl8nIAwBYhncUUgjBAIGAsC6677ror73+O3XDSOom5h+CPj0YNXgHYzOKNMt3pnXnT63gcUEC1r6/P3PGbfeZ//u/9kQpYtLTTvO19F5p3/eW3s+nBZ+S7OY1k8TFffO698eqTzPEnrAk0e2qSedDV0Wb2DGT9cu7dN2gWd841969/UjTkJi34x5mgDB41TavafcOCaS5tJ3eaUj4ftaxbtP/CI//aOgSwyWpzSU/8OhMV/ZN0tgjBRt2ac6X0J27eMZEexjroj1+HwkukywV9APw6wHgXX2Z4rbWbYhJz94PS/4C5AIAXJ+iHu7duALCYCMDLFswzt/zNi9xF1+RnZBhkCA42EQGY8M0G2IbMwOZitcyDkWk2b95sI+TCPN57bNLwHp1tG170XWUIgD/GCXlO/ekhHyA/kAb/ddUmNiyZOxzIMBCWKQC3Kl9Xso3I7mghfuhDHzK33nprXouUAB7XX399XfuOrCQf07pmGQesEFJnfQ6QD+qsJ2lzq8iBFACsIvOrWbUKKPj4QNtKgT8At1oA/ty8oU1qzkibAZgQ4hEOEaCdBOAH8Ef6lCrLASdgqwFD1NQcx9IAtphfscBB4OcZ91QTlUUPz/v6Cs1FK9uLmV8boOrJpx4TCgAS9fUll59mLnvNsyxTlh26wGx9bLc3iCULkKWLOsx1N1wii+1FkZiIGTAA4NKueebh7XvNcZ095qGRvQKCjJkuiQK8f/uBSOV4JZqm/SeJgJX4rSsFtKKuuWKu/MC92WAlfA4jG304NFFYgsLnFiITwKzYwBuFpcX7ROTZ0fnSAvldLoXc2n8t+0T7rxbAPwTs0roWiy2ToltL8JEW0X4MBP+k1PGWEvnuAsW9vidBje9sazX/8JGXWjkhKF0tPgM4WrNmjTXNDDIPBmgqJwCH1hhBIjBJVuKdCPg32zW0kCEA0jjgExrrjBWALVYGHGi0AQQC3JZznHRsnGfeH8gtyJ3InxBaigB/1TJXRoYH9Lvuuuvy1i/HH3+8Nfc9/fTT6/K76uR5ep1yIOVAyoGUA8lyIAUAk+Vn3ZSmJpYapQyhiwNhCmCm0kJVHMbhlwTfKhCaY06iDzz3Mjl0pkuvy8sBxgF/jRyAewC2jBX+5wD88HXEPNNAL6RXH43VNMcqL1dqq/SVa5ZmGyQLGi+sY353q3nhK48yq9f02oUY36nVxy7LAYCuHFLGieuWmb/5yItj/Xb0zG03BP09RDT/Fja3m4f+sM20SFCNF734RNMmZqDf/dbtRTOtnP7xjlwq2n/bH4/UNrtZ6wJdvDLCUZvW66HPPRsReMoDgk+qZG+j60LQj1LBP/rq1P5rHCbIi9x0Ta1kWx+xtAq1AeCP/w1iHt4sRxD4R8sniCAdYS4F9dKpAWjHoFAhOyiraZONgy8K+NfeVnummIENdz0MMw8GnMP9CKBcku8jL62xagaJcLGl5j7yzkHWw3czsipagWwAs7nIwTgyRhxot5ebkGUA/jRwCXJyX1+fPaphqQAY+bvf/c5G973zzjtt99n4RgvwDW94Q4HP3XLzJi0/5UDKgZQDKQfqhwMpAFg/Y5VoSzF9gb761a9aDcBrrrnG+mGpZeCP9rILjMmFM0AEAjo7+5iNAGyyY8yByQ/CWS2Yi9D22Upom6r/Ocx42MHHZEbBPwR3ds8Zr5Qqx4GFi+aZnt655ukd+wsqRevv3ItWmNXr5tjvE981Fl5oN/Quzi78J50ghCxCXnTJCea1rz+zoJwoHzQQyM5dB8zk1hHTLi6w3nTVOeaC56wxv/11NPNkv3oK2uhMBOpRAsAzf26buX9T4caDs/hp11EBOtoVhZzpAvzERSmqmDQjaP45x7+YQiTPRMYxEDKHmvcLANZQwsAU2Q6vbJiemxICm3iV6b6X1foTVkpdGQE/w3o+IUBdsf76Cup28NiW5/hckM71ISMt/Mz7X2x6FswcX7rIO27zYN2oUvNgnqNthmVBsfMToAZtP4BFp9YYAT7QYiu2XNcQzdiPgGsAsmwS4lqE9xH8RIZgM5GxKqcZN3IldbCRaTXIhdPUx/hVY7OZNuBqBb9+ROmFmEOve93rzEc/+tF0TlmOpH9SDqQcSDmQcsCPAykA6MeZGXwf4YEIruoHBwHiG9/4hrnkkkvM2972NvOMZzyj5nqPAIbQB/in2ovs/rIzjGCIIM/uujMQBQIiBzuifX199pwK2tUZWkx5AP+cGpuMGUAgfv/Wr19vtQUZp7AIz9Xpwcyrle8CWoC/vu3efOeWHi7aAzdeKmDfPPs9w/QKAJBFKwuOplZUtIRyaAWnq958jnnen6+1t+P+6ZnbYY5aLJGKx5rNtn0HzXXve745/rhDbTFHHl0aIBzXtDFq2/sXd5mNT0b0/SeFRjL/1crlt1lWcvop9Gw1AENTJZdgRAI3T6KFViqJ6fJk+1Q5gH+NTtW0UssvMX8D7SuxDL/sFvgjCrSAt9QTBfyzfv+S4Ls0yjkfo0bJRjPxo2+5wByRi7Lr17d6vu80D9ZNRN5NyEkcPAcIjGsejMYa0WHRWIN47/Ge46iG1lg9jxHvLDQmOQDfdJwAAlXewwJEx6lU/iIrM/YAt+qfmIjMbFgS2KwahPz7xS9+0Xz4wx+2YChtOPnkk62576mnnpqCydUYlLTOuuUA77Ywzfta61y9tbfW+Je2J8uBBnnBlUvOTXlc4xxAoPnmN79pbrzxxgI/emeeeaY1KTjnnHOqLkwAECHksfuqGmOYjvb3+weIYEojDAIWInwrIRgidFfDb4y2YbadGTPGDvBWf2qISItZD+bB+NJhVx3n7EpoDDJOcRdamj89R+fAv3/vj+Yrf3ebSEDGvPIvzjCXvvyUgswE2yHCoZo8MYa3fnaTOSD+yjKiBfbmq59lzjn3hKIXst/69Xrzu3vFrGvDLvOhd15kli9dkK+ful790n+QRc5g/l6ci5H5TWa8bbp9o33hZaTDRVDnvDYz+vSQGR0RBCcCUdckPtsi0jhbcg0h6QGOHOWNo5SZgEaeo0jPy7FW0cCeIzWHtc8zd+FNQLDxHABIkJfW3bWj/UdLM4Myvk3T505hL+J9ssAfGpuAf2RF8w+z35BimEPjrZIqoTFuGJswbU9no82OzsmIL8eQfWBpwNsuP8NceM6xIS2dWY+RPdj0YAMErTOlqGanvPtwd8EmihLahABXSZoVa9mz9azjhIyhICu8aG5uzptxF6Ol5wZuARPZcMZHMSBupYn34W9+8xu7SX/33Xfb6gEhP/KRj5grr7zSulSpdJvS+lIO1CMHWFOySQCd+OfvN60dXXXVjeGBPeaO//iIbTO/e7hASCnlQFwOpABgXI7NwPQIUD/4wQ/MDTfcYP2JaBfXrVtnhY0XvvCFRS/wtay4Z4QdtMXQGlOTGQQwhC+i/iLchRFlYBYMwKQABnkQBikDc5JSd4jD2jBbn7NLzcIJEBbNTAi+4+QcYM+pick4OSM8K88wDWa80fCMMt6aLz1H58CWzdvMZz7+U3PtDWLa1zsvn5HvnJpr600AW8buE9f/t3ls6z7z0pf3m+6eNjs2CCAIVHH9MP30T1vMH373sLnmFWeYeWJa66YPvfd75o9/eNh9O9LnoQXNoq02faFWCgC49ohDzKb1Uwv6sIagbRXHbHMczCkI6LGIYiEASCCNOHWEtdnrOUE/hruSAf8A/GwAkVxFbTsnZAc+DAbzalX57jUOSZsy0+dOMTVaXUIZt4axHPAnhWBi3BgB/KO+cdH8S3R8pe72nVlN3uFOAck7AoBO+W1+5XNPMFe+7JnFdH3G5FGzU7TBeF9BvMMA9HhHYR6shDzFogzTVLVWYPMRrTF+Q1MqHwecUZ4ZByXcV/B+whrEKXvoc+cZNzO8+xhDJd57bFpWA7hlvrGh/f73v9/80z/9k20SAOTrX/96C/6lFhM6Suk55UA0DqQAYDQ+palmNgdSAHBmj2+s3iFo/OIXvzAf+9jHzE9+8pN8XnY93/rWt5rLLrus7AIQbWDXHQFMI8MisAEw9IvWX1yAQTuBYAgQiACvBKiE8I5gmAJMypXSzgjdaDyw+FGNTXjLHGIMw3bO0ThjnAB/daEFSAsICGhbDQG8NI7UV24WP/CfQxdQqrGpCw188/UfucDs2r0978+RXvI9XbJkiR0n8kShXfsGzPw5raJs5Q1CfP3WX5t/+affRSlqWprBHlGN89D0KxYA7JrfboZ3DJqx0SygPa1CjxsT7FPE0Jizft5CAEC3+Qcg40RL+QA0ltHDC5LTQHNq/zUdmDDNg+Vru8eQRLrVMDohWnqlA4BW6w/gL4sZ2brjgH/Mh4mW0ttR0Gl5x7ZvzwKAQz3NgeWfe+JR5j1XnVeQfTZ/wGqC9xvgkL7f4AfmwcgSWCdgLqqyC+++o446yr6/woCn2czXpPvO2OAOhnHSDWTqwLoAeY/3FGPlJOQN8jB+vAch3mMrV66sGnBLOz7/+c+bv/3bv81bSZx22mnW3Pekk04KBTOd/UuvUw6kHMhyIAUA05mQckCWJvLSc4imKUtSDmQ5cNddd1nT4O985zt5IADT2auuusruPOKDJUliGqKth8mM0xxUI8MWY8Lh1T580QFuIMQrwKEAEwJ8UvV41T2T7zF+7FIzfvAYgq+AdhxuYTuMFwjtCO+8qFWLggUUGhd9fX020nNYGenz6BzguwCvAW518QPYzuKVxZLf4pWFFvncC2LAQsY9isZFUCt/86st5obrfhSUxPMZL7WhhQIA+oBv1g+azzPPAuVmbO0/wbXi+stD0y7Qd6F0bBoAKG2bEPNcv7769Sfq/SGJ+JuUBlqB9p9oorU9XVumv3meiG++xvGGUPPcfHrXhQX+MPWdUkKyKeKAfyhFWtPfmPPU1RTPj21PDdu+DSzyBsnJdFzfYvOpD7zIM/9sv8nvpZd5sPKF30uAJja+0s1F5Urlz8gluBnh/YR8qYRsgmzJGAEKouGJn0aVPZFXsFaIsmmpZSZ5pt2//OUvrSuejRs32qKRvwECX/Oa16SWK0kyOy1r1nGgAAC84H31aQL8nx+148ZvG79TKaUciMuBFACMy7FZlh5A4KabbjJf+cpX8jupmLPgc+Tqq6+24ECpLMFvC8ARZqBKCDsIYFE1iTRf1DPABT+cHAp4ILRj6gFwQR9TCueAArfsmqu/RfjIC4nFD/6SSiHAP8BaDUShZaWBXZQTpZ0ZPxayjJ8Ctyx++gRkBRBnoRSFWBCjtck4ocWpxPeX7xPfqzDtT83jPG9/ap95/RVfdt6KdE1wg2E0AH0oLgDY3dlhBp46aMbFf1pUQnvLSwMxKP8EoGFQ9FkPAJDyyuIHUOaGke4OLhRUUr7TSZBT+691l2jZCchWq9RIZN4gbUyPhlvgjykC+Od63iB+MxvF/DlKjwGwk/T752qKads+bG8NHgJyPJ2Wd88z/3jjZb7A//Qcs/MO7yeAI2dwK+UEv3n8hjrNg/VZeq48B9DKRN5Dy0/dktAKNro0wAefsTZg46tU2YWy4hLvY9qHue93v/tdm5138Bvf+EZz3XXX2Q21uGWm6VMOpBwo5EAKABbyI/00OzmQAoCzc9xj9xqQ4NOf/rT57Gc/m3e0jID0yle+0poH4x8lLgEUAPyxQ6uE5hDCFyY1lSAEQQAmtAKdpiI4Vwa4wGePn/ZTJdpXy3Wwa874OXfWWfQA3BIpL0kCYELDkHFSoJHyAZgAq9AMLAZgSrKN9VYWgDsBPlTrQbVW+sXUvtjFDwsY/G0CBDr9brLIQtsCYDiORgzlXXHp583+fUOx2IvftJEFAQAgKEwMcGftEYvF998TsdoQ1/yXvhJ6dqLVDR05qhVwyQtAStwPIBpwYgU31iZAandAexxNC7sEUBvBj6BQZnDSEPm3ln9bmwYnBIyN1neAP/47/fw5+REH/CNf4n7/nI2R69adwxINWMa2d/p3pKu91XzrpisEGMF+PSUvDvBdBfRj40RNgbEeAOzjd083FcnLPX770neUFycrf083FQlO5hwn3bhEfonzjkqqB8wj5Gt8cesm2umnn25uueUWc8IJJ9T0b2VSPEjLSTlQCQ6kAGAluJzWUescSAHAWh+hGmsf4MsXvvAFc/PNN9udSpqH4PSCF7zABgw58cQTQ1s8MDBgTQ2du+YIyQB/aHZVgxRgeliCVqjwRTtoF0AgGom1vFitJM/YScdHI4CcEsAtIHC5NSdZeHkFdgFgQtsCgCmuubH2Ybacmd8sXHfu3JnvMotTvn9JArf8VgAE8j234JbUBkiLhgVjFbWua9/zb+auOx7JtzXKxVhbowSa8AcwbMyJiABgT1eHObDtoJkYj6791y7A0UGn47eARsObhpzWWOecFrPLiNM4P/IBAOnPBJFiSyXaItU3iAYbpQ2J77+xjmggWFjVee0/qaNtJ5pwCbQ3rNISnh+zpMc88OjToSVYrT8BS/24FBf8K4vfP1cvWp8eMROiaToigXKc1CbaRt+48XKzQOZ8St4cwGJh8+bN+Y0TNLTQduc3jd83lSXQNismerB3rendpDjAxhdamyrnIdfp+4k6GEPV3iy3PEN91H3bbbeZd7zjHXZecY/6AQIvv/zyyFr45Esp5UDKgXAOpABgOI/SFDOfAykAOPPHuCw9xGTim9/8pvUTiDCsdOaZZ1og8DnPec40wAztrW9961vm5JNPzgtcaHABPKBxVwsAG8IYO/gAgU6TZMAKgED8oUU1i1SezJQzY66RYVVgRlMT4K8awC0AE3PKGZkR8E8BpjRgSOHMQ8MVk340XpXQcGX8ALrLRcwbNbdXf47UBajOdyrMn+jXv/Ir8y/f/n2s5o3OyZixOYVO3p0FiLJWZPPc4/oXm3vvjqj9J5qq3aIZvVv6PN7uXz/V8x0C+FOw7dAlXebTN7/MvOSqW83QiA8I6AcASnml+gHEP12DAFkKy4FJDiwRWCsiUEqf/Mip/Td/SMDZ/YIw1jjhA2/jlqlNDndzLfAnwxSE8+LzMCOmv1GpnH7/nG1o2TUiJsaNZmze1BxtkpH/hw9davqW9ziTptc5DvD7ica7c+MSP3LIL2xAeREAIJsgbJbpOxM5B4AHrcBy/u56tWc232P82PhyBoJjDND4473EO4p3o/MdxbuJNLyrkrYwYD4ARLznPe8x3/ve9+zQIL/gWufaa68NfS/O5rFM+55yoBQOOAHAk55Xnz4A//CTj1oW8LuV+gAsZTbM3rwpADh7xz6RnrPb/cMf/tDuVt5+++35MtetW2eBwEsuucRqbOG/BMAQM4dPfOIT5rjjjrOCF4JwLQB/+YY7LhDeAQIxf1bCNFI1zaphJqLtqOQZMxmANg7GGwIQZeFTC5qRCPYsspzCuy6yAJgqsYtfyfGIWxcLGuaxc/xwfA7wV0ngHXN7/BvRDvU3SF/CtGz/95dbzMc+HC8QyHBnkwBi/v4LowKAC7vFQfzj+82kmMSGkmgIdkvUz317BrPmxx4RiG0ZlCW+BBtEZ8yCbbIQPHHdYea6v7nY/ha+/j3fNg9vnXJYn69XsrkDgOSfycU4ylx+dToTuq8x90XrL/vVxpLV0libRP/t8eehu5igz20yFgcax83i+XPM4KMDYnqXqywoU5WfHX/kEnP3vdumtcICfzTfW08D8AAAQABJREFUw8+fM3EjkYR9cFxnOr2G7+X0+6f1cG7ZM2rG2hsFpM6OL/PqhrdeaE5ae5gzWXotHOB3i/cLJqPqO47frBUrVkQG8NgEYdHJoSbDMJdykCfKATClg5flADIL48fml44fwB7Rfd2yAc8BCFlUO12NIPexyObwA3vj8Jv5gGnvxz/+cYNFDMTmOffWrl1bszJxnD6maVMO1CoHUgCwVkcmbVclOZACgJXk9gyui93M//mf/7FA4E9+8pN8TxGSEaZUyMFU9PrrrzeveMUrEt9RzVea8AVtB0Bh518BMLQAEQYR3meqphnCMGNH39VXDsIv5k5oPiS9I17qsNFGQECEfQRsJeZcX1/frPPnyFyFH2htOscPjQfGr1rAO78V+P0ECMScTgkfWnyfaJvTjPupJ/ea//Oqr2iySOdBfJsFaK51i4njngHR0gsx60UL7N4N4dp/bWI21jIyYQYHsqaVo13TfasZgLahcQviTTbngDXhxUXPPc5c9Yaz8v36wM0/Nr+965H85/xFCACI6WisqMNSt9Pc1wJ/WURS0EDxDyfRf5Mw/12xrNfcsytrbr5mXrcsxMPNavN9ruLFOgEANzgAwKjAH00uBvybaBFTxGIA3CJ41Lx31IzOzch8EQ1PGfh3XHGmueDs1UWUNHOz6O8UflJ1w4L3HxsnxW5c8puMNiDvKPW9CgcpF3kC7fUkAKaZOyrxeoabCyxUVP6MOn6MvWpvsgHMZ4h3Ju4yVHsz7juUcn72s5+Zd77znVablDIZ8xtvvNG87GUvm7XWJfAhpZQDleJACgBWitNpPbXMgRQArOXRqdO2/e53vzNXXXWVueOOO/I9wNT3vPPOMx/72MeswJN/UEcXXqaMCICYBaNpRh9nAiGkAnYCHKFdBwHI9AmIBkBT6ybQLLLYxQdgUj8/9IHdfsZppjtjZ/xYtGCupgsfxqy/v7/mxo9FFuPk9CfJXGMxzCILcJ3+XPESCQSyP1ogECL8Dva2ZrXrGHgXyVfW/P1HX2Y+/NmfmscFXPSjRT1zzZ7H9gVr/0nbukQ7ZHD3gGwOZBeJo2J6PJHTrLJly33MfDMC/kFOzcQ3vfZ08/yLjrf39c//Z+9M4K6ctj++mt7meVSGMpYihEQlNBgKl8yZQ6QQFbklRJPCra6h/E1XuWYKV7iIjFFocs0yDylDc/nv735b533e855z3jOf5zlnr0+nc95znmHvtZ89rN/+rbWm3f+qPPH8h/pnybu5fCwGYEJxAI27r2X9mWvaUlvgz9xq6zvfrW1SMS2A1HYt6snnK1dJ++ZN5X/vf19SH59/2n2HJvLRJ8b4R0PmX7QEH6WqYcYeC/5tiRYRsNTRoT9I4rIFMC5LUuW3TbKhjkGMTXuffmQHOatvxyzdORi3Yd4AONIEV2x2MXcwB3o3J1KpjQJMkdyDmWezlQgtlTr49VwAW9pPE8yxTqP9Wpk5MNH2Yw3ERlo4e5P1BO3EeqK8NRFzGPPclVdeKbNnz7Zqw4PkkksusRl/nSu4X58kV6581EAZALB6vUBVc/3aVeJcgAPVZL4srAMAfdkswSwUjLH7779fRo8ebRc71ILFFrvlxNVjUcai6dxzz7VxTgDOgii4VLIgZEHnZZo1btzYGgjlxTTza51ZpLJgBjgi0QeC4QMQw8I5aC7P1AcD7gvDYFRDjjoBKrFwZ+c9UWOA8/0sxK0kzhHGJYLhA5gGazPZzL7ZqC9jA6wY+pW6aVF2xg4Mt5tumCuL3vsqrqKsr13Jxt9TTCv8pJ4Ht5YrzjtURt78TGSm3dYT2gEALSnrAhq6ngF7GlWvJqt/+iP0FcDZhgaG/cfNAY0M5gdoV8G4g1Ywx2+pWhxzrZKp26jhR5p4qK1C5/IB4OGuB1+ROfO+LPW9/cO4nUarE79z73LjAJo+YeP84cKKcMEIF91c+S9Z17gkPpw9FoAzBqvSHhP2327bGvbfLz9L7WpFUu1X6lfCzA071Hd/tmxWX779ZpVsNszOWHH+QgU3bt241m6uYaLpmfaNVwCsNxeZ4xM4J95rRzuu6rotss4kd+m57y4y/MLu0Q4ruO9hSrPxhYHI/IHgngvrL96kRYkqLRrA5NyDE9Vksbs28z0v9dbAAwBPFMJepCLK3sQrQudXrse6iLUE6wrWSuHC3HbLLbfIpEmTQkzS7t27y8033yxt27ZNaKwIv7b722nAaSBxDTgAMHGduTPyTwMOAMy/Ns16jVgoP/bYYzJy5EhZtmyZvT9gAyxAAhwjU6ZMkalTp4Zc/vj91FNPlUsvvdQuru1BAftPmWYsNhUwowoAgIAWAIKJGIK5rD6umABHXpdMXDFxF80HF2fcrZRppoYd4J+6cQfd7Yrnj/ZTxgPPUiYy+2b6GY3mxr3w7dUy/5UV5d4ek31Ns6pSUQGusDNq16oqj9x2ju2Xt896XR79z/thRxT/2axRbfnlS+OeXIwBlDmmohnzGlSuIqsN888rm6pUkM21qoQSe9jfjJtxZZMIYlORQXoMyFPNuP9OHm+SLezQKHQqGwkADwCg//vqD3nilQjAYzkAIBeLGgcQ4A8wUmPSRQH+LGhpgL71dU2ijtoeRhrgHzo1mWMTke23rS+f/fKr7N3YuFIvDQD7z+hpu/p1pFnV6lLNUCq/Xf6DVDLjRCWT1bliJfMyLD37blx1K/A3gKj5vObP9fL9F7/KXwYpXFeDYIzxCefjur0Z/Xra5C/AQFW1/V7/iO+65R1VzSQnab5dbRl7xZE2gVNQ5qny6pXs78zl9D1vuAQY/bvuuqsAIGVDdD3BRog3/py6BzNX+XkTJxs6inYP5nTmPlh/6rVASAnaLxPrMNYTAIF4GuizM2jQIOncubNd9x511FF2jnn22Wdl2LBhNn4kZQcgJAZ23759fRdCJZpu3fdOA/mmAQcA5luLuvokowEHACajNXdOKQ0cccQRonH/YIydffbZMmrUKMuy8h7IovbOO++0O58sthEMj2OOOcYmDOnQoYP38MB8ZvFJrBmAQC+Axo4zQCBMR7/Fy1PlwjgCOKL8KiyYSfCRLy7NWi/eozHNgurGDXCkmX0V2ASAxvAJslsRRhWucYC2jBuffLRa/vNU+QAgiQ3WNagilUw223CpaMaaCSOOlj3btLA/zXlpidx6z7zww+zfbbdvIv9bGgGEM7/WrFzJuvMS7y9cNuL6q/H9+NEAO0UG/NtggEHAv/p1qss/bznFtE0NeypsR+rI2KHMx99NltzbHv4o/NIWgCsPBooYB9CUoSL6AMy0YFLZS/MNWYAprxiw60/cfz1gX0XDGLMuqqZ68cr2DWvKZ2v+lF2aNJAVS34J4Vnxnp+t4wi7t3Oj+lJHqshPX6+WPw1Lse3OTWXhm5/Lds3ryrdfRkjI4incFnOBv4qKFbPFXGtT3QjxHz3Hhz6aeaNo5XqpZBiG5QlNx0vb7y+AR9rSiAULwWrN84UbuIF6DVOTv/nV/G2P3foHb6a8O2xfS/52xLZ2/mWeghHNGFieK6O9YJ79Bzsc4EjDRbAxxPwHqysX8zbjuLoHe+PPURYY0YBIzj245CFk84v2w8sEQU8tW7a0r0w/zyR0YS07ZswYefDBB0OFUq8J4mIjALdDhgyRESNGlEk8EjrJfXAacBrIigYcAJgVNbub+FwDDgD0eQMFoXhkMmOX84QTTpDrr7/eulvEKjeLJjICEw+QhZtK165d5fLLL5fDDjvMGib6fZDeAQAx5r1MLHbwMbDYwfeLyylAGMARWVlVAI5wdQqqC7PWI553mGYsAmBbeLMykhUX0LZ+/fq+fgZxQwc44qXAEYY8hmsmGA/x6DQTx2AM49a8aOFyuXV8SZbxaPda07jIJsKIlH21U4dWcu1lR4ROXbj0axk2vjgeU+hL86F5kzry0+fGX9UiLp5fTFkaVq8qf/z8Zyjen+dXA7QY8KemYX8ZIMaKOb6aAf/WAaSZ71pt20Buuekk4zJmki+Y34izibu9hhHASIRxW69+I/nbhf/nvXRxWUx5tl659G+ev0rFATT3sO6+Wo8oJwP88bIJIcy1iEe3pkkJ0ldxw19iQ9pRLw+w5LltxI8N6lWRXzZslCbrq8iq1WXB0ognZenLmsZtb+eG9aTyur9kxee/mFiZxeVrUL+m7Ni8nix86wur67btmsuyhSuilmqzYQQCmKpsqlrRxH8Mc53WH8PeK/++Uar8EQGpDjsunX/ubOoz8IqeUqt2BTv2KWjCPdSVEfAiH1jf5emNOZAEH4BsKtTdT+ESYLMxT/HSRE6UlTmaNQVjfS5AStVXLt+ZA8nMzByom1+4a7P5Bfsvm0JZAADvuOMOee+990rdmvXEbbfdJocffriv1xSlCu3+cBrIYw14AcD9el0tVQMYA/Cd526wLQQTGdvSidNAohpwAGCiGnPHl9EAC2lcf/fZZ58yv8X6ApbPU089ZTMHv/VWiXHfvn17u1t67LHH+gYwi1WPSL+xKw0QiJGvi1M/uJwCdrFoZgJE/whMP4AjwK9CcwVDB7jx0FZeN24YFizcMSj8ZGBRXkBbXNUUuFTgyI+ZmSP1jWS+ow+ddvxtlp0V7fzNhmW31rj/VjCgW8XifBuhQ2vVKJJ///NsqWLYeyo/rfxDTr3sfv0z9L77do3l42Vh7qpG701rVpeVP/weOi78w6ZqBtjbygQznV6qmTKs23q7jh1ayqgRvW3/gnEE8KBufspY4XnTDYLjLrxLfjdupSEB/FMgL/Rl2Q8cYuMAmnuHdBAF+LNZiY0rqM0crKClOX9DrQomQcRWUMswAq3bMDQ5I39RCBhl5ciOTevK/35bLTuaGInffbW2nKOz83Nj037bmxi0Gw0Y+aUB/cIzQG9n3JUb1KgmSxZ+FQJ/d9qliXwegQnKyPlXVdO4Xl2YNt9Q1wDAhoFUnlRcv9my/8rXZHlXiuN3c5PW7beTQcN6SfMW9UudAOsNA4IxRecD5gDGPQAmWMT5NiewYaLAkda5QYMGFjgiRrEfhTJr9mAdNygnm4uAlrAVC8U9mLmAOZsxVOdA4jO2bt06a+7a4c8Iz9GcOXNk+PDhdkOH52n58uWhtR/H9+rVy8a+xmMm08zE8PK5v50GnAZKNOAAwBJduE+FqwEHABZu2/um5izocJUYN25cyJWYwrVq1crGCDzttNOyvqObLuWwg4+BxYTDLjGCQQVYg8GfamDqeMuJAaGMMS0HLA8YR7h+5ZuRF69e9DieQdyg0RGMMxWYBBjCGFi5XLRTPliluGt7M/u2bNnSPke5LJvqKtPvo4Y/Ku8DzkSRdfUry6ZalaWKAb+2GBBQBZBl1GWHy0EddtSv7DtZe4+5YIas26CB8URaGODqh09XlmLaweeqbxhjq34pToxT6iJb/4B5t8nE/jMdyX5T1TDq1vPRvI7vs5ece3YXCzBjtHrd7aPF2bxo1MPy8Rc/ldzKVCceAJATKEtIvJ/1S/MsVQT4A9TzAlhbf1/TyLDYiopPxPVXmYEAq0WbKsi6OGLpE/tv3eZN8ttnvxugraQttAhZeTf13NGw/BpVriarfvjDgFwmpmMU2W2XplJxw2b5dLmJ6eUpb1FRZflrvUnsYRJ8qFjwr5p5KsJ1a+63oX5VPSz6u4kJWe3ndSYpTPRD0vKLKV+7DjsY4O9wadK0TsxLauxN5iqNocYJbIQw/uVD5nQFjhhDlXXLHAhjDMAzCHMgdYjlHkxb+RXEjPkAxvkj4CfAmoZaYd6DsUm9c7FRR3vwPOEB8/zzz9ta8EwNHTpULrzwQnnkkUds7GvGfRXKS3zsc845x3oa6Pfu3WnAaSA7GnAAYHb07O7ibw04ANDf7VNwpXv//fdlwoQJ1p1Cd+dxcxk4cKCcd955gXVPVQMLgEl3rWlcDA9AnEzFa0OHxKjB3Vfvi6sXi1Bo47lYNPv9ocbAop1gXKigM/QF2yLbCUMwdjAy1OjBUAWQpA2zXRbVRy7e773rVXns3wsi3hr32z+3MeCLAbRqGq/KdQbcU2m/e3OZOOJY/bPU+wUjH5LPviqOHcUPu29r2H8GBFKpXWQSeqzZKJHi/ekxvHvZf5UNgASkWMEAbAP7HyzdD9stlOADgxEpj3F03ZT/yKvvfGaPtf8lAADqSSUaMN9YsAqXYPMtoN9WRp8eq++4+q5pav4zz1jF9QYk3BoHEDfgpluK5LIBh8rQ+5/TwyO+a+bfNtXryxdfxY6fF/ECKXxZZNh3u5h4fjU2VzTMw19NnymffbhHm21kjUnmQqy/jSZbc7i02r6BfPlpMRhLjL+/AP8iCLrbVKec+H+m/Yt+MXH/ItwnwiWT+qqCad/2HVvJwMt7SqPGiTHamC/YaCA0go43FIJxhvGPVxCZZiRtINyI1ol5T4GjoG6eFJJ7MOsnWO8A1CrEQyRkSa7c1QEjCX9DgjtdW/Xp08dm+8WjQgFl+tSLL75oj4MlqHPA0UcfLU8++aRWx707DTgNZEkDDgDMkqLdbXytAQcA+rp5CrdwuOhMmjRJ7rrrrhAjgZ1tdk3JtgZrLYjCYhC34C+My6kyuagHMecAAsk4qAvHVOrHIhMAi0Wz3gdDh51y7qOuhqncI9/PxbUdIBAAlXZDMBw1YUim2Zu4JBMjzhujCsAY4yLT9/Zj286f9z+ZMObpiEXbULuSbDAx5xqbmGy/rt5UzG4zR1Y3Lrn33dJP6taJTFu7fupcmffOp/aa2zWrJ999UpKsYgfDBvzuk59DBlvEG5svvey/igb8M5w5qWzi/F1z1VHSoP5f1t3QG6cRo7U8d/s7TYbih59dVHJL8/gZ7Ck5MWNB5bWbpeLaLbK5ZiXZTKKSKLKxRgVZX88gWbDguKcBkyqt+0v2NOEBJl99rHWh7jPmfln5R3RgDfZfHQMYffT+d1Hukt6v61QzsRNNTDT5c5N8ZVx7168vYXTGuhMZeNvs3ER+X7nGunavWxs5Ht/2O9SWrz9ZZeIgmiQbuP1GkY24gJtXLKn82wapYsqZCaGt9u2ys1w0pKfZKCtOMpPKfdgIAXDB3VJBCx3/mEeCkCQKph9jqDfWba6Bo1TaJNK50dyDAcYAbIPsHsxzx/xLG2oMRJ473H1ZM+VCWAs88cQTctVVV1nPDsqAJ8XNN98svXsXh3mIVi7WtcQDnDFjhtx///1CtmAnTgNOA9nVQCkAsOeIYMYAnHujVRpzNOO8E6eBRDXgAMBENeaOz6oGAD/YYZ06dWpo9x4GwimnnGLdg3HfCaKwsIVpARCIoaXC4haALhWXKwK7s2CG9YAAKDJBtGrVqqAYY6rTVN8xPFgwhCcMgZmKGzcB2dMB2mo5YRPA2OSeanjDEOVZL4QELaqH8Pfvv1slF5x5d/jXNlfHGsv+E9nOBN37hgwg5plHeh3YWPZu3dAawYAW4cHh737kLZk5uzhoe5sWjUy2YcP6NH2zR7fWctEF3eT2f/xX/jt3SZl7er/YbECfLQZotFl0zQ/ValSRq4ceYuIV/hjavIBBipGIMR4P6/apFxbLlPvmldwmWQDQMCGr/L5JKm11iYYVuCkGCLi2QUXZXM2w/wzoR2zAIvN+3H67y6Azu4bKcs2sF+X5RZ+E/vZ+gP337do/pOKPm2RNFEDNe3xSn037tKhbW5rXqClrflkrX335C02WkFSvXkVaNKota35bJ2t+Xy9/mPdo0rBRVfnlp3UhV+iIx5kClBf/r+K6TVL064bkgdyINzaPeqWK0qnbrnLR5T3MxkC1KEcl/3U0phksVvpUeWB28ndO/kxAGsZrABcNeYE782677Za3YyhzBQxHDMJI2YNpqyC5B7Muwt1X1zFsWrL5Fe8YmvzTE/lM9Et5cO996aWX7EHMJ1deeaVcccUVQhzCeIVNWVi1QWWfxltPd5zTgB814ABAP7aKK1O2NeAAwGxr3N0vKQ3gbjF9+nSZPHmy3RHmIoAuuFEMGTJE9t1336Sum+uTdNEOEOiNDcbuPeASMcLiZeuxUMZVlCQDKgCJLJoTWZzque69tAZgWsDehBWorEqOAJyjrVKNI6VxGnkWlDFGu8EYy6fMvqW1Gv9f9JV+fW+TPwxg45VN1SvKukZFsk+jhrJ80TeyvnFV+3Or5nXk5CNahAAAxgvaiLZSl/vnX/tIJkz/r2y/TT359uNfpKoB8vqffqD0PrK9vcbGjZvlmuGPyJIPv/HeMvQ5xP4zAJRxFpZa9arJeWe2NmyVYoYcYB/3a5kg6/bt97+Uqyd52I5JAIAVjZsp4F947EALAtYyTMAwthp1+bNZRRMLz2CgxvW31oaKctWp3aTbAbuE6suH2W8vl7GPvlLqO/3Dsv+MJj5aXuI+r7+l8m44ibKzce2tV6GK/PTtb/LTj9GTsZR3n4YNagpPyEbDFPzL6GiVYQBGE4sraqxE8/xFFfNbrPh/FUwMwarE/YtxiajXjvJDRZN9uHOPNnLBoMOkRs3iZz7KoWn5mjEJNiDAGslDVDROaiJzlZ6biXc21oi5pmM0G4bMgZQvnRs1mSh7uq4ZDbSFNUcYCz9nD2YDjHWMl7UJ6Ecb5sr9HDBy/Pjx8s9//jPERPzb3/4mN910k91YLZTnKl3Pp7uO00AuNeAAwFxq393bLxpwAKBfWsKVIy4NsDh84IEHbJxAdmNVunbtaoHA7t27B3aRj1EF+ON1uQL8Y8HO7n20xS+GDow/b8w6XIlZMMN6cJJeDQBEAdbSVhpTijtgCCtom8jOPmyVSJl9iVGVK7ZDejWWvquNNGDcBwtL4kBx5bWNqxiAu0gqrlgjBrOSDQ2KpGpl4/p76+lSu2aR1S2grTe5AUxK2uqn1Zvl0jFPyG7NG8qaVetk6CXdpU3r5qUK/Ntva2XooJny/bclTF09YLNxCd1SVFGqGnCxUZMacvwxJIsBrhLrKg7rL5x1qOfGev/q21/l3CtnFR9iQKOEgCPzfFYy7r64/RbzIMveqVbtqlJvpwbyydcl8Q83VjfZf2sbN1dzv+aVq8vNQ44R3KLD5buVv8vx42eGfy2w/7aY8HifL/4p6n3LnBTjC6NW2cbEYKxhLvrLD4apt8YgkykKmX5//+kPq8+qBkD7+ccSICv80harU/CPH1FMFIkZ/8+cV9XE/QOQTYdUMu7lhxzZTvoPPMSwiEzimSwL4x+JkgACAdtUmKsA2ZivcrHhRMgEb5IdQBnmTcbReDfRtC758h4NtPWjezDzIIY5YUuUtclGDaxN3bDJdrtQpoceekj+/ve/280/7g8TH3dfsvk64C/bLeLu5zSQugYcAJi6Dt0Vgq8BBwAGvw0LsgYszJ566imbOfitt94K6WDPPfe0QCC7s0Fd9ANUaOw5ZYLBJMK4ArRQ44r4RriKEiMHowwB8AP4AwB0knkNwAwACPTG6cPdEyOYVzTQlpIpkAjbAeMVoZ1hi9HOQX1+bUUy9N+9M0wikIdKEoFsNm6qa5tUkd2r1JavPvtZNpn4dhvrVpH+Jx4gJx69T6gUjBeAFfQrr8v9X4ZRNvM/JoZKndoG/OtpDM3Iblxfr1gpwwbPMm69JexDZf9VN4DfttvVlN6GbYjAssFITAV832AyEx/V/87i8puuHTcAiMvvH8bll2QfEaS+Yb+dedZBcmiP3Y2RvVnGmBiIr7/7uT1ybT0DF5o4cns1byqThvSRoiqRk11wcN/xs+TblcUhBvQ2O+3QUP78bo38EoNRp8dGe29Yw4DotWvJmp//lG+//s3E3oxcj2jnx/qeTL9ffvS9jWNYr3Z1+e6bVVEPt3clAYoBkazEAP/4fWMNE//PsEcjSZXVG6TymtTj/lWuWll69NlTzh7QTaoYENAPwuYTLqfMQTpXUS4YZoBv9IVMgySEaMDVF0BS50HckumDhRgrNdJzgV7YrFLQVvXEfENMW+aqXLoH47VAkhZlljKHwnzPFWsT/SxZssS69r766qtWpTxLI0aMsOvLXCUeidS27junAaeBxDTgBQD37xHMGIBvP+9iACbW6u7ocA04ADBcI+7vQGmAhdq8efOse8azzz4bKnurVq3kkksukX79+iXFwAldKIcfYsWeA1iCKahGFywjFsypuqHmsLqBvjWGMOASTD7AJiQSaKuVBISCraIMQoxkjB0YY4WU2Vf1Ee/7a698JBNveCZ0+Lr6lWXbRnXk54+KmWwba5m/d2okd046NXSM9wPjBbqnrRS0XbT8V9m3XeMQaBtN/++/95VcO+Ix0+eK2xf2X1UDOLZtV0+6HNjUAvPpdNc+afA9snKVcU81aFQ8AGAFwzArMuBfhQhEs6ZN60j/Cw6Wjp128qpDNhgX59G3PCvvfPCVYU5Wkr6d2sqgkzuXOibSH+OMC/BTxhVYBfZfdcPWW/LBt/pVfO+mPVo2qCdNiqrJasPG++br6KBcfBeMfBSZfpcY5mg1A6I1bVxHVnxRwnwMPyNR8A9mYDT330prTdy/VakxF6uYrMNHHLePnNG/S4hdGl7mXP8NY4uxD4CJ5EkqxLQFCCTxRiKsaD0/1jt9mXvCftcsrGyOwRgDAHQSWQO0DwYwoK0m1uBIwFraKpvhJtjsZB5U7wXmQcBIWJuAgLkQ5uQbb7xRbr/99tD66sQTT7ReJ+gn04B2Lurs7uk0UEgacABgIbW2q2s0DTgAMJpm3PeB08D7779vF2n//ve/Qws3FrMDBw6U8847L7DBvwH5iD0Hy8HrxkgDwRKD8edcRf3xuGKIwojh5TWuNPYcwG24uzbPKG0YhKyaudbyd9+ukgFn3W2LAQNv/TbVpMGvW2TNn8XMvMrNasiUm06R5s3qlltUQFsACwxhBW0x7jTLc6T2eO7pD+Sft7xgM/9WqVskXTo3kb3aN7EGK4l2AH3TJZdc/5gs/fj74my85Vy0knH3rbymrMvvdts3kAsHHirt9tw26hXWG7bhdbfNlcMO3EUO3a90vL9oJ5EEhGQgKnvtso0sXfRdiIGl30d6r2Lcandu2EBqba4k36/41biTRo/BF+n8RL7DHbv1jo1luYnhWNm4/G6/bUP5/JMfo17iL5rPsCDjZf5xIZMuRTbWq1rmmqnG/atqksn0OXFfOdUwNoMCOgDKER6BfuWNRQuYQ/8A3IkGsJdRYIwvAGkIAUJsYIR5ENCI66ezD8YoQuB/Yl0RKaYj7Db0yJoiUyAc4y2bMKxpdBMTABLwNldMRMo0a9YsGTlyZAiQbNOmjdx6660S5NAygX9QXQWcBtKsAQcAxq9Qxul//OMf8vTTT1u7hvkbogKbItjW6pEW/xVLjmQN/p///Eeef/55WbBggbWNYIHjPQODv1evXjJgwAC7gVhyVvRPXI+EoQ8//LANJYGXHHMZ2dYHDx5sPauin114vzgAsPDaPO9rzKJy0qRJctddd4UAM4z5c889VwYNGmQN/CApAaMKAJDYOOEAIPVgwdyyZUvL/nPGjz9aVkFbJk8mpUhCXCMYYxg+TuLTAH3htONvs664m+pUlpaN6srXn/xsTy4y7K7zLz1UevRoF9/Fth4VjWmLGz2u2GQ7BYABdICt8vTjy+XTL36Xw3q2kM6d29q+lwlDeextz8t/3/g4NgC4JbLL7867NJGBg7vLTjs3SUgX8R688o+10vv6++zhsP82/rFRVsRg79WuZhIxkC3bgJRfGlft9SYBR6aFuJDbNKwlX3z8owGFDOi4YxP5eLkBVCOIZf0lAf5xqS0G0NxUO4ytZJ5Tkn5U3Jp9OcIto35VrVZVOe60jtL3lP0DA/xFqgzgHEAgIJMXYIcNCJMqGRd55j9CJnBNlVwniNByBPWdMZWYjpo9WOvBWoLNENoq0maIHpfoOwAx7r46L2JQYuyRsCwXQDf1Z/OYTL5vvPGGrQ5rKuL+4UWSDsA6UR25450GnAYyp4FSAGD3gLoAv5B5F+DZs2dbLzrNxB7eIozbAIMQGBKVDz74QA466KBQ2Ido57NOuPPOO+Wkk06Kdoj9HmLFkUceadcHkQ7kOuQP6N27d6SfC/I7BwAWZLMXRqVx8ZsyZYpMmzbNLnCpNQysU045RS699FK76PSzJliYslhmYNPYOCyQ2dEANGIS++WXEle2ZJNQ+FkHQS8brnHLli0rZbBSJ55DGCu4/abbNS7oOiuv/COHPSLvL1oh1betJeu+KmYAHdB5Zzn3wm7SxLi6JiuAFAALgLba37gWO5wwjHQRtMWAbrVq1Zd99mmX0fACdz/ylsx86t2oACAMM7L8VvS4/Lbdo4UMurS7ea4yDyqffrPZZf1+pXTcdVt5770VZdS+TZ2asm11E8/v17Wy4suVaY3nV+ZmYV80bFhTioxefvyuOHHLzsYt/NOPShJWeA+34J832Qc/mrE3XtlQ08TjC4vJV2XVepuEJd5rcFyNOtXkxDM7ydHHd8gJEJJIWRM5FlY0cxUAk7rqcj6JeNTltLyNKzZUlDGmYCLnwxhLBkhMpPyFdGwm3YO5NsCfJo5hLcMGC+FachXvFpbqmDFjZPr06SGQ+tRTT7UhZQCWcwFIFtLz5urqNJALDTgAsHytL1y40AJ0jNtsAF111VVyyCGH2BAfDz74oB0zuQogIOy9RJnbr732mnTp0sUWBCAQYG7fffe18euZIx577LHQuIyNBBhJ4qVIwmYj57JBj+Dxd/LJJ9v1+UsvvSRjx461a3rW8vPnz5e99tor0mUK7jsHABZckxdehRkcWOBNnjzZuvuhARZ2Rx99tA3ozMDhN4FtBNOBdxXAIkAjb1ZR6kYSCmLoABgisJEACXkBNDnJvgZoC83sCw0dwcjBvcoLLtE+tBPuca6t4munu++cJ3NeWCy1K1Qy+qwi55lsqB32bxXfyXEcRdthGMIkhhnjFdpv9913z0qSnWdfWSqTZ7xcHAPQWwjzudI64/L7Z7HLL2NZh/1aysWG8dfAAF/Zkltnvy7vf/G9fPPZL7Juw2aBQLdTo/pSzyRW+eW73+XHH0onCclWuRrUL5J1Ju7eehN/D9lh+zomQUzZDM78ZkdMb7IP+2X84F+k+H+VTMKPIpP4I16pVb+GnHbuQXJ4n/xelALcMU/BClQwHR3FcjmlL7KRx8Je2e+5ZozF265BPk4Z7IC23vkqVltFqy/XYo3CS8Fb2NWAt7lK0kKZ/vWvf8k111wTAiTbtWtnXd26devmgL9ojem+dxrIAw04ALD8RuzatauQAAm7hTj7nTp1KnXSxIkTZdiwYfY7xtHRo0eX+r28P15//XUbXoFzWVNHkieffFJI6Mk6ALdjbOJImzKjRo2S66+/3l5iwoQJMnTo0FKX414HH3ywzS7P+8svv1zq90L9wwGAhdryBVhv2AczZ860u7vED1JhoBsyZIgv4ryw2Ibxp7vklDGeGHHs0sCQ8MYzg1XBLja77F7QUOvt3tOvASaqcNYm7UAbtDRu2kymZPylrXDrVoNI2wpGTCoxNdJfI/9dcd5LH8njj78rnTruJMee0MEAp9Gz1SZTetoEw5cM2zA4wyVbbbVw6dcybOxTpROAmOeryh+bpdKGLVLRsNY6d9lVLrz4UGNIVw0vZsb/fnXJ5zJz7iKp9PtmqWrwrq+/WGnAgpIsyRkvQIQbbN+itnz3xSrZsrkYxGvRooZ8+2Vxhu3ww1MG/8wFucbGeiWbLCRiwfXXRBEsV+o2qimnn99VDuuVmMt6uRf2+QGMkSTiAQgE3ONvhH7FJhdjIMAQm1swxhSE53fGUF6ONZ2dRqZtknUP5lzWMbShgresQzRJSyRDLtO1okzvvfeedfd9++237e0IxYERSjwrtwmX6RZw13cayL0GHAAYuw0YGzt27GgPuuCCC2xCpPAzWCezaYKHE2x85vJMhMLp27evPProo/b27777rvG82adUUQjhg43MmoKYrYsXL44YB5hYgnfccYc9l/rtt99+pa5TiH+k13IqRA26OgdGAyzuzjrrLDnjjDPkqaeeskDgm2++aXc32OHYc889LRDIjkO2XVJYIBPjD9aYCoMqsRXiiRHHwrp169aWIQh4wYuBkXcmO+LrYDglStPWsrj38jXABMQOlRqsnAEAC2sT5oQKxi07XrRteFvxN20FYIhh4qSsBnbfYxvZeZcjpPm26XVzxThkEUMbAqgjjAO0H0lcANfpS9qvaCtN7kJbpdugbWay1XrFuvyaLL9Fhvl42BF7mKy+XU18qrDYc94TMvx5r5bN5JY3v5d1WYjnF09VNNOvgm/Nm9eQbwz4p397r1Em2Qc/mvZPVLZU8VzduIYX/bo+4v28163ftLacc1E36dyttffrgvlMP2Fu48W8p3MUYDv9ixcsP2VOoxjGROKluo2s7D4mtBUxUHkxJtJWjIO0Fe+8+A0WO0aYjoFscrHJqolgAG9x9WVeywV4y9hOuJRrr71W7r777hDofPrpp8u4ceNsrEMte3Y1HNy7MVdiSPN655137EtD0px55plyzz33JFy5F154wTIzcQ9kg5T5l77P2vywww4T2iud8SgTLqA7Ie80UMFM+7yCJJku7xNPPBFSx9lnnx367P3AmI4tjWswnmq42vbs2dN7SFo+43asACA2cjgAyH2xvRDGHcoVSbD9FQB8/PHHHQBolOQAwEhPivsurzXAAHHsscfKMcccYynOLACfffZZISgpgwSLRII/9+vXL+MGB2ACroYsrJUNBkCEsdOoUaPQgjreBgHkhCoN2MfiHKYZRhaxzXjhesNvgIpuwRuvVmMfh2EEa9MbmJ62ow1jLVa9bQXwS1txLdzkeNFGGEzJPAexSxzsXxs1Kg2MpaM2LCBwM1SXe/oGRi3gn+5qAthixNJWsJcIYo8RxAsAEOYSgGC0BUii5WzSoJbpo+YsszitbBh/NTdVkKP+1kFON1lhK1WKvMhJ9B6pHF+7ZnWjj8aybPl3qVwm5XPRxW47NpKlC1eEwLdtmleXb78qC/7ZdT6q82b6pQRJgH+cs7layRKqinH7rbiVeRheKe7buHld6T/oUOl44M7hPxfs32yMME7Sz+hXsG5h6iv4R18iPAJ9LxfAUcE2TISKA74S74n1BeAMYyBAHyAfL35nw4u2A8QFdEMYEzkvV+At7r6AfqzrFJAkBhSZLTt37uzWQRHaOp6vAObSJWyaAjTg8hcuhAtgUw4QADdEF78rXEPub6eB9GoAAB7BFu3QoUPUi+NOq0JsvUwAgLoW4D6R1gBaVn73loe/vUKoL7yrWLdTVicOAHTPQAFrACMf919egH/EDiC4KYAcSUJuuOEGueiii2xA0XhYeImokkUpC2ji4qibIcYQi2sy76UKzjFQAkhgPAEmAS7hUsUOLS8Cp7c0QCCL81TvlUi98+lYDFUFb9XYQa8YtLAi4hXaSuMAAibxTLDoZVHMi0kYIJDnIl3gUrxly/fjAFwxLugjKvQJ2jCSK7a3rXBvo18BGgIgfvjhh5bpSb/DEE6VRQywVbdmNamxpYL06dNOjj9xX9/11b3ab5tTALCmyfTbuG51+ejDEuZ002bV5fsVa0JgoLarhSOM27QF//RL3pMB//T8rUBspT83SmUTlzFcuGez7erLBSYxy977tgz/2f29VQP0ITbBNEkIcxJjKptizJOAg8xlvHIFJLnGKtYAYyDtwBjH/ET7MBbqRpjqiXbCJYtNx1wIzw/sNLL74vaLsI677rrrBLc23djJRdny7Z7MeXigzJ07N+GqMXf26NFDcO9D8MDB7Y+1MM8a48Irr7wSYgElfAN3gtOA00BCGsCtF2HjLdY6lj6voufo3+l6p++rMJ+Ey9KlS0NfecsT+nLrB+pBfbD1M1XW8Hv6/W8XA9DvLeTKl1UNAL5MmjRJZsyYEYpbA4vr3HPPlYsvvtjGKEqlQBg0ynTQnQ0WojCLWFSz4MmEsBhm95v66S4492GRDhAIuJSpe2eiPrm8ZiTwFj0yubArniqgSlthENNWxBNUgTGooK4zXlQryb0r8xbjFX0jsPhgquCamIhgwAAEAt7qtVhsYCDTXl7370Suy7GLl34j7XZvkehpWTt+8ZJvZPjVj2Xtft4bkelX1m6QVSuL3bX5bZvmteTHb/+QLZuK21SPt3+FJ/sI/Vj6WP26vHfOIv5fBZMApeovZV1/m7dqKAMu6yF7tN+uvEsV7O/sxsO81Zi3jJ3KvAUMVPBP2fH8DkBPv8qE233BNkQKFWdjESPMm9hFL8dGGG2VTRY7YzDPE3H97r//fjsm89zAMGNTNx1ztNavkN/RL3G0eKFT1iusY5FEXIBxI6SdcPt/6KGHbHK+SHqlXVl7xQIkIp3nvnMaCNcADGXmGaTjYSOkavXE1nzh18v23+vXrpK3XrzR3pZNDuy3WIJtGa/gMaabbEcddZTMmTMn5qnYxzDBDzjgAHnjjTdiHpvoj++//75lINLv99hjDwvehV+D+7711luWKOFNWBV+HH+Tafjpp5+2P1FPxpxCFgcAFnLru7pH1QALyClTpsjUqVNDMd0AXU455RTLDiSQdSLC4gWAAFdRjB4ENhfMLl7ZBHQUsPCynhy4VH5r0oa4PtGGXvAW9zUm2Eyw85jQ1Ajm/ghArYJLOlGXX3p3BBoASGDxh6shICCiboipGoYwYGgrXO9ZsCAYnlyXPg47NN9k48bNclK/O01/KJssJZN13aZZLVn9/e+y3sO623ZbE4j6299kowHkvJIJ8I/rby6qaFyAK9mkH17X3+13aSwXDukprXdv7i2G++zRAKx32NMA5zquARYxr4aHTaCf0qdgArFoV6E/AS7RvzIx9up93HtkDdAuxGSiXVRoC+IMM09iFKowT2Fwk+Qlk2sdnis2b8kIqeEccP3C3RdDkfHYSWY0kAwAiPtely5dbIHIKgpb04nTQKY1UAoAPOSqYAKAL42NW006x8ZzArYvm2zISSedZL3iYp3HmI9tS0IQvGDSJdhYhGhYsGCBvSRx+/v06VPm8m3btrUbUJTDG4apzIHmC+rDJgMCuSJX7HRbAB/8VxLAxgeFcUVwGvCLBghojasIac6nT58ukydPtsDBfffdZ3crGYjIHBxPJiEYd4BGAG8Ii1AAHICjXOxAwJwgqDJAJAYYjETYFpQRo4yyAVikwlzySzumoxxMnrhN4yqqO0wYnBifLQ17MpMGDcYwCUNwh8HQ4oWRA9DEZya9fAWX0tF2eg3akIUNbagAPEwCGAu0YzoABIxcAAz6tRew0Pib+RjTsUqVSrJH2xay4L0vVdUZf29hXHx/WLFa/vLE22veop6s/OGPsuBfpHh/WsKtgLr+mdC7OXdz1YpStKok7l+r1s1k4NBestPOxYvnhK5XIAfTDwGH6Ifq7ku/gXnrTSLhVQfjK+Ms/ZQ+zNgHuAPjjIx/MAg1hAIbWU4yqwHakPGN9YJuojBP4X6loVJoL9Y9zFG0GZsjtBOAIWwV2pLQFukSygT75PLLLw+xRDDuAALPO+88xxpLl6LTfB022BHWpHjYOHEacBrIrQa8m2zxzKdqwzLGp1MYDxT8g1EcCfzjflreRMrKeekuL9cMmjgAMGgt5sqbVQ2wsL3ssstk4MCBMnPmTJs5mOx27EbwIn4gvxPDJHx3mQXp7NmzhSxGKgA2gDnpXPzqtRN9J8YZMRUojwJKXnCpWbNm1vAKZ2Qkep8gH68BqL1u07AY0Fk2AVImWVyMMawAbAFumfgUXII9AxCI0RP+HAZZ/+koO22I8Um8KgT9wNgEqItn0ZBoGbyAhTf+pjemIwZwvrjdt99z26wBgNs2qybffvF7qfh+TZrUkT9+XWsWdMWMTm2viJl+Qz8m5/arp/Neee1mqWTYhru0ay6DhvWS7XZo5P3ZfQ7TABtgH330UWgjDCYzADzjVjwAPMcwf/KiTzNnMf4BJAIssXnFnEXfctnuw5Sfpj9pQ9Y/6B/RuEqMp955h8/MRbzYcAEIZN5ifQH7hhe/Adym4h4M8Af75O9//7tdn1EmnpP+/fvLmDFjUro213KSOQ3QbzXpB+tnXU/BnudZ4Z3+rN9nriTuyk4DwdVAPC7AidTO2990ky7W+eoNlU5vpLFjx1omN/eFZDNt2rSoRdDyJlJWLpbO8kYtnM9/cACgzxvIFc8fGgAoOMtkCCZeCaAemYPffPNNmTdvnn0RnwBG4HHHHWfZDSNGjLABkVmMAty0b9/evrPT6TehbgousbOPYQW4BFODFwt0gCdio3kX+X6rRzrLw+4QRiX1V8FgITlELo1LDC4MXAwuDB/cbojBBEDJC7AWg5qFczxGtdYtH995hmGpeNsQ1wae9WwA8OgfkI+2APwDtMXtANc4ghBTNgxgXpkAIrPVpntlIcadUaU0a1hVvvvij1LgX/0GNWXTuk3y+28lrqEW2ouU7EMVYkCDVKVGzaqy4/YN5WLD+Gveon6ql8vr8zEQeNYx6lXoF/RDXbzr9/G+4/6LyxHjsQJKGADcgxdMNMbJaKzCeO/jjivWALqFteltQzwFaMPyxi42GmFGs2nGWAwYyBioCckwxHRDJBE2PezDO+64w8b1U0CyY8eO1t0Xo7FQ1ipBfUaJ76XsHdbPtOGoUaPk3nvvDblv82yxyX711VdLt27dglpVV26ngYxpgLkUeyBd4rVv1OMp1rU11EO6iCKM6djPCKzyZ555JuZ6XcubSFm5drrKy7WCKg4ADGrLuXLnRAMY9cccc4wNVPzqq69aRiADFLEPCDJ94403WiYCO90ILCPAs3322cf3C1LAJcAjAAmYFQAWDKqAFrwAL6lLPhtVGBUwSQBBYRcgTDC4qCWS2deemMH/eA4BlmDDAC4BBGJQ0V5LliyxBjdGFUZaIkZVBouctUvT99AHz68mDwAwoA3VRS1rhTE3whDl2eHFYolyYQjzrBGLkLKyiKPvZQOYTHfdWxrmW12TiXf16vS6gGg5q5pYe3WqVTRuvyUxxfitdp1qUsX0g59WFrOR+M722GjJPuwBqYF/5nayW+uGcu75XWWX3Xbw/ZhOlXMl9D3GUZ5xjYlJP2RRn66NMJjRAEvMS7BtuR8bIsq2BVxiPmMcZH5zkpgGNGYqm2G6pqHtaEPaMhFB/7QFxiqbVbQV6wo222CGAhLDrueYWOMg8zJrL9x9NQMkaxLWXmzSunZOpFVyd6y2HSXgOSNWIyCzVwCeX3jhBXnxxRcFVtDw4cO9P7vPTgOpa4AlQWrLgtTLkOgVMlheNuUgO2BPsLkWS5hnFQBk3E5VZs2aJRdddJG9DOvh559/3hJQYl2X+YQkIJSD0CCxkvix+YQwX7B2KHRxK6JCfwJc/ZPSAEY9O5O8cPW94IILLAioCxgGmMMPPzzkhpLUTXJ0EuASC3FACRboABYM9Lj/sGvL4pzBmd85Nh8EA5XJAfBPDR0mQhgOAG1+ZRN4wSXAPwWXYN3wLGJ8M0ECBibLtglK+2JEwGClzuoO4Lc2pO8Q05HniueNBRZlpdy8YNvStwAq/frMhT8PFStWkPZ7bCvzXittvIUfl8zftWtVlkqbtsjKH0sYflyneo0iqVPTuAOvKHbr5ju7Js4E+GcuXLdeNdln3ybStn1t2y5frvhYfvrlG9uvGCtdBnVaoFgAaJg3AHU0zg5MHth6zBmZeK7Rv85ZGAGASzCkub/GnuN3xkEYaU7K1wAgHW2ozIp0tSHtj4HJK9w9mDGR18KFCy2oe/zxx4f6Fs8VGyewwTSQO+0+YMAAufbaa+31yq+VO8IvGuD5Uhk/frxlA7JmJvY2MaphBD766KNy5ZVX2rUn7wDPbMA7cRpwGsicBlijssnCpgz2ULRNFcJBqBBOKhUhpBYedqzjWScA+sfDbKSsjBMI5SHZUyShHmxkIamWNdL1g/idAwCD2GquzL7QACALdGVizRDoGoFxAPiHMX///ffL448/Luecc44MGjTIGii+KHichWChTl14Af7BVMKoYqeF3VsG06CzzDAqYDsy0ak7CpMdzE12tIIEcEJpJyMWjBgFl5j0AAUxiAEyAZeUMh/nY+D7wxRwAPDU3UiNL8bz6UdwBmNamUvKtqXsACe8aCPaCoZnEJ5B3IDTDQA2alBV1q5ab0CC0hmGi4oqS5OGteWrz38OPZt/VTAfcfs1Y1ZEMf08UeFSrVtvI+ec11V2Mxl9ec7YCKE/0UaAFyw4GTtYqDJe5DvIXp4OAYsA3GAPIMwhPMfE+otmRJR3zUR+536A57wA/xj3cFtlHFRwKYggeyI6SPVY5kHaEEYlgk55tpkT080mD3cPpr1Ya5C1l75GbD9Yfeeee67ADiH0CgxP5KCDDpJbb701EN4VtsDuv1Ia0LmaL3nmiAM4Z86c0HzNuhNwF1f/gw8+2AIDV111lfW+4Zl04jTgNJAZDZB9FwCQPvruu+8KoRUiySuvvBL6mvE4WQHsO/HEE+08zcYQzD/Wx/EIZVWhPNEAQBKK6JiTSln1XvnwXsEsahNfGedDzV0dnAaS1AA7FCxGR44caRljXAaDfejQoTYhCIbHlClThAxnLGIRFs6nnHKKXHrppTYejv0ygP8xgGIAY1Tp0IFhpyyzINGqMVIBjdSgAGgBMGrZMrOZfbPV7Bi9ANEYVQpucm8mWIxyXFKDvpCm7TBWlU1AfXD3Y/EAyBYUoS/xPNK3tC6Unf4UBJD9hx9+k3MuuDdt6t5h2zry3RerZLNh/3mlcuWKssN2DeWzj38MfR0z2QdHJbjEqV6tinQ7tLWceW4XIdZfJGEcBFCifzEfIDx7ALa0V7pcXCPd24/fqTs7OtF5AQMet/tcM+4YB5mvGAeVkYgO2TChrdgY8eMmQbbbmeeY8QcGtT7TAKmwrrIVL4lnB1B98ODB8vrrr4dUALAOU5py0ccAAk8//XTXbiEN5f4DG8QA/QhZO++55x77Odp/N910k10z6+/vvfee7L333vpnqfcTTjhBHnnkEfsdXigwBJ04DSSrATwv1GX1gG5XSdVq9ZK9VE7OW79ulbz58lh7b+bceJhyiRSUxCIK+uHddvvtt5c5nbEYcJ541rjdQg5JZoOIcb5nz54WnGPdBBjYoUOHMveL9gXzArG92TiC2UcIpEh2DZsJEHYQ6kec2EKX/PDfK/RWdPXPmgYY9Ng96NevnwX/ABkA9WDDAQiyUMbwwY0Bg2Py5Ml2cMZAuu++++zABhD4zjvvZK3M6byRujB26dLFAmWAfxhYLP7YMYIZqLss6bxvOq8FaMRik5eCf1DODzzwQOumlswkls7ypetatA1AH88rE7UacQBN1J24GbhUqbGXrvtm4zoAmkz0JOJRwAxmD7t/LAKCBP6hLxYslJ+FD3VQV0l15aZveV0qs6HjRO7RtGkdaWZe6ZA2uzSWFZ+sLAP+4Wq8445NQuAfO5d/wfqrZJYxRn8RJQHwr3nzenL5sMPl309cLBcO7h4V/OM+jIMAI4SAwJ0boBbwAjYni0vGdxhUCoZFLFsefEn9MKbmz59v5zv+RjfEvN1rr71yDv6hYsZBgD7GQcqksVxhKzJf0be8DPA8aJaEqwCjlVAm6IH5gOeZxAyMRzpvJHzRJE5gHGT8njt3rk22xpwM+Md4r/MU4I+Oj0ncwp3iEw14PRFYM0cD/yhur169QqUO6to5VAH3wWnA5xrYf//9BRsPueuuu+zcEF7kSZMmWfCP7y+55JIy4N/LL79s17WM6bC4I8miRYvkqKOOsjYj64ann346IfCPa7LWZ8MIAYxkYyFcmNuoBwKb2IF/xRpyLsDFenD/Ow3EpQFYYgyMgCeAgAB9LVu2jHguC+fLLrtMBg4cKDNnzpQJEybYAYpYB7wwHvkd14dIOxYRL+qTLzEQiOnEji8GIGAnYAWMGF7syAA+xQrImu2qYJJS1Q8AAEAASURBVERg4AB6qcCGy3VmXy1Lpt55ZjGYYLoAlsHyAAQE/Fy8eLHVibLMMJb9LIDNlP8LAzirQYgh4bckLanokPoA2HrjBFJv+hgvGDD0Lb+xzHAD/s/cJUlXvZIB83Zt1Vj+9+G3pTL96gV3262ZLDe/IdZtIVa8P3tQ+c4NlQyo2GHfltJ/QDdpZgDARIXNAsZA2kOTUBC7ijh0vDTDKfHn/N63Eq077HZAad1EoX4wb2EjMOb4TZhjARp4Af7Rl5gLNPETY0qhMThxY4dBrSFM0BHPMs90rp5X1hF4ULBeYjORtQZxntT7APcwXmQWJrQKcaO8YJLfnjtXnsgaUAYWv5bHYPIeq89q5Ku6b50GnAbSoQHCK7BpBmsehh6ZeQ855BD794MPPih33nmnvQ1rbxIyJSqQZgD2WSchhNJiTYtNEk2wK3mFC953//73v+1cNmzYMGvTnHzyyXb99dJLL9kEUayhWY/dcsst4acX7N/OBbhgm95VPFkNAKIAeiXqhgBgMXv2bJs5mB0JFXbahwwZIscdd1zOFt1almTfqRvGFOCMlwEIAAhACrspVyAnBh7GHQafFzQC+AMALETBaKetYCwpSwmDj4U4i22/xTKjjADLLBo0wQeGISBZvrNBWLhEcmGkb2GsA2jkqm95+86r8z+WcRP/4/0q7s+1jKttE5NJ+MvPSuL6eU9u27aFLH3/a/tVyuCfeZbq1q0hRx7dXk48paNxI0wfWMVz6k1CoXWgb2kSChahQRYMAkInaIw46sK4ETS3e8qtCXhwowJ4UsEQYVMEY8OPYKaWM9l3kl6R8Io5QOdE5kJANZgYuRD6DhlfMeZ4vhD6DEAgxhxjHMAf8QGfeeaZUBHJRnz22WfbeIGsM5zkRgOssQCOkXhcgHn2WBsirKVx7Y0mtDdMIWTixIlyxRVXRDvUfe80UK4GnAtwuSqyB2CvQnRhUzOSAP7B2mMdHi4wAAEMkUjjASECGLcTkWuuuUZGjx4d8RTIHUceeWRo7gg/iHnigQcekN69e4f/VLB/+5vuUbDN4iruZw3gQqRuRImUE0OCDGZHH320vPbaazaODQubDz/80A6EZLKDSk1sm6AZidSN2Gss2NmhZTFITAaMYWjesCEBK2ChZcugwrDBsMPQAQREALaYrCiHH0CTRJ6fdB7rZZkBjLIgAmii3ViYA6rRXtl0/4pWP9zTMAg1GyXxujAcKF8hxO4CPAKMAJglzgrto31LWWbogr6XS33saTIBJyNNGpvMuusNszMK+NeuXQtZsmgr+FfB3MEwBU3nTeZWpu83kbP6d5U999ouqfPLO4kxJTwJBcC1l8HpR3Z0efXid0AjxgdeChpRV0CjoDKwcB9SBid9i7GQvsWLeZlNBvodAGc+hIYAZKOesP40LixrDdowV5t0lAm9k+UVzwgEXePWRVgVL9MZxggvyj9t2jS5++67rXGKexfrJyfB0QBzFvMabc+YwnMQbU3Gxp8K60wnTgNp04CNJVK+t0Da7peOC2WpuH369JEPPvjAJlsC6MNOYM7EhiIu58UXX+yLMB+olDKRPZ554eGHH7YsQDb4mL8BBrGtGXOclGjAMQBLdOE+OQ1kXQMMruxwQ6nGwEJYiOM2fN5551ljMuuFStMNAScA3wBwVADgMu1uykIyUmZfDD0mg1yCJKoHv70DkAJUsBj3MmFghQC2YehHW5xnqi7hGUW5jyb4wDAvZKFvAQRizKsog5P+lSv9DB7yoHz6WXFGdC1XrPdWOzSUn7/+Vdb8uSHiYTD/lhjmH3Bfuck+9Aqm/3ulqska3KXrrnKWyeZbx7AMsy30LWVwKuhCGYLCMmM8he0HCK/lZxxn9x8wM9vjQqbbj77FOEjfou4Im1ZsitC3/LApkowOGE9x2daYqdRJwc9czYk8TzfffLMQT0oTtBx66KHW4CSjfXnPFsyUe++913odACA6yZ0GAPF4npBIjJ9IJcPzhfZHYHd279490mGWSQSjCKFvso5z4jSQrAZKMQAPJglI3WQvlZPz1q9bLW++MtbeOxNJQHJSKXfTrGvAAYBZV7m7odNAWQ2weGIRzE62LoQxNM455xwb5wZ2T1AFwwOwAhdhNagAK1jE8UonWIFxAztA41JhQGC0sTDNBwZHpp8BmD2Ap7SXMu64JwwfgMBsuMQBQLLjDyCpAhAJ4BBU41vrke53YnhhEAEw6QYCzzwMV3Y7s83M+r975sujT7wXVzXbmJh+ny4x5Q7L9Ksnt2mzjSzbGvPPsv5MvL5yxQP+kZjkxJP3l+6HtysXSCj3umk4gL4FO1oZnHpJwDTGQcBtv41RACyARoBiCKAR4wCvXIFGqrdMvwNOYVwxDimDnHsyFtFeuWLMJVpv2Kdk9mWc0PmXcZzxNFeeBpTj2WefleHDh9uyUSd0insnzJJseQkkqkt3fHQNJAMA8kzCPqWvEQoHzxhc9bzyr3/9y3rF8B1uwHPmzPH+7D47DSSsAQcAJqwyd0IeasABgHnYqK5KwdUABuLUqVNtEGyCrCMYhWQOJtswi6WgCos8FnxMvgpWsNAH3ASsqFGjRtJVA6yCoeJlGwKCQAvPlZGTdGV8cCIGGolCACuUMUKxACtoK9oMEDedwjPB/TAk9PkA8MNQxeh2El0DABT0q/BYZoQqoL3QX3lsmuhXj/+Xdxd+KaOuLXbji3XWHgbcW7JwhWX2RTpu112byifLfzCupoaBhctvnOAfdWxv3HtJ6rG9YRf6VXAx5Vn3sswA1OhXbFikMhamo864zhBTxwvCM54SN5UxoJCEsYjNK+Yub3xb2oi2ghmY7rEwHfplDGczhw0xjZtKfD/WELkaTykTXgEEagcARHApgwlGkPlsb1ikQ8+Feg3AOsYIFdZexG9ESB7Qv39//cm+R8sECujL84DwbAIKExOQzYfHHntMbrvtNrseABhcsGCBHYPswe4/p4EkNeAAwCQV507LKw04ADCvmtNVJl80AKA1Y8YMywpkskIwbglgymKZNO1BFQUrMKjUMKEuyWQ3BVSELQYDSgXQA0M1fCdZf3fviWmAhThgBW6AGHBIOhmcXBMDG2NC3Y81zgiASDaAq8Q04t+jozE4AVIVrMgUuwZwZMmS5TJm3BuyGeAuglSuXFF22aGR/M8w/6LJjjs1lq8+/dnEzdtiHjQD/plxrzwhm+/Rx+4tp515oAEU0gtMl3fvVH6H7a0sM5haKiR2ob2y7XrP80N5YIxpeQBlMMwpSyEL4xSbIcxb3o0mxkLYmzDY/LLZBAN++fLlIeYm4DJJWihjpvp/ec8G4wPunryYtxHi+fF369at3ThfngJ99juAHu7X8YquHSIdf9VVV9nkeNGOgbH6xBNPSKdOnSKd7r5zGkhIA6UAwK5XmU2tYLkAr8MFeJ5zAU6o0d3BZTTgAMAyKnFfOA34RwMAZLNmzbKLo2XLloUK1qVLFwsE9ujRI7ALZ2VWAC7hyqgSD2sJEJHzeGG0IoAcmtnXgUaqzfS9A1Zg/MIKUoYeek4lYQgsQ5ib6rKNcdqyZXGCDz+yatKnzcxeScEK+gc6VgFYBQQgqQGf0yH0RQAjgCPu+/DjX8o3364tc+latapKozrVZUWUZB+csP32DeSHb1YZINjEQ40T/Gu2TV25ZcqpUsNkEg6qALZpnEANAUFdAN9gcLI5kmngBlALtpiy3GCew6AG3HLjaeknCx3xvHtd7zkCoALglgzdudAZfZGNFN00pEyMz8yL6Qy1wXXjFeZnAsjD7GI8QhjjCXly7LHHZvy5jrec7rjENJBOAJA7v/HGG5bt9+qrr9oNQY0zStK8QYMGlUoGk1hJ3dFOA6U14ADA0vpwfxWmBhwAWJjt7modMA2wiCb2ybhx4+xCSYtP3BQYgccdd5wv3ZC0nLHeAQ1whcP1E7aZCoAehoLX+EUPTN4ADhg7CIYNhiqGTi6MLi1vobyjd9ognMFJTCzaKx7jN5LLNmw/WCqF5mKY6ecGXWN4e2NwAiapuylugckIfREwGAau9kXcIpf/b6M89uSHpS7ZtEltkXWb5Ocffy/1vfePbZrXk99++VP+XGsSguDyW8G8yhHAv/GTTjTstOTqUM7ls/4zYyFhIOhbGgKCQgDWAiwBxqULuNXKAWYB/CmrjTEUkHjHHXf0XUxCLbNf3nnuFbhVVhtlA7ilvXCbzjRwy/14buiLgH/aFykDzDrG41wIZaI8uHfOnTvXFoGx/YorrrBgIPO7E6cBpwGngWxrwAGA2da4u58fNeAAQD+2iiuT00AUDbCoJvbK+PHj7a66HgbwMnjwYBssOdfxo7RMib5TN4xegEAvawmjAWMKRgrAnzJkYIhRb37L94D0ieoyG8cDAAEqAS4pa4j74npNu8CGCQdkYbRqgg/aG4HxSZw/DFYnmdMA7tWwllj8KkjA3XA3hWUWD3CrpQtni9EXAYwAjv738Y9y+fCH9VBp1dJk+l0RPdNvcRlqy3qTCfi3P4xrYKWKoXNjfahXv4ZMmHSSAALmo7AZAhBIHDftK4BJyrhNFrhVXcE6JB4b/VevT2w43H1Tvbbeo1De0V804Ba2La9MMfBI0IK7r7Ko6YtsiHHP8PE3W+3BpgOx3f7xj3+EwnyQwAF3X8qWq3Jlq/7uPk4DTgP+1YADAP3bNq5k2dOAAwCzp2t3J6eBtGrgww8/lAkTJlgXYXXJhIV10UUXyfnnnx/omE0YMximXuNXlacMFTL7ppsNo/dw7/FrAOMXQIj28rKWiIelCUO4GmAGgIM+q4AMuKYFJZtm/Brx95HoX1lLXtd7gFvaC+A2GmsJwx62mAL09EWABsA/7YubN2+Rk0+fbtz6N8juJtPvx4u/lS3mu2hS3wB5YuL9/fqbAf/iSfZhLlSjRpHcOKGv7LhTk2iXzZvvYZZhsIQDt/QbNj8A0BMBVOivAPe43msMVjaNAOEBg52kpoFIwC3tAxuQ9kpXbFoAfRh29GUVGKIAbNoX9ftsvbMp9OSTT8qVV15pn1fuC6sbd19cORN5TrNVZncfpwGngcLSQCkAsEtAYwC+6mIAFtZTm/7aOgAw/ToNxBUx1tmdJTYLrBB2p1monXjiiTJw4MCcZyEMhBJ9UkgYc5MnT7ZJQ5Qdh3vN2WefbWOnYBQEUQAbPvroo1JZaKmHgg6AFX4Juh5E/WaizJrdlIQhKsrOVOAP45SxBhfUaECTnuveM6cBZS0xF8AiUlHGLeOGxmEEKIJ9y8KZ8xDYYoBGkVz5rrtxjqwxgN6SRdEz/XKNWrWrSZFh/K1cZWKAGpAkHqlSpZJcO+Zv0m7PbeM5PG+Oof8A3AGkexm36B9gCYBJ+1q0StPOjKkaaoHjAW853/XFaFpL7nsAOgVuFWjlSjBtmbsAW5MBxADZWLPRHzVRS926da27b7rAxURrzJjAc4V770svvWRPZ24m7h/fOUZpohp1xzsNOA1kSgMOAMyUZt11g6QBBwAGqbXSVNbZs2dLv379QkZA+GUx6gAG2Ul2EhwN4II0depU+yJbIYLb7MknnyyXXnqpNRCCUBsYLxg3xDRSwcDB0CVWoLovYjwRH7ClcTd17qOqKX+8A0RjEPJMeoV22n333dPGgvFe231OXgMAtwBL4ZmeAWkBAflNwQaMeeYIGGjR5KPl38ttU16Uzz79MdohBryvItWrVkkI/KtoGILDRxwlnQ4q3LkJsAUGJsCtjvMoGWAdNiZu2OEMMMZU2GIAiCq0LXN8plxT9T6F/g5gB5OdPqRuuuhEgXbagXk6HqG9cfdVAJh2hkWdy/i3gMmEJJk2bVpobia5B6w/WPrJgJzx6MId4zTgNOA0kIwGHACYjNbcOfmmAQcA5luLllOfhQsXykEHHWTjqAGoXHXVVXLIIYfYvx988EGZPn26vQIG3oIFCxywUo4+/fgzzLkZM2bYBTgTnUqfPn1swpD9999fv/LVOwADbEYMW4wmJNxNVN0XOUbZjhwHGwkgsH79+s7gQCE5FIxT3Au94B/sIm1Tigb7hfbKVYD6HKrH17emT8Eu4uVtLwqtscVgBsbDFgOoevvNz2TWv94sAwRWKaokNaoXyerVJltwhfiYf5Rh4ODDpNcRe/DRidEAgBLAEgCTthdtAxtQGdL87nW9ZzOFOH+8O8meBugPMDBpDzayVGBhAgIC3EZjygHg4nqvzGpAtVwnauF5e+SRR+Tqq68OuSGzbiTO3xFHHOHmYW3gON95Jt5++237euedd4SXhlo488wz5Z577in3ShyD50c8cvfddwuZfJ04DRSaBrwAYKfOV5rNmGDNhevWrZY3Xhtnm421Ght/TpwGEtVA5URPcMcHWwOXXHKJBU4w5sjM1qlTp1CFDj30ULubTNY2Fpvs4I4ePTr0u/sQDA0A7ML4IxbgrFmzbJzApUuXCsxPXl26dJHLLrtMevbs6YtFOoZEeDZRdUnHMPIyCDCWMHwAITRzMEYwC2VeMMxgHURKQBGM1gtuKSO5iQLIYhRi2GrCEOLOAQ7yAoQACEzWHS642vJnyXHbg00Eq8cbz5HSAtADQMBciiduI/22Y6edZP8DdpS3LBD4hnz+6U8GPKwgRVUqy2pi/plj4pV+ZxzowL8wZTHetW3b1s7bCtxqZlpiw3mBd8ZU2GKAg94xNeyS7s8MaQCdMx7yUqCdeY9+pW0XHteRuZHNLhjxCvAS8xEAl3k+FwKQyXoC19558+bZIjC+s5l8+eWXuyzuSTYK3gxOnAacBpwGnAacBrKhAccAzIaWfXIPdhc7duxoS3PBBRfI7bffXqZkLDLbtWsny5Yts+wcQJZ43VPKXMx94QsN0KZz5syxbjqvv/56qEx77LGHBQKPP/74UKyv0I9Z+IAhwfOFa5omIwDgA8AjJhWfyxOugVsUzEGvO5w3AUU81ynvPu736BqAlYkBC8tI3UQ1qUA4UER7Afxh1HrjznE8bQ7g69oruq4z+QsALn3R63pP+wE4wDDTuHGUgfaCYQZYGG970fZvvfGpPP7ou7JsaYkrajx16n30XnLegIMdcFWOsuiLjIX0Lz6rMIcT64+Nk3jbS89175nTAOOlxnXUOZC7AajR70iupEx3AFyAv1xubjFmjx071q4ddazv27evzfjLeOCA5eSfFa/umAtbt25tN+m5YjIMwOeee87Op9FKBGvIMfCjacd9n88acAzAfG5dV7d4NeAAwHg1lQfHjRgxwi7eqMqbb74ZAgPDqzZu3Di7m8v3LCJgijnJDw289tprQvsS41GFhTvM0NNPPz1ryV9gF+EmSuwxhMVveDZRLV+87wAUGL/qJsV5xEiCMcjLAdnxajK+4wB00DXtiIsago5J8BGPmyjGJEAFILAK52t7hccx02Pce3o1wAaBuomqUQ+7COYmrvUIba3t5XXtTqa9vv9utQy//N+GYWgSf8QhXQ7eVS4fdoRlDsZxeMEeAvPv008/LZWohT7kTUBBe2mcQBf7zz+PCv0LBjv9UN0+vaVjTIS9mSvwljGCEDEjR460mwGUrU2bNnLLLbdIjx49HPDnbawkP19zzTWy33772RdsQNYybIYiyQCAbMi1NOx6J04DTgOlNeAAwNL6cH8VpgacC3ABtTvgD8LucocOHaLW/OCDDw79Nn/+fAcAhrQR/A+dO3e2bMAPP/zQugbjIgwIM2TIELnxxhut2/D5559v3ZQyUdtI8eFY7BKMHlZRKkIGxD333NOyCTGkYDJh/GIUs5gGlALsxIXRSWoaAAwiTIACuLgawlrA4ABkiEdgH/CC+cIziMsiIAbubrQXbEDaK9XnIp6yFOIxgA6AebSjsoxoO/oifcXLSOGzui/Sh+lf4e2lcefKc01stk1dGX3D32TE0IdNMoMNMVW/197by6WX93LgXwwtAc6Eh1CgDWCLwSIjJiztBdOM/gUwQP+iveizucocG6NKBfeT9i/GVZjs9E2vYLAyl9Fe2WRtUQ7WCrj2qvcAbufE/SPMiAORva2U2udrr702tQu4s50GnAYS1wDhxotDjid+bq7OCFp5c6Und9+YGnAMwJjqya8fibOFS0n79u1l0aJFUSsHOwvDATnhhBPkoYceinqs+yHYGsAQnDx5sk0aoiAAAPE555wjgwYNskBAOmq4fv36UGZfNW4wZGAZZSoYPQaTxlbC8EUwtDB8AarKAyrSUe98uwZgHYw/L2sPfQIa4XadikRqL66HyxvtlannJJUyB/Vc2LIAfxrnj34B2Iqe4wVwaS+ACfoYn1VgDXIt5hCuG02WLv5GRv39cdmwflPEQ3bZtalcP/Z4AwAXRfzdfSkWLCLbNiAfEitRC20EUAgY6G0vgF2AJReHMzdPFPMh4yn9UZnUjKUwqfmb/sX8qcI4SHsxLsaTjEfPS/QdIPKGG26wieHUnfyUU06xoURgkcbq24neyx1fVgOszRwDsKxe3DdOA6lqoBQD8MCAJgF53SUBSfU5KPTzHQBYIE8AC0k10I866ijLAotVdcARmB4HHHCAvPHGG7EOdb/lgQYAhqdMmSJTp04NxdIDCDj55JPtTj/xaJIRXAphd/FSIwKAEXem8PhwyVw/nnMoA2wlyqAGFudxfwAPgEhnzMTWpDLzMEYzDeDynGh7KShN6WgngCUHVMRuq1i/AiQQ5w/9qgAk0B+TZVrSXsQIpH8xZ6jAFAKoACCOBlQsePtzueG62WZsKL2l3aJFfRl30wlSt15qrGAtS7690y8AjBSIZ/yKN4QCjEHaCyCQBEoqrA9oL5i3AIlOMq8BgFsAXI1fSz8B9GGcU3df2ot2pr2UcU3JYN/hGgxbN53hEujP//rXvwSXVHX3J9HMP/7xDznkkEPcXJn5x8LewQGAWVK0u03BacABgAXX5K7CETTgAMAISsnHr1jIYeghJ510ko3nEqueuGWy6CQhCC4gTgpDAxgkM2bMsKxAwB6V3r17Wzeg/fffX7+K+a5uabhzKtsEIwVWAwZmNEAg5kVT/JEyEbOOhbUyZriky0QbXbHojOeAdtT4cAAFMDczDcQpMwZgyWv4JpOAInoNC+MXjHoABNw/+YwA0NGOyvZOVRO0F/HLaC8FNLimAhUAVJHYhS//d7lMnvif0O0bNKwpEyadJE2a1gl95z4Ua4C2ow3RMX0Tof2SyQpLe8EA5blQoIfrAf4BKgEGunAJaCT9wlhKaArvhgprLoB43aiNdFfGQdqLeUw3YphLScZDe6XCaud6CxcutPM8CeMQ5sZRo0bJxRdfnFaQMVLd3HelNZAqANitWzcLLrO5i5s/LP3u3bvLhRdemDbPjtIldn85DQRDAw4ADEY7uVJmVgMOAMysfn1zdRaaLBARkj3cd999McvGsZwDYANjxElhaQDGF/EBx48fL0uXLg1VnhiCxAskMUwk1hxG6ZIlS2zGUM1qCJMBpp2X1RC6YA4+KFDBAltdICmGAku5AihzoIqot0RHbADg7qssPMAbMokC5GQbwCU2Fu3lBSoAlDVhSCRgKWrlCugH2jE8UQt6A2gANIjUh9OhHphlgFQwzSgDwjhA32JuCWcbzn5yoUy//RWpWauqjJt4guzQslE6ipE310CH6JL+qO6g6QTiGasBlmCGKkDMs8GmIeO2c79Pz6NEOxKLkXbUjTEY8TDsEwHiYbJjxPLS8BaUkGvQvxJh11MmgPvrr79e/u///i8ELPfr188mDKPPZmqcSI9W8/MqqQKA0bQCqE/ylgsuuCDaIe57p4G81oAXADyw03Cz0VU3UPVdt261vP7GeFtm7HTW5E6cBhLVgPPzSFRjAT3eu5OvC89YVfEaGbGOc7/lpwYAVM444wzBCCBjMJmDCQJOIhleMEMBAo8//viQu9gLL7xgg4MDOEybNs0yf2CSABr5KVg4xgwGEi8YFSy0AbswgpctW2aZGRhR0RhL+dniJbVCJ7gXAroh6At94JqWK6AN99+99trLupgCLGFEM47BooERxXMGUBGLPVNSw8L4RDviXsg7AmiLjloaMD7TLp6wCxkjYJ2wQGXBDeuJz7wUWKJdkT7H7C1r12yUdnuYdnTgn9WJ/ke8xuXLl5dqR8ZU+qS6ieqxyb4DyAJCseFHnEDaCJCJsZwXACDPDqzfbIP/ydbJb+eFtyNth77ZxEhUp6zn6FuMyerODasd5i0v2pPrAt7F6uuAvffcc4+QgEKzDxMjGnffLl26OODPbw9RHOVhbDjuuOOkU6dO9hngFBj8jz76qDzyyCO2Xw8YMMC2LQnfnDgNOA04DTgNFJ4GHAOwQNrcxQAskIbOYDUB/gACAQRVMAphlL766qvyyiuv6NcyduxYOffcc23G6dCXPv5A7DIFltS1DgMNEBBD2wug+7gaKRUNph/MFAx+lXRlaNbrpesd8A/GUjgDhvLyTBYyY4mxHtY2IKlKPO6Femwm3jUOJ22mjFLuo8ASgKBjGZXWfKR4jcRThL2Z6fGIMZBNEcZEgCsV7st4COAeC1jS4927hDYqGKtUYN/SjunaGIPFB5ud9sLlU4U5bO7cuXLWWWdZgFe/5/gFCxZYd993333Xfk0yGIBAwKFcbfRo+dy72I1JAF7kzDPPtEBteXphswd332hj6Zw5cyw4CGsUkJgNNMYUJ04DhaQBxwAspNZ2dY2mAQcARtNMHn4P44ldXpcFOA8bN4tVIibkhAkTZObMmdZdiMUmBgVCsHAyB/bo0SOLJUrfrTC6FVjSmHfUD4MNYCmVGEvpK2V6r4QxAIuOems7AswQH04ZWum9Y/quBoNFM5t6gSWMWdorEVe49JUqN1dCF7BZeSmIjTFIfDi/tCPPlwJLykxEWzA3AZbKYyzlRrPZvSttR1+kT+oYlKt2pL1oJ4Al2k0FYEnjBDrWrWql9Du6Y2wCjFc3XZixMC0z2R+97tyLFi2SkSNHWoYh4TuI5dehQwe57rrrbBgYysj8BkB44403ChsF0cCj0rVzf2VaA4zjiQKA8ZRpzJgx9pngWD5fffXV8ZzmjnEayBsNOAAwb5rSVSQFDTgX4BSUF7RTd999d8vUYkGKYRFtBx93I5U2bdroR/fuNGA1gIsJ4BCGH8w5jAhiDzVs2NAarbgC86xhIAZNYGTAzGDhzSIBQxxQkNhYvHCBa2lcKDNpwGVLZwAN1BH3IDVQaVPqHxRGFkAEwBHubgAUGE0wlmDD8CK+FkAgAG6ibnbZaodU70P/C48Pp88x7A4/GfSUBZCBFy7mCiwB3uKuDCMF1i3tmWmWW6p6z8T5xLjE/V7jpxKvEVfPXMVho70Y63jRRoyHgFqAzXzmpe7cbBr46VnLRPvEe02ebdZRmmUZRh3tyJyYaR153blx5Wbsgw08b948+2IO01iqgIG4++Iumulyxas7d1xmNYDbL4ldmDfw2nAAYGb17a7ucw3AXSjmL/i8oJ7iBa28nqK7j/7RgAMA/dMWGS8JO8C4agLa4PbRsWPHiPf0unIedNBBEY9xXxaeBgCNCRJ+zTXXWMABDWD8DR061LJE/vnPf1rDderUqXLHHXfYbNOXXXZZKdejoGgNcBygD3AJcAVgiX6D4cQLg5jfg8gwY+FPHXD3VaCB+gJ6Ut8gAmUYr4BKPI+aMARXONqMJDZselC3fIvrSF0BztRNk7bjueQFOOpniQQsMcbQ1wAGAS8Bb2FN5bvwnNKOGoeN55nnlc2WaBt12dYJmwOwSb1xAgEFAd55wVKkzPTDII4h6dAnm0WMq173e8YcdAaYm00BdMR19NRTT5XJkydbxj7joIJ/sNmPOOII+4w58C+bLZPbezFHslnL/AiY78RpwGnAacBpoPA04FyAC6jN33777RDoRwaw22+/vUztYQURvJ1kCBhoLOxdPJgyaiqoLwCMZs+eLcOHD7esBioPs+qKK66wMYTUQCcI+V133SWTJk2yQeRVSb1797YJQ6IBznqcn9/RAQtmwAlAFxX0ANgCWBEEoxd3vvAEH7CtAP+ybaCqDjP1zvMIkIQxTvsh+eK6CPASHq8Rpg8so6Ay52Chqjs3QIoK7GKAQIzWfAMqqDMMXJha+oyyqQDDmrHFz0J5WR/AAvSOibBPGVPyDWyP1Rasm2hD2lLdtmFE4u4LMJoL0fbBBfiBBx6wRYBJynzNPKZ9jPXdySefLJdeeqnss88+uSiqu2cEDdBGmXAB5laAgADBeGosWbIkwt3dV04D+auBUi7ABwQ0C/CbLgtw/j6h2amZAwCzo2ff3KVr166WBQirAJcQXD+8MnHiRBk2bJj9CqbX6NGjvT+7zwWoAdy9MAw++OADC6D079/fsgABHCIJRu2sWbNsnEDv4hIGKozAXr16BdqQV4aZMinQAUavMsz8wtjxtg2AEewP2IwqGAEARn4HGrS8yb5j6AJSsOhT41wZgwBLuTLQk6mPlyEH6IAANMDM4j0fhHqRiAbwVl0oqRfPKe0F2O53dmN57QA4Q0gB+iQJbRBcN2lHAMCgCRsL9DHaTYFMNkRweQUMzOcxBtYm7E1YnAgbKYRRYH7MFWDNHHznnXfaGG/KDt5///1lypQpst9++9nNLFj606ZNKzUnMEcDBB5//PFBewTzrryZAgBZt8DSpZ92795dnn/++bzTnauQ00AsDZQGAIdJtarBWjutW79aXn9zgq0iG09stjlxGkhUAw4ATFRjAT9+4cKFglsvgAAuICNGjJBDDjnE/v3ggw/aRSNVhIFAljhldwW82q74KWrgueeek9tuu81m9403LiSGPBmDyRz8+uuvh0oAwxQgsG/fvr5xbwsVLoEPkRhmgH9MxoCBgIK5FgAjTfChgBGAF/2bRBmFJOgC0AVgiUy5KkFgmEUCjGD6ATRgzOUKaFAdZuKdOkfKbArAogyzILJW2UDwxodjzMDVlzoFgUUcq63pVxgkXrCd4wE1AW8Zc/LlWQ1n4VIv2hB331xtAtFn5s+fbxn3uvmG7knwcfbZZ5cpF+DzQw89JLfeeqtd79FWDhRCC7mXTAGAJGn7+9//bit4/fXXhz7nvsauBE4D2dGAAwCzo2d3F39rwAGA/m6fjJQOd85+/fqF4kaF3wRwAOAGdpATp4F0aOC1116T8ePHy5w5c0KXwyAcPHiwnHHGGZb9EvohYB8wepVhBlsSwZCHAUIdc8F+AezDnZKkCrBBEAAj+rTfEkPYwmXxP3SD6yIGlpdhxoaIMsz8BMSsXLnSum1rWWG/abzGoDPh4m12wHb6GO7cCmTTRrg0Arbnoo/FW3Y9jnECt20vCxeGHH0yiECm1ivSO+MgYDttpnFGOY4NRdorKCETotWNTQQ2VvRZZBMB9maussQD/NE3SOgAoIfQPwYMGGAz/uI+H0s4n006gMCzTEbgI488Mtbh7rcsaCBRAJDj2TDZe++9o5aO9RfsToBfYnoyHgUxWVvUCrofnAbi0IADAONQkjsk7zXgAMC8b+LIFWQBy2IPoI/BEAMEQ+SEE06Qiy++ONCATOQau2/9oIHFixdb1+CZM2faTJKUCYbChRdeKGSnw5AKqgC00ZcwetWtj7rgakucwGy4aGLIEauQhb26pMFG4f4Y3oUCGMXzDKErDCYMJ02+wHnqzo1hlMv4pwAntCNgpQqAF+O0H9ilWqZsvtOvYJjxUmCb+5PZlOfbjwwzwLBwwIj4ugBGQXI/T6addTyi/vQ1laCyOHGfxN0X9h/CpgobpozxuWI20ieUna+bBAceeKDN7kvojlyVS9vavcenATZJCQmgwjxOgjUErx1Cr3gFoNYrL7/8svXmIaxPnz59pH379va55BhiUz7yyCP2RZ9EcP++6KKL7Gf3n9NAIWnAAYCF1NqurtE04ADAaJpx3zsNOA1kTAMYhGQmnD59esiYgsWDm9KgQYMCHdMCgx82BnX0sl8AJwDiMpXMgFhPAEYwxhAMP9yRcS/MN4ZRuh9MGGYAgbCz1EACLFV37mwm1gDYUrdtLQvPDkBDvgNG8bZrtD6GfgAC/ZCJlrYDvCXpjrqc57vbdqz2A5xiTPT2MWVK02a5Ys/FKrP+xjgO8AcogzC2tjRjOUzcXG2q8Hy98sorNhkXSdsQnvuxY8daVn2uymUL4v5LWAMAevfee2/c5+ncoCcoAKh/R3sn1ujNN99sN1yjHeO+dxrIZw14AcCDOgYzBuD8tybYJnIxAPP5Sc1s3RwAmFn9uqs7DTgNxNAABtXUqVNtcHIFrmCskZWQOIFkUQyqsECHMQKwRJB8FQxdjMd0gRSACzAHAB1VYEQRHy4IrpFaZj+8o8vwGGYY+7gs4h6cyZioPC+4bdOWym7DTQvgj/Z0TJ6yTwg6YwwJZ5gBtBGPLVcsTsAuACNlvQF00ed5FTowQ1Ie7WP6nNOybIzQx2CB++VZB2gGjGcMV8AFxjrsTYCUXIiOE8RvfvTRR20ReKYGDhwoo03SNjYLnARPA6kCgIw5Tz31lLzxxhs2niPrAcZGYt/yTLRt21YOO+wwyySEserEaaBQNeAAwEJteVdvrwYcAOjVhvvsNOA0kBMN4K46Y8YMmTRpkjUOtRC9e/e2Ac07duyoXwXuHYNNMwcrg4RKAFJg8AJSJAMKRMoIC0AFYBRkV2o/NDC6BYzDnVvZW5QrUyAFLsgwxWAiIoDgGucP8MhJ+RqAAQsQ6M1ES7+if8EwA0zNtOCOSdxNDAwVgH7A+GzcX+8ZhHdlcdLHNFwB5WbTQmNxJjMupqPujNnh7E3aD+APMD5XAnhKJt8JEyaEdNa1a1cbzgWXT78Ap7nSj7uv04DTgNNAeRpwAGB5GnK/F4IGHABYCK3s6ug0EBANwAiZNWuWNXA0iyFF79y5s2UE9urVK9BGTiQ3OOLMwVbiFY+rLoHnCbAP0KCxBokJR2w4Eo84IzB9Dzu6BlCCAaTgHFcHaAWkSJXFCfAB8OcFhnE7JpNoPM9C+mqaP1cCsAVUAsAFyEXoE7BeaLNMxOLkOcGooE/qPWH6Ahg5MD72swXYBgBOm3ljceq4SH/IZsxL+jnsTWWkA8ATRgEQOZeA5IsvvmhjwjFeIIz1JNY69dRTc1au2C3rfnUacBpwGvCfBhwA6L82cSXKvgYcAJh9nbs7Og04DZSjAQxqEtRg4MyfPz90NG4sQ4YMkb59+1qWVOiHgH0giLyCFDBhEAxNZStFcy8DKCLOn4JRGKS4FQJs5Mo4DZjqkyouIAWAAECgAgNcCBYnwADtBmsvXgHoJjA7rpBcGwEogr2ZSTfjeMuXD8dFY3GSgIP+ki63akArACNlsQFcAcbzTDgwPrEniXFN4wQyByDoELCLfpbJvhGpTwLw0yfp57kQxgbmiSuvvNK6d1IGni/i5I4aNSojYHYu6unu6TTgNOA0kC0NlAIA9x8q1arWzdat03KfdetXy/y3J9pruRiAaVFpQV7EAYAF2eyu0k4DwdEA2fEAAufMmRMqNMbg4MGD5cwzz8xZLKZQYVL4gNHJBI6Rx2cVDE+APU36AHMQ4M/LkAFggCmWTXaMlq+Q3yOxOAH/YCrB4owFFihTDPBP2xuwF5CB2GIOMEr/k4XOcecEWMJNWAW9M46QWTkZ8JzEELCxiPOJ0Ha0P2wxQBonyWtAsz1jqCnLmasBktNm6ewrgGzES2N81Xvhhkz82VyyN2Gy3nLLLTYshiaTOvTQQ+137dq1c2NF8o+XO9NpwGmggDXgAMACbnxX9ZAGHAAYUoX74DTgNOBnDSxevNi6BuMirG52GIIXXnihzWiXS2MtVb3BAsStF5ACdqAKbCXACS/wRxw6ACM/Z83U8ufzu7qasphUFqeylWCYhbcP7E0AI2WKARoC4AIcujh/mX9SAHqIxUkfU9COuwLWKXgbD5jO2ENiCK7jZW/i7hve5pmvVX7fAfCWrMHoWlnP1DhV8Fa1BiC8fPnyUJIm+iQALkBurvokz9Rzzz0nw4YNsy7llJXnc+LEiXLCCSckBVZrfd2704DTgNNAoWvAAYCF/gS4+qMBBwC65yCQGoDR8fbbb9vXO++8I7wUJIEVds899yRUr2effVbuvPNOex2MQ9zD9ttvPwssHXHEEQldyx2cWQ1gDE6ePNkmDVFmBIyNs846y7ICMZaCKhh/PNswxLwGL/WBWQYrJZdB6IOq10yWGyafJgwhSL8K4DRAIACTl70JSMgzCtDg4vyptrL7DggL6xbQPdzVNBJ4S+nom+FMMZelOTvthu5xvafNvPEy6Vswoctj3oaXEqYf2bbptyowQXHdjgcE1nPS+U4dCTEA8PfMM8/YSzM+XHbZZULGX2WDp/Oe7lpOA04DTgOFpgEHABZai7v6RtKAAwAjacV953sNxHKVSwQAxPg7//zz5a677opa5/79+8sdd9yRM0ZA1IIV+A8YglOnTrVZETUuGwyOk046yRpNbdq0CZyGMAI1wYcXTNKKADgAUCTrtqjXce/p10A0tpL3ToCCZIR1TDGvVnL3GSAIYwA3fHX/pDTh2Z5Xr15t4/zxjsDKbdWqle2LuWKK2YIU4H/RwFvCJjA2xgLKGF9pb5K1qAs+cQXZWIFtnSthI+vmm2+2G1uadbxnz572O+axWOudXJXZ7/dN9yaxt760F27YMIERnjvAWydOA04D/teAFwDsvG8wYwC+tsDFAPT/k+bvEsYftdzf9XClK2ANEBOIBfzcuXMT1sLVV18dAv/23ntvu/uOWx4GwoQJE2ThwoWWaQbr6sYbb0z4+u6EzGkAMGX06NE2MyIA7k033WQN+QceeEB4HXXUUXL55ZdLx44dM1eINF4ZBisuosr8A1ggDiBgBAAFrnC4B+OyxvPJcw/zBRaMk9xrgPYCmAWIICkEQC6AgwrtRFvGihGox7r37GgAhhVMTPqZ19WUvsgLV1OOwXVYhYQUgLi5YoppOQr1HbY3oBjzNAw+xkY2S2g/XtGSvNCGjJ3E8EToj7lO1sL4QLKr4cOHhwAkwCTmsuOOO85tOqbwkDMOZ0pIwKLgX6bu4a7rNOA04DTgNOA0kCkNOAZgpjTrrptRDVxzzTXWRRc3XRZ67L7CyEDiZQACtpBVlphO++67r8ybN09gWKmwy3vwwQfLggULbIbPZcuWWYNBf3fv/tIAjI4HH3zQJgxZsmRJqHAHHXSQzRzcq1cvXzIpAPxwEfW6tkVyRwP8w/0Zo1fdFmEi4QIHGOh9dkOVdx+ypgGMeY3zp67pMFIBkbzJJ/gO4JaXA5Gy1jxx3Yg2jJTtmZNpK+YLQFwn/tEAY+EPP/xgx0YF9ygd4yHjIu0FWIP7tgou+ACIuXLB5znDBRngj3h/CM/XFVdcYTP+OoawtlTy717WZPgmcbxrxEh3Z1OYdScAMi+eOccAjKQp953TgD814BiA/mwXV6rsasAxALOrb3e3NGng2muvTflKZNjTZBJTpkwpA6BguPN9p06d7HG46EybNi3l+7oLZEYDLMZPP/10Oe2002wMpXHjxsn8+fNDL4x34in17dvXF6w5WCsw+bxxqEhkQoIP3NLCBYMWpiuMJVgvvAA9iYvF52bNmllDJNK54ddyf6dXAxiBbCioKzrGJwAfbcVzCXhLO9HWmkSCTQuAXoxHWE1O/KEBACVvIh4tFf110aJFIcCd+cFJ7jUA8xZWJuOfN8kLbQgT1yt169a1Y2gsN2Hv8Zn4zIYPDL9bb7015HZ+5JFHWvdfxn4vcJWJ+xfKNWHpAdRF2iROVgckezrvvPNs0ic2ofE88ILOyV7Xnec04DSQIw2YzRjjqpGjmyd526CVN8lqutMyqwHHAMysft3Vs6SBRBmA7MDDAsBND1AFdl804XcMCZhWAC1ugR5NU/77HgBw/PjxMnv27FDhYAMMHjzYMkVzYcRjRAAGwUrR7LEAQBh/sFXifb44F0AJVqDGjaKSXAOXxvr168d9rZBy3IeENBApmQDhAnARjQTqAdiy+0z7e2PO4c5Om+G+GG/7J1RQd3C5GgCYAcTVZFK0A+AsIC3sMcZ+3TDiYk2aNLG/5zJ2XLmVKtADmNeZs73tles2A1h+6qmnLMOPZwlhg2DSpElyzDHHuH5vNZK5/xJdI0YqCcnHCCtCxu8PPvjAztnMv44BGElb7junAX9qoBQDsMMVUq1qXX8WNEqp1q1fLa+9e5P9lbkkyIkPo1TRfZ0FDTgGYBaU7G7hPw0AvmAkILj5xhJ+x5gAbPEuImOd437zhwZw/8XoWrx4sUycOFFmzpxpwRdcrWAIDhgwwCaByYZbH6AzQAKuX5rgAxc0XNEAGRJNJoD7L2Amkz8ucDybgBgavwyWC6ASQIUDldL7PGLMA+KRrVlBXNz2MAxhcUYT2ICaPIJnAeORpAa4DvOCoYQx6dosmgbT/z2gLO3IQpo+igDiAsjrBgGx4mg35gzaDHYZSQZ4aZtxTqJ9OP21Kewr0i6AuLQLwrhH+/A9Y24u2oxnivXD0KFD5b///a8tF2xu3H+ZhyJtFNiD3H++0gD9HlYhcvvtt+fMfdxXSnGFcRpwGnAacBoIpAYcABjIZnOFTlUDS5cuDV0Chl8s8f4OU1BjDcY6x/3mLw2Qse/ee++V66+/3rpaTZ8+3QIuY8aMsZkWzzrrLMsKzNROGq6hGKbqLgRQANADQEdMuFTE6wIH+Iehwv2IOwdLARCDe+EmB2joJHkNYMwDIhCzEVABURAXhnC8QCttxvEAvwB/tNmvv/4qZJmlzQAIaDN+d22WfHvFOpO2ZFMHQB4QEAGMAcSNtCFAO+DWzRjx008/2TbD5dTbZgDytFmqfTpWud1vZTUACE8fYmMPcB4BiKctAeb5jn4LaE97aZuRkIc2pS8CzqdbGO9hoJOtXp8x2H6w/mD/xTtepLtc7nqJa+Ciiy6ymzWEGenWrVviF3BnOA04DTgNOA04DfhEA6lZnj6phCuG00CiGoACrlIe6IOBoKKuO/q3ew+WBjDQif04cuRIa5QR4xHQjNiOd9xxh5x00kk2TiBZJtMhsLsAiwAMVADiYBSlOxssxiSupLwwcDGIYQaSkALgWjMH87xnwtjV+uXrO4AqTB7NCIu+AenYEEgW8OEaMMd4edsMcFGzPTP+8MpVwoJ8bE/AVvQLYxah/WDi0jfKY/HRZjA0eYW3Gc8H/Yzr0Gbp7uP52Bap1omxFb0rII/OYW96WbS0KTECNU4gQCCAIKETGJ9hgALcMj8o6zOVcgE4Pvroo3L11VdbkJlrERaAOMLE++MZchIcDZBc7JlnnrFhNQBvnTgNOA3kiQYg/QcsBGDgypsnj0q+VcMBgPnWoq4+cWlAmVgcXF7GPa+LjhqMcd3EHeRbDfw/e2cCb9W4///HvVTmmcvl3mS6FJpTNBhCAxluJUNFRTI0yJCxpCQRRZRKiKQ0SIZCI0qTlJKb4X8HxHWRmYv/eX/v79nWOe2z9z7n7L3XXnt/vq/XOnufNT7P+1l77b0+6zvg4UMSb0KwSOTNj3puCh977DGbWrVqZZWDjz766HL1gfxu3FQiNPuwQnLycWOajQT0hL0deeSRJv4hBBK66PPV4SWDQMHNrgSK5MNL6CBeYj5lAFsgLnBDnw6xwLcgOGaci3in+fBUwrsRjhEcg9cjv61eUyOASITggzDujc9CeSvC+jFjv37MyDvHePG5U2EeTzn9rzxcwasaD1oMkc8L8om8ZsnZyMSY8UCPzxljxnsmPttcG8uTj5NrPQ9b+F5ZsGCBtYvP67XXXmvzdL01JJH6w8OCXr16WZtJG8LDGpkIiIAIiIAIRJmABMAoj57aXm4CwaIJyTxrKleuHDuO9zKIzdCbSBPg5oyCIJdcconjKT/hWm+99ZabPXu2TeQQ7NOnjzv55JNT8togFA0hAAHAJ6DnGIhFeOZl2/MDgQpvxmDlYNqFOEE7vaiUTASP9CCXs/E+rJCx9Hn+qLBMWCFibqaMMSPtAGOGgIwogXiLUMGEQIHQoeITqY8A48c4MvkQUcaQsUxH1WxCttkXY8YY8dlCOCbPIxPhqIhKYVwDUqcUjTUZSx5iMJb+4QqiTDBnYyo9YczYhjFD3GfM+H73eQI5L/ic7b333km9Qjke3qC33Xabu//++2PX/rPOOssq/rKfbF/7U2GgdZITIHcjDwwaNmxoFYCTb6E1REAEREAERCC3CUgAzO3xUesyRCD4JD5YjTPe4XzBBpZx0yDLPwKExJLb59xzz7VQH4TAxYsXO6oIMx1++OEmBP71r3+NGz6LqPDaa6+ZuODFZfaJZxH5pZKFFWaaKCI2YcdVi3IOIlAgAHJec+PLhDBBKKtEpaJokCIvHm748BTzY8lDAkRcBNNs3chzTMQJxANfMIRwbi9Q4H3GeCJ+ZKtNmT5P073/eGMZL0Q0XcflM8+YIPZxDvE5w9ucnJxMPAxgGedRIi+1dLUnn/bjxxKvP/+d7IXXinhlEf7NmBCyHcztyLhRPIrrAMu4NsYT/rn2T5482d1www3u448/NuQI+IT7pvrgKJ/GKZ/6snDhQjd+/HhLEUDhD11n82l01RcREAERKFwCvyvcrqvnhUwg6PWRLKyXUCNv8pTyJPLzFaGudevWbtGiRSYAnnrqqdZRisZ07drVHXHEEebhgRDj7YUXXnD16tVzp59+uokz7AMRAO9BbhzDFv98O3nlZhdB6dhjj3XVq1ePhZMSRrds2TL3+uuvWx+8Z01w20J4jxcPHNasWWPiH2OHMMpYkiMsjBtAhCLCVBs1auSOOuqomEhLW1evXu1effVV8xT0XoqFME6p9JGcjcuXLy82lgjycMSrK5NjyXmDyNegQQNXp04dE9hpM98lhIhyfSFXYLKHT6n0sxDW4Tt6xYoVNpaIf/DlgQZjWRHxL8iO8wHvWq7ljBvh28zjePPmzbPjUSyKIj0Y10jeI/Lx3YD4x+8DvADfeOMNd8opp2T0HAu2Xe/TT4Bxv+iii2yce/bsaSk10n8U7VEERCBMAlsVXcejOIXJTMfODwLyAMyPcVQvykiAG2pvwYIgfl7wlRA8bwg6ssIggOjz9NNPW0jw0KFD3eOPP27hmOR34iYPj8GVK1c6vAS84S3CzUKue4pyA42ghUiB+Ec4na9oiqiEpxJCIctZN98NTz/y/OFp5w0BAJEhV8bSCxSIFIwV3mV4A/oiL7Sf6xNTsrQGvo/5+OpzXeLp6o2xxIMz6Pntl2XylTEj/JcJ8Y8x4xxTbsfUqHtOfAf7hxKIt4TuZnIsydPKwx7OGY5NnliuEVOmTLGJcNCqRQ958PzzIeVnn322pZDg88e4y6JNYNCgQVZchvEcMGBAtDuj1ouACIiACIhAgIAEwAAMvS0cAoR0eqMaZCILLk9XddhEx9Oy3CKAp9zDDz/sBg4c6O666y43ZswYqxx8zz33xG5Ka9So4RAJmzZtmluNT9IablTxoGEKikqIFXg9BisHl7fSbZImhLo4Xm44QmsRGHI5HJq2MTFO5C4jjNuLJYi5hJ0T1pjOIiWhDlQKB0eIQayh+I7Pv5mNnI0pNM1WQVTnewdRmXYyMWYIlUyE4SO6E2Za6AISYh9CKQ9UvJck3nXkWURMzZYhMiICPvjgg44Q0LFjx5q3LekemDAepDBfHn/ZGpXsHIc0INiJJ57oZs2aFfegXH8xXskhjPGA5vjjj7f3+iMCIiACIiACuUhAAmAujoralHEChPXxw50bZ1+tr7SDeg8vbqp56i8rTAJ4niCq4F1FsnhuUhFhEM8I65s4caK9D4rLUSIVFJXwVOKzQRgUN+HBysHBojhR6l+wrV5gwGuOPmL0i5t9H/oXXD9X3yMq8VCC0NagqOTfczPKNQtRM58NL1Zyw/kbcnLxIbRxzc41MY3rB+PFuCByIeDSbvrAVNbiE/k2roRu89DaVYi/AABAAElEQVSNEHeMBw/wwms/LG9kxEeEoGeffdY8DxGYEdoxrpMXXHCBu/TSS1337t3tO8AW6E+kCXjh+aGHHnJMiYzPbYcOHWwVHgJKAExES8tEQAREQATCJpD/sV1hE9bxc5IAN4Vt2rSxtnGzsWTJkrjtZL73AGT9XLuZjNtozUwrAcQiQr8Q9q688kq7Md19993dHXfc4W666SYTkPAkI0SY/FFt27Yt9XxKa8MytDPvqUSeQEQKbsD9DS+5y/AM9EJLhpqQ0d3i6UiuQ6o9I/4hKiAwEPJNyHMUP+NeVGrcuLFVEPZhy4QI01fyGlLggHM5n4zzcNWqVTbxnrFDpGcsEYxyeSx9bkfCSWvWrBkrMOGLT1CECJEJL8FCMAQXri1Lly6NiX88pCPPH2MahvjH5+Wzzz5zvXv3dk2aNLHPEg8NuDbOmTPHkROQzx4FX/guoJ3dunWza0shjJn6KAIiIAKRJsBvoihOkYauxucCgaLcl3l2R5ALVNWGrBPgRgmvPqxTp05uwoQJ9j7RHzxGEHUQb+rWrWu53PyNM9vh5cWPfhLJI4Jwc4KHkKxwCBDmhejnw7242bviiivc9ddfHwsR5Qad8B9CgKka6Y0bV7aNeiVIH6aIV6D3iqCPUfMu4/OMNyM3694Q/PAUy2Q+MX+sbL7ytY74x5h5TyqOj7iLSEG/o1yFFkGaUF+85/xPGER5QkTpY1QN7zf6REEJ3y/GyYd0B7+fotrHku2mn+ThRVgLhm5TSRev5LCM3wWkfujfv7+JgLTjyCOPdCNGjLDfBV5c5noyatQoKw6FyO6tS5cuFhrs/9druATK8xsxWYt5QMY1ltB99i8TARHIfQJ835DbE2tcs4+rUjlaERLf//ClW/TGXdZ+oj2COe1tpv6IQAoEJACmAEmr5B4BvCO4YfBGCMZVV11l/+L9QVW+oPGkPp7169fPDRkyxBbVqlXLXXPNNeYNRO4zcsDgXYKx3uDBg+29/hQOgeOOO87Nnz/fOty+fXsr/uGF5pIUuJElRIzzifPTGyIzHiR4BhKaGFUjxxohi9zoBKsgk7OMGyBymPmb4lzqI6ICIcwIKz5hP8ICef7yPTSWc9LndgyKEwjZCIH8cIzSOUl/CLnk2u/FaPIcMpbpqgabC+cuBSf4Yc+NihfFaBdpCPis5ct5y7mJhz1ej1guhG5zjvHQj4c3VB7GuF4gBPbo0aPUzwtj9thjj7nhw4ebB+CwYcNsH7YD/QmdAN9b/rs71YfEyRotATAZIS0XgdwjIAEw98ZELco+AQmA2WeuI6aBAIIeT+dTNX7UxzMEAUJ2xo8fH2+xzeNJPoUfwghBKrVRWpAVAtwI9urVy3Ezd/TRR6d8zFdeecUE5GDycJ444j3IzUeUvZT4LCEmcUMV9C4jTxbiBDn0cuGzEk8swtMPsQjvxVwUK1M+wcqxIiGyeKsg4nohFO8ywiwZt1z3LkMs2rBhg8NLDqPt1apVCy08tBxDUOZNEP8QPBk3BCZvCFKMGaJnFM9jn1uUc9Eb10dC8cMSpLle8CARoY/fFv43A9drqr6nmhuU7ebOnesaNGiQN0KtH6MovabrIXGiPksATERHy0QgNwkUEwCPKvIArBQxD8AfizwAV8sDMDfPrui0SgJgdMZKLQ0QSJcA6HeJ5xYiH7myuAnAm4l8bhdffLFr0aKFX02vBUiAG7ry3mSTZ47QYPIDek8eQhVJFs+5xfuoGlwQZRAC+cx4Q2TDu4ywxbAqB//nP/+xohDeswixCO8P2sX7QjbEF+9dFswvh3cZN7Q77bRTTuFB+MLjLygWIVoSup0PBWlSgY1gi+heMqQb0RYhEB5ROK/pB564hG8TYoshZhLuS/GTsIxr87hx46zS++eff27NqF27toX7ksqhvNf/sPqj4zqX7t+I8ZhKAIxHRfNEILcJSADM7fFR67JDQAJgdjjrKCIgAgVMgJteQsMQmX34LKGL3KTgFejzkUQV0ddff21CYDB3GeIf/UJ0I+Q0GwZb8vyR/84bQiSeRYUiFvl+J3tFgPnXv/61hXdZroR00z4+N4Rve7GI0Ffy/OVLCGyyMYq33Id0B89xvOYI5+bzlqvnOcU08OD0BYS4JuCNm6pnXTwWFZ3HQwwKffXt29e98cYbtrvddtvN3XLLLe6iiy4KzRuxov3S9hIAdQ6IgAjEJyABMD4XzS0sAhIAC2u81VsREIEQCXATfN9995lnCe8xhLJ27dpZnkDyBUbZ8NbCSwlhyYs2hAP7MFNEz0wYnmw+zx839RhCFmJRmJ5FmehruveJV5YvGOLDazkGYep4uGQ7pJvxw9uNIk0UbsEQtSjAFKZYZA3JoT+I3QikfNZ8SDeeahR4QXTPlfOeMWQsvWBJG2kf4dtheQhzjtEeKvdOnDjRRpV2ke7j1ltvLcgUATl0aqspIiACIpAxAhIAM4ZWO44QAQmAERosNVUERCA/COAFQ95JcgtyE++tZcuWrk+fPq5hw4Z+ViRfEeT4kUXffLEGOpLuMFOEDwQQivb4cFbCIn1RCIXupX76IIoQ/oiAGwzpzmbBEDxJ8RIjhBtDPCbEtWqREBmWWJQ6wXDWLO2zRnoB2OHRFsbngAcAnEsI816gpC2E+4aZAxVeDz74oAl9Pocp6T5Gjhzp6tevHwqrcM4cHVUEREAECo9AUABscmTvSOYAXPjmcBs4VQEuvPM3XT2WAJguktqPCIiACJSRADejkydPtoIha9eujW1N3imEwJNPPjknCmrEGlbGN4gA5G77oChPoPfmYhcIAYg6FREn8KDEswjRCEMg8nn+cqEIiTUqon9ginjD2HmPSnLMEU6N91a6C4bwOUDE5Ye5Px6FWhBy032siA5J0mYjshGCz7j5zwQbZbs4D+NX0oOTvKB444ZZtIR2vfrqq3Zd9ddacv0OGjTIXXjhhRKYk55hWkEEREAEok9AAmD0x1A9qDgBCYAVZ6g9iIAIiECFCHBzSiGa22+/3S1atCi2r8MOO8xuWNu2bRvpfFT0j5A7hMBgmClhingp4RmYqmiH9yTCX9BLjfxn5PnLVq7B2ADl+RsKhuDFyQ9mX8QGbzLvyVnRMNN4HpwIVohFiMOyshPgs4YHJUKgTzPAXrwnJyJupj4nfDbx4PTH5TON0M8UVpESeCCM3nDDDe6JJ54woLSLHH8DBw60gl9lp6wtREAEREAEokhAAmAUR01tTjcBCYDpJqr9iYAIiEAFCOClghD49NNPx/ZCcn+KhXTq1CnU8LlYg8r5hptxwkwRAr1IwK7wEEIIRJwoTSjAS4zqoYQ8sB8MkQixCNFIljkCiH+EWiMGkufRW0U8ORGpEIu8txqFLBBxOQdSFYN9O/Qan0A8T07YpjsnJ+cHob6Ijv6zibcfHpyZyvsZv8fF55J+4IEHHnC33XZb7MED3tX33HOPq1OnjsJ9i+PSfyIgAiKQ9wQkAOb9EKuDKRCQAJgCJK0iAiIgAtkm8NZbb7k77rjDPfbYYzHvK/J6de/e3V188cWO91G2r776yoTATZs2xUQDRCDETibvpYSXGD/YEP8QATFEBcQFQvjCyG8WZe4VaTtjwXgh9DB+3soSZhqvKIT34GT8ZekngCcnwjmfI/8Z4iiIdAjvu+yyS7k+R4h9nA945HIMjM8mojyfzbCMdi1cuNCq+65bt86aQUg5QiAPUUp7yBBWe3VcERABERCB7BAoJgAeQQ7AnbJz4DQd5fsfN7uFa5QDME04C3Y3EgALdujVcREQgSgQwOtq+PDhlrieEDuMm+zOnTubVyBiWZQNQQhBKVjN1Hsp7bTTTrbM95s8f3iJIRjJSyy8UUdgiRdmSrVecgQyPiWLdpAP0nuJISRi8uDM7hgyBh9++KF5clJF2BufM4RARLJUP1cIwHhw4tGLIaqRg5P9pLoPf/x0vXJe0r/rrrvOTZ061XZLuy655BI3YMAAhZWnC7T2IwIiIAIRJSABMKIDp2anlYAEwLTi1M5EIHMEli9fbnniFi9e7PBqINE6HjOEcx1zzDGuS5cu7thjj025Ac8995wbM2aMW7Zsme0LbxCqIZIbqUWLFinvRytmhwAhs/fdd58bMWJELHwWkaVdu3aud+/e7vDDD89OQzJ0FML18FJiCnopcTi8/OQlliHwFdwtQhACLnnWEGAwzktfMARRkGV/+9vfYl5iqtRcQegV3Jxx4vuDcfviiy9ieyMUHwGXsSsp4PqV+GyWLNhCTkg8ctk+LMMD8d5773VDhw6NhZU3btzYwn1r1qxZLg/HsPqSa8clf+vrr79uE78XmHwKBzwqJ0yYkLTJ69evdy+99JJtu2bNGssJSx5XBFrOH357nHPOOe60007TWCWlqRVEQATKS0ACYHnJabt8IiABMJ9GU33JWwJNmjQpVhyitI527NjRPMV8+GS89fC+QeQbN25cvMU2r2vXrm706NGheXKU2jAtcHjDjR8/3t155512A++RtGzZ0gqGNGzY0M+K3Csi4MaNG80bsGTjCXmmmMCuu+6qG8SScHLgf3ID4q2KJ6cvGEKzeEjhBV3vJYbIxHtZ+AS+/PJLu44g8sQTcL2o573rEHL9eOZCwRba9fLLL1u4L6HI2D777GN5VBGUdJ5V/BxLlGYhVQHwvPPOs3QWyVrTtGlT99RTT0U+xUWyfmq5CIhAOAQkAIbDXUfNLQISAHNrPNQaEYhL4KCDDjKvC7z9qAiLZwM30YR0vfbaayYGceONdejQwT3++ONx98PMfv36uSFDhtjyWrVquauvvtrCKvHqwHti1apVtoz1Bg8ebO/1J/cIcBM+efJku9Fdu3ZtrIEIgFdeeaU7+eSTIyPgIkrj+UeePy8ebb/99pajDE+TYL45whURAglXTHRjGgOiN1klwPh9UFTkBe8yH+pLA3gogZfYH/7wB41bVkcktYMRiu8FXL5XMD5feGchvnPThFiI4R0Ydig+wh/XDL6nZsyYEWvX5Zdf7m666SbLa2gz9afCBILXWX53/OUvf3Fz5syx/aYqAHYuSlnx9ttvW7TCEUccYdcBog4IIWc+Dxz99xjfYUQ6hBVKXmFg2oEIiEDOEigmANboFc0cgGvvNr58BxIdIxOBshKQAFhWYlpfBEIg0Lp1a4d331lnnRXXo4FQGsKAvQfEggULHF6DJY3l1atXN5Glbt26liidcDxv5IXiCTzhxtzkEbaD+CjLXQLcCBPOjai7aNGiWEMPO+wwCw0mRDhXiyvQdkIROS8RIDDaGqwGyzrkm0NU4tUb5y1CIN4+8vLxVMJ9RfBDRELI9SISN/FBIXDHHXe0PHEIS7rBD3e84h2dBws8TOLGIljx2a/LQ6iDDz44VqTHz8/mK+2iku+wYcOcz2V43HHH2bwaNWpIYE7zYNx8880WokuYLp9brsXke8RSFQB5MFBaWDn74XrBd9W0adP4182cOdPCge0f/REBERCBNBGQAJgmkNpNpAn8LtKtV+NFoEAIPPPMM/bjuDShg4qLhIR68wnQ/f/+9e677455WI0cOdIFxT/WobgE8zF+sFN8QpbbBPDOIPyXqpevvPJK7KYJ8ZZQb7wtRo0aZaHDudQTvPpWrFjhVq9ebeIf/aCAAEI2hU28OMR8PJDq1KnjGjRoYDeg9APBkD7iKUJxCR+WmEt9LKS2IOTijUyIKDfzePyRlxJhpnbt2rECDIw7nj6cq3gJeo/PQmKVy31FgMfLq7QwbbwACRf2Am82+8LDgBdeeMHVr1/f3XLLLSb+4f2Ax/vcuXPtWhf0Vstm2/L5WBRQ4SEk4l95LZH4xz75bXPVVVfFdh98mBWbqTciIAIiIAIiIAIVJiABsMIItQMRyA0C3Gh7I5y3pHHzxFN1jBCeo48+uuQq9j/zDz30UHvP+mwniwaBRo0a2Ri/9dZb5pnBTReePNxY4RE4aNAgh7domEayforYLFmyJFZBlFAw2k6IaCJvRcJ/jzzySBMJufFHJPR5A7lhpCppPK+lMPub78cmJ+XKlSvdG2+8YYIMAkzVIs9MhFyKSTBGXsDl2uJDgBknPD8Zt2CBkHznlev9IyRz6dKlNjaIfHweEeZ5yIQx3gjvjBvfM3z+Mm18ByHyt2/f3p155pl2XNrFdY1rHWkvSns4lum2af/pI4B3sDddxz0JvYqACGSMwC9Fe47ilDEg2nGhENi6UDqqfopAvhNAWPEW72aIG6gPP/zQViHMN5GxHDGFULBguE+ibbQsdwjgeTWhqDLjwIED3V133WWFYcilR05HvEAJ27riiivMyydbrUZMIDyU89B7D5W3iACeqgiahAqzT0ROPMn8e0QmRCj2L8sMATwuCfWFvX9IgEiEiEv+xnjGDT4eqaQVYKx8wRCuMXgDEs6N2KRxi0cvs/MQXBBiqdjsDU9cPmNelEf8Y9z4HvHjz9gxbngMZmLcCPHFE53rmBeFmjdvbvO4zsnjz49W9F+feOKJWCd4SCkTAREQAREQARFIPwEJgOlnqj2KQCgEyPvnDXGkpOF15S3Zj+vgcrw9fL4fv71eo0GAG3hunm+88UZ33333WY4shMD777/fREEKyvTu3dvyQmaqR4hDhAwiLvg8f4SHIgKRT6wiN/B+P4h9iBKISIgEH330kU0IUizbZZddKnScTLGJ4n4ZT4Q7qjX7sGsEP4Q/7yWWrF+kHsDLuFq1alZcAlEJTzLGkIn9IASq4nMykhVfHi9vI58XvgOCHlkciXH2wjt5lBB/GTfOB6Z0jhvn2ezZs90111xjD6E4PiIjef/IhetTBDBfFl0CeKTz3TB27Fj30EMPWUc4j84999zodkotFwEREAEREIEcJiABMIcHR00TgVQJcBPnK/uyDcm0Sxo3bN6SVY1COPLGTZ4s2gR22203EwGpDjx+/Hi7iUYsmzRpkk0tWrRwffr0sTDcdPZ08+bN5kn6xRdf2G65aecmHkE5WU6osrSDfbFfzutNmzaZYPD1119buDM3mDvvvLMJgYQaV0RwLEub8nFdwkPxDPZVmeGOiBfM2ViWfuNZxrmA2IfnGd5keJkxZkyEfLOMis8SfMpCNrV1eRhABVZfSCPVSs2sx7j7ceNaEhy3ihR6QfgjtJjq9OT7wypXrmyVza+99totRMnUeqq1colAs2bNXPCBZbBtiH/Tp09XFecgFL0XAREQAREQgTQSkACYRpjalQiERQAvr9dff90OT44kCiaUNH/TzvxkoVrBED6EFFl+ECB09rLLLnMXX3yxe/LJJ93tt9/u1qxZY1WEqSTcsGFDEwJPOeWUCgkueOFxE483lzcSyFM9tGThGb88Ha+IRIQjEgKMuIGghGhF4QKKjdB/PAJZR4JS6sTjhYeS3w8vTsSgihpjgTco4xIcNwRkzk/OGQRejhkvvUFFj19o2+OJS/5FPHMxRHH4IuqVRZhnLBgTxo5xQwikUrcv9IJnlx83H0aciDXfNRSzosKvT2lBgSPm4TEq8T4RvegvIy0F3uqIgDIREAERyDSBrYoeODFFyaLW3iixLaS2SgAspNFWX/OSAE/S8YzA8JQhvDOe+fxJLEt2047HhTcftun/12v0CXAzTojVOeecY+If3qMk9aeKK2HBhPkRGownaSo37p4Iuf0QARDefJ4/vLgIDyWcM1uGUMBNJBPiH+1B7MDTiVB4wlfxXkK8KEv/stX+XDmOH0/yNuJljBEeihjDuKbbSo4b5xIenVyD8DxEVMbbkCl4jUp3O/J1f4wnnwUmP54UaGE8gw99ytr/4Lgh/jFueHQi4iECkiuSzxqegYiFJY22zJo1y8J9vcc5nqHk/WvTpo2Ev5LAIv4/ob54jOLtiXf48uXL7XfLvffea+cK4cAVqTgccTxqvgiIgAiIgAhklICqAGcUr3YuApklQAXEM844wwogVKlSxU2ZMsVEwHhHZbm3ZJUbvfcF62fSY8u3R6/hEODGHQ+bhQsXuldeecWddtpp1hDyPl500UVWsIHcgdysJTJu5Mi79+qrr5pIg9CAQFO9enVXv379rIp/JdtJ+O9RRx1l4c2IEPSZ8x9hYvHixfYaPN9Lbl+I/zOeCG9+PBFouH5QwKNu3boZEf9KcmbcqPh87LHHmuCHtxmFXhAjGTeE3GTnZcl9Fur/jCcCOOOJGOfHk89FrVq1KiT+lWSKyFejRg0bt6pF3rZ4FHI9WLFihaNox6mnnupefvll24x2EYLMdxgPIxD/OM9uuukm8/w8/fTTJf6VBJwH/yPuco5wPWncuLE9bHrzzTftu+iZZ55x9erVs9ygedBVdUEEREAEREAEco6ABMCcGxI1SARSI8CN8EknnWQhjtwcU0GvSZMmpW4cTOieLKw3eGOdLFy41ANqQaQINGrUyM2cOdMhKnfu3Nlu3LkhJxcXBQEGDRpkedlKdoqbec5DQtDxMiWck1DCY445psJFPkoeqyL/4+GEAMENpxcmEJTwhsL7kX4Hz/uKHCvK2+LBhVjDDXlwPDk/CK1GQM2m8QCC849x8yHHCFgUnUDQeuONN+waiJgk25IA5/SqVassBL7keOIxnqnxRMgj5J9xw8MQ0ZaiMVwvEAF5MMBDhqOPPtq9+OKL1nAeQBDy3b9//7SKkltS0ZxcI8D5gmcgaRr8906utVHtEQEREAEREIF8IKAQ4HwYRfWh4AiQW+3EE0+0HGvcwFHYgVCpRBYs/BEsCBJvGx+GxbJgQZB462pefhFAJONG7JZbbrEKwmPGjLG8XoMHD3Z3332369SpkyNXEzfzffv2dXPmzDEA5G+76qqr7Kafm7lcNTwTESbwQuFzQAVaPAB9BVoKhSAQEupaSIZXJCG2wWtDNvI2psqYUG3GjJxyhJcSZoq49emnn9qEx6AvGJIpUSvVtubCet5bEk5eHOXcRozLple3L9AzYsQI8+waNWqUeW8iuDNhhOqTuoLqvhq7XDh7wmkD5wEPjubOnWsPo/iOUYqGcMZCRxWBgiDAg8OoPTyMWnsL4kSKXiclAEZvzNTiAidAdczmzZtbKBcoRo4c6Tp27JiUCsKON8KuEllwOfngZIVHAOGXHFw33HCDIwyYG3jOPW7UJ0+e7PAi9aHknFt4DRLSFRVDmEDoQ1AifLmkoIQAyHJuSvNZlMCbDtEP8Q/RCMPrF6GI6tG5ZsHCE5yPeHCSR4xcj3gtIm4hBJJrjnULzRD7EEgJcfeh7XhVMZ6cy2EZY9GgQQNLU8FnjocEFCLBGEe8AfFUpEgRDxNkhUkAkRojXyvnhc6FwjwP1GsREAEREIHMEVAIcObYas8ikHYC3OSefPLJ5kHBzinecOmll6Z0HLxnfAJ2CockMnLCYeRMQwSRFS4BRCAqMxJyft5555moQqVPxD+WEf5LFerjjz8+kpAIWeY8pwIyOdG85x+iEuGlFEbBOxChLN+Myq1LliyxAhuIf3jbIPgTlpmL4l+QP6IsYgH5wggnJZwVo2AIDzAI60bU9CJ1cNt8fU/4NgUV1q5da+IfohverpzbYYp/fG/169fP2sF3D1635ICbN2+eCX6E51OtGy9jxNvORQ8TEHNlhUeA0H5vSj/iSehVBERABERABNJHQAJg+lhqTyKQUQI8EW/VqpVbuXKlHef666+3qompHpQbZh8mzA0yN/7xjPneA1AVGOMRKrx55O867rjj3MSJEy2hP2G0CMMIDoQAI0qfcMIJ7tlnn42sUMbnAxEJQYnJe6IQZkqoIgzwEvReclE+C7iWIG5yLaF/9B2vLMLvSBXA/1EyX+iF9uO5iqhL+CAFLxACKWpDP/PV6Ku/piNcY+RrJG9j1aLPKTzCMERzctPWqVPHPNX57JDP8bnnnnNTp051zZo1s/mknLjtttvsARV9efjhh02MP/PMM2Phy2G0X8fMLgE8kXnggiEEB/MWZ7clOpoIiIAIiIAI5C+BcH4V5i9P9UwEMkIALxYqJVKpFevZs6e79dZby3ysXr16xcLiLr/8cvOWCe4E7xnmY4RIsr6scAng9deuXTtL5I9nEYZ3DsIK04wZM2KFZxCO27Zta95Yjz32mAkwUSWHF2DNmjXNYwmvWQQxwikJWURQ2rhxYyy8Mkp9RIAhNJTiGeTOw3bffXfz+CNENOr5tgh19QVDKERDf3yIM31evXq1hQtHacwStZVwXzym+F7weVvxmkJwIxw/rFyctItiHqeccorr0qWLhdjTLjz8CPNlflBk3nXXXd21115rXsaPPPKIiX/0O4pidKLxKtRlXDd95efSGOAlSiVo77GbSlqT0val+SIgAiKQGoH/ywFIXr2oTE4Fz1IbW62ViMBWRT/UdCYlIqRlIpADBEiOPm3aNGsJoZYUYwjeQJVsYqVKldwhhxxScrb9TygWocNYrVq1zIvwwAMPtHC522+/3W7QWMZ63LDJCpMAef+uvPLKmNBFNU9CfREXShpeG5w7VBH2xs07xUIQDAnxi7JRPZWwRTxUfv75Z+sKXlXkp8LDCuEpl42vefIcIv75G2xy5XGNwNMx0bUkl/uVrG2Mlc/viNejNwRePIyi3HcEE7z+Nm/ebN3igQ3XcT53YXn80RAfyjt69OjYZ6V9+/Zu6NCh5p2ZyrnG+YpgRNVnxkkWLgG8n3no4Y3cfBR8wvC67dq1q19kr1zzgzZ//nzzICfFwumnn27fIXiocs6SrxIBe9y4cfae7QgPX7p0ac5fV4N91HsREIFoEOB3nC9u2PQvl7sq2+wUjYb/Xyu//2mzW/D2SPuPB39858tEoKwEJACWlZjWF4EQCKRy0xRsFjdNJMePZ3jEdOvWzSoHx1vOPLw2qP4a5o1kaW3T/OwQIEyvZcuWJnDdcccdKVXoXLdunWNdQoV9qCy55C6++GLXvXv3UPOQpYMa4Yn8eEQM9EIa+yV0GCGQUNRcM4SiDRs2WJEM2kZeOPKBco0olM83ghIej1wT4eEN4RYOCLlRKRjCeYcYE8yVhpcquf548BOW8b3y+OOPW77QTz75xJpBcaB77rnH0gOU9TssrH7ouFsSQNAjLDtVK+lX4AXAVLYnzQlV6H0KhlS20ToiIAIikCoBCYCpktJ6+UxAAmA+j676ljcEynrzlEgA9FDI14bIt2zZMqu2R5J4cp8h1rRo0cKvptcCJjBp0iQLPS9rKCFPJfEW5PzyudfwOONGEq9A8s1F2UrzLCOUESGQsNqyfmbTzYOQZTz+8IDzhlCEVxU5HAvVyJFHLkcvUsEB4QyPAJ6khymiJRoTH8ocrNa80047WchzmMIzYg/5JPv27RvLK0u7KBxEOolCPtcSjWeUllVUAOTBCQVfXnzxRStSww34pk2brNIv5woPJCg81KFDB/MojBIbtVUERCBaBIoJgIdeFk0PwA33GnR5AEbr3Mul1koAzKXRUFtEQAREII8IUC2YUOIRI0aYyEzX8LQiV2Dv3r0t1CvK3UX8QEjCs8yHYtIf8p0hBO69995Z97JDKELgIn+jD1dGICLHX5hCUa6NM8I0npzBCs94RFIRGoE6l8K6Casl3Pfrr782jOQ2xOPP56cMiy1VpAcOHGjhm5x32LnnnmspJuAYtggeFhcdVwREQAREIDcJSADMzXFRq7JLQAJgdnnraCIgAiJQcATIvzZ+/Hh35513mljmAVAMgDyDVCuNsiEEItIgBCKKeMNzEm9cxJBMh5jSBsJcSbhPMR8MbzaEIkJcJcb4USn+SkgtT9GZ8FTyhnjL2IUpmpJ7Ei9O8qR5w1ORXH9hFmxBWKZYR//+/WPCPkVHEPqbNm2qc80Pll5FQAREQARyioAEwJwaDjUmJAISAEMCr8OKgAiIQKERIC/gk08+aR5CVAn1RvhXnz59LPQ86nnpvvrqK/PAQ7RBlMMQaxBumDIRYopnGMKfFx8R+xCvCK0j0b4sOQFELbwB8Z70AipbUTAEb05SJGRLRMWbDu9EKm17L07aQYXjHXfcMXlnMrQG5/OKFStMtPdVwWnXzTff7Hr06JGRcztDXdFuRUAEREAECpCABMACHHR1eQsCEgC3QKIZIiACIiACmSSAkPD888+bELhw4cLYoQ477DDXq1cvR9XQMD2cYg2qwBtEJEQcijV4EceHmCLOkROxoobHGiIR3mtebCR5PtV9cymEtaL9zOb2cCSsGyEwWDCEStaMG96UmRSpqbBK0RZftZgcenhxUjU1WwJkSd4woV0DBgxwEyZMiJ1rnTp1skrx8jAtSUz/i4AIiIAI5CKBYgLgwRHNAfg35QDMxXMrSm2SABil0VJbRUAERCDPCLz22mvu9ttvdzNnzoz1jGIMFBAg+Tz59KJsiHQIdIiBPsQUIceHmJIEv6yGIIOwSDVYv08EKvL8UYBEVnECMPYFQwit9oYHJzkCOUfTKVIjGCP8+WPlihcnXrtUZUX8I8wdq1WrloX7HnPMMaGJkn489CoCIiACIiACqRKQAJgqKa2XzwQkAObz6KpvIiACIhARAuvXr3dDhw51EydOdIgO2G677WZVqbt3724hmBHpStxmlhZiimCHZxl9TcXDi8IqCEW+IAQhvuSEQ5DKpGda3E4VyEwKhuARSIgwwiBGTkdfMKQi3pycF+SOZPKFNDgnEHMRdcMy+rl06VKr7rtq1SprBucoQiCV4tMpfobVRx1XBERABESgsAhIACys8VZv4xOQABifi+aKgAiIgAiEQABvueHDh7sxY8Y4hBcMgQVvwCuuuMK8r0JoVtoOicjjKweTL9Abud3INYdnYDwhEA8xCkJs2rTJb2KiH+JfJvIKxg6iNzECP/zwQ6xgiBepGau99trLxq4s3pwIbHj7IeZS7AOjaAzCH2Hc8c6BWEMy+IZ2cX6S1+/RRx+1I9GWCy+80A0aNMj6GlbbMtjtrOwarq+//rpNy5Ytc0w+byfh1IRXJzNCw0mfMHfuXEceRryAeRjAuUfo/8knn+x4YELIuEwEREAERKA4AQmAxXnov8IkIAGwMMddvRYBERCBnCaAp9uoUaPcPffcE6s0itdV27ZtXe/evV2NGjVyuv3JGofQQh/x/OLVG2InHoH77ruveZnF8xDbddddTSgKsyCEb28hviL++YIhXryDAx5yjB0efIlEMoRthD8v/uC5WbVI/GXKdLXoRONFvx588EE3cODAWP7DevXqWbhvgwYNEvYp0X617H8EEp0TqQiAb775piPs2nv/lsYVMZAHKORSlYmACIiACPxGICgANjvoUldlm7KnYfltb9l/9/1Pm938jffZgXlgTvSHTATKSkACYFmJaX0REIGsEdi8ebN79tlnzVMCbwfynuE1gzcU1ScPP/xw17JlS9elS5eUcp+9+uqrJiotWrTIPKnYx1FHHWXeZR06dMhav3Sg1Ang8UIOsmHDhplY5rc85ZRTrHJwo0aNIi9McJ4jBAa9+wjtRUgiDx2eZxgeYnj54HGWSEzwjPSaWQKleXOStxIhEC+sYFg2Atv7779v4cQIwBhjyZhWJIy4or2kLVwbr7zySuerc1P1+NZbb7VrqypJV5Tw/7YPfmbJI0lV5zlz5tjCVATAxYsXu8aNG9v6CIGtW7d2devWtesE34vTpk0zAZfzEiF51qxZVlk9Pa3XXkRABEQg+gQkAEZ/DNWDihOQAFhxhtqDCIhAhgi8+OKLrnnz5kn3zs0queMIfyrN+vfvb54tPs9WyfVatWrlpk6daiJLyWX6P3wCiCdPPvmkFQzBE8YbnkkIFy1atCgmtvjlUXpF2CbXHD9QvUBE+xEO9t9/f3fQQQeF6iEWJZbZbCtjRYEMxo5qud6o4IvQgzcn3n7vvPOO+/HHH20xVZoRgBB5wzLajeh8/fXXuyeeeMKagWDZrVs3E/+SeTKG1e6oHpewajwqmQj1R/Q/4IADrDupCICItHhEsx8efsUziimdccYZdv0gPQBpA4LCY7xtNE8EREAECoWABMBCGWn1MxEBCYCJ6GiZCIhAqAQQAMk9ddxxx7k6deqYCLLPPvtYsny+xBHs8HogTJI8aORXwqOvpI0ePdryIjGfm6LrrrvOHXHEERbGxw3VvHnzbBO8AB9//PGSm+v/HCKAaEEOLCoHL1iwINayww47zPXq1cu1a9cusjnxEIfI6YWna0njJh6PMsJEo14ZuWTf8ul/wjMRAj/66KNiIq7vI55Z1apVM2Ew6B3ol2frlerRDzzwgBs8eLDDAxU7+uijLdwXrzKJRpkfibIKgKm26K9//at76qmnbPUVK1a42rVrp7qp1hMBERCBvCYgATCvh1edS5GABMAUQWk1ERCB7BNA2EuWE2vGjBnm8UDr8HxAEAwa+dW44f7yyy/tppsbIjwGvXEMtiNcCkMMbNasmb3Xn9wmsGTJEhMCOQe8UZmVYiGdi4qGREUowyuVXC7vvfderAIy+f0OPvhgK4SCoBTMNcf5ixBICLuEGj/yufVKgZe33nrLBQu90EJCfrkehZW/EQGdFAh4za5bt86g0SaEQD4zya63uUU52q3JlAB43333ucsuu8zg4DVN3lSZCIiACIiAswgLIiqwZgf2iGYOwHdHWfuVA9Aw6E85CPyuHNtoExEQARHICoFUbkZPP/10K4hAg7ixLWljx46NJbTHaywo/rEux6DYhD/WHXfcUXIX+j9HCeCxNH36dBMyLrjgArfNNtuY99w111xj4ZUUMyA3Vi4bIaMImYSHEuZMH/BmJLSZEExCSMn3RdETL2iyDTkxqSJKZVFEHVluEGAs8DDgQYMX//BO9pWaGS/Gm+WEBWdr7DgOnqV8TgiXR/zjmodQtH79esv156+BuUFSrSgvAZ8zlO01puWlqO1EQAREQAREID8JSADMz3FVr0SgoAh4b5qgl5QH4L3DqIx45pln+tnFXqmideKJJ9q8l156KXbjXmwl/ZOzBBDMxo8fbx50ffr0cdtvv73lZBsyZIiJaX379nV///vfc6r9VIJdtWqVTbzHk4/CEYh9nI9Bzz5CRQl9R/CsVauWowowhlfr6tWrrYADohPerLLwCDAepCFAUCPEluIZhx56qBVuoHgDIq6/VuGZvHLlShMDCRcuLTdpOnpDaPndd99toaBTpkyxXR577LHW1hEjRlj14nQcR/vIDQIlUyPkRqvUChEQAREQAREQgVwgIAEwF0ZBbRABESg3gQ0bNrg33njDtiepftC48eWGHGvYsGHMCye4jn/ftGlTe4v3BN5VsugRQDi78847TezD+2/PPfe0itH333+/iS/kk1y7dm2oHcPLD2+/1157LVYwAk8/zk+qweIBWJohCuLBSo62+vXrWzgp61IpGdGJKqFUmUV8kmWPANcZwn251vh8eoSiI+biwYmA60VcPDvJyeaLf5AzkHOSsSMklPMjXYbX38svv2zC8Q033OA4FnkkH374YTd//nxrR1BoTtdxtZ/wCPBAYPbs2dYA8tzycEQmAiIgAiIQh8AvRdETUZzidEWzRKAsBCQAloWW1hUBEcgJAggeVDe86667HMKdv2mmCETQEFq8V1RJcTC4Hu+DyxFTZNElsNtuuzkEDwSVe++91/LlcR5MnjzZQmvxBH3llVeyFn4JSR+CyXHJ6cf/VIKtWbOmCTF4LZbFdt55Zyt406hRI4fYhMDki4gQCs+5H88jtizH0LqJCeC1h2cpY/rhhx/ayngaI85SpdWH/Qb3guCG+IcIiEcnnp3M48ED1zTGjteKjB3nFu06//zz3amnnup4SII3ItdHrm0dO3ZUaGhwUPLkPedQ165dY995gwYNypOeqRsiIAIiIAIiIALpIrB1unak/YiACIhAJglMmDDB8leVdoxrr73WnXPOOcUWExbpDe+wROaTArMOiXVl0SeAwHbppZe6iy++2JEMnxyQb775pnvhhRdswhuLkOGWLVuagJapHn/xxRcmwnjvMPJypasSLMIhYhPVrTlvmRDEERkRgRCYCC32+QMz1cdC2y8hvAhreNVheG5StGXfffctFr6diAvhwIQFH3TQQTZWXK8YO4Rrxq88VZ8RDgnrJZcpD0owHpJQ7fzII49MuW2J2q1luUmAfI7ee71Tp04m/uZmS9UqERABERABERCBsAhIAAyLvI4rAiKQFgJ4UI0ZM8bVq1dvi/35JPwsSCaABD2w/E39FjvUjEgSwPsJcbhDhw7u+eefNyGQPFlLly517du3N+9PvKN4H89rq7ydRozZuHGjI8ebNwQiBJ/KlSv7WWl5ZX/st2pRdWCKPSAg4RGEZxoT4dAso3KwrPwEGFO8Kzdt2mQ7wXuPhwsIsInCtxMdsUqVKhb+fcABB9jYIdwydpw3THgMMnbkfiwtZBevvzlz5rirr77azjmOh2fo0KFD7bxWMYhEIxD9Zbfddpuj4BXGdyGVgGUiIAIiIAIiIAIiUJKABMCSRPS/CIhAThKg2i+5z7DvvvvOvfvuu+bVRRVYhB2S3Ldu3bpY24NhdMmEnaAgw/5l+UcA8YQKqExUYsUjkCIxb7/9tuvevbsjb+Dll19unqbJBONEdAg3RsQhH58PQSdkl4IQvGbSEDvx+MOj9eOPPzZvMoqMUA2ZCQEQMYlcgqWJSZlsX1T3TbgvompwTBHkGFNf2KOifUNAZGzIG8jYcTweRlAtmGnWrFmOvG54d/nrGcIf61H5+plnnrEmsJ+ePXtaGHymz7eK9lnbV5zA6NGj3XXXXWc7IpXFs88+a4WQKr5n7UEEREAE8phAUQrAonws0epgxJobLbiF01oJgIUz1uqpCESaAMJF0HsJL4ezzz7bPfroo3ZD3KZNGzdu3DjXuXPnWD/xrPFGfrREhseNt2233da/1WueEiD/GuIxOdEIl5w4caJ5XxFKjjBI2DCiIJ5zqRpiDCIbHmJeREZYJjSUcM5sCm7kBMTbkBDgf//73yYEEorMRNEcPF4Rm2gX68pKJwA/wn19SC1jSsGWvffeOyNjGhw7hD8EPvICUryDEGG8vbp06WL53qh+TeEbf75RzZyHIYSFZ/N8K52elmSSwKRJk1yPHj3sEAj/c+fONXE/k8fUvkVABERABERABKJLQL/6ozt2arkIiEARARLdt23b1uGhQw4kcnN5C3rmJAvrxUvKW0W8v/w+9BoNAlTJRER57733LB8gY//555+7IUOGWAXNK6+80gSYZL3h/Fq5cqWjCidiDCIOIZ1UgvWFHpLtIxPLEYEQMRHMmbygyflO5dpMVJ/NRD/C2CfjiFi6atUqE/9gWbVINKXwSjYEXY6Hp2adOnXM8+/444+384rw48GDB9s8Cj3QTrwGyXNJfsvq1atL/AvjhMnyMZ9++mkr6MJ3H9eYl156ycLRs9wMHU4EREAEREAERCBCBCQARmiw1FQREIH4BPD+wxA1yPHmLVj4I1gQxC8PvgYLfwQLggTX0fv8JcC5gicVobu33nqrCWUIKw888IAJLRdeeKFbs2bNFgA++eQTx7Lhw4fHxOe99trLRCJy8uVS7jU8aMmZiYDli1Xg+RqsPhv0hN2iswUyg7BtUgy8+uqr5tFJt8nD17BhQ/PmJMw624aoN23aNIfoQzvwbuZ6h9cp51izZs2soIi8ObM9MuEcD7GvXbt25hHKuYnnH3koZSIgAiIgAiKQDgJEH/AQnNQSRI3stttu9iA5WGSsvMfhwdW6devchKICj3ix84Ca6AoefDLNnz8/pV3z28dvk+w1pR0WyErZ/xVbIGDVTREQgewR8F5NHJEvLG+E6XFzzA09ed4SWXA5XmGywiRAXrfrr7/evAEfeughCw/+oKgq6+TJk206+eSTbRk/Vgi1RPij2AzhoE2aNDGxkB9JuWz8kENQ8pWDg9Vng5WDg4Vxcrk/6WqbD+Em3NfnDyUdANcRrjH8uAzLEPsQqDnnEGmpcE2Yr8/t+MgjjzimU0891fXt29c1btw41PaGxakQjoswzUMvzgNyPHqvz0Lou/ooAiIgAukjUJRQL2o5AF12kgCSc/i8885zmzdvjuEmDQqV5pkoOjV79mwrPhdboQxvSN/UOZCyqQybatU0EJAHYBogahciIALhEqDqqbdg+C6J8uvXr2+LXnvtNZcoDyBVYTGeQPliIzZDfwqSAMIPTyXxjnv88cfdkUceaRy42UYEREC75ZZbTPwj1JycgXhm5br4FxxMvMjIT3jsscfaK58XnsryeUJkIJz5yy+/DG6St+8R2HwIN+IfnnQIpIwpHp1hiX+MB15/XJPITYnoQ2g5OSvXrl3r8FwmhB0xEONHe9OmTR05LqdOnRorQpO3A1dgHSMkvVWrVub9iUDPDRgh4jIREAEREAERSAcB0p60b9/exD/uqUg1wm9CPM+7detmhyDXNd9FPAAvj/HA1RuFy2rXrm0P0P28sr7yG4konURTWfeZz+vLAzCfR1d9E4ECITBlypRYT6mSGTSqByP+8RSLEDoKh5Q0PKBefPFFm33CCSekrapnyePo/+gRINyTKtOcNxRhuOqqq6yoxkcffWQiESG1PCW94IILYpVZo9bLYPVZ+oXHI096CW9mwiuyalHuO0INwxLCMsWUohrkf8Tz0f8gRfDD6y/MYkC0BfGZ881fmxBs+Z+Kv947kwcWnHtUBib9AaE5hM68/vrrds4SykxxCFn4BMi3uXHjxlhDKC7jjfmEQgWtpHcEY8nDBwr5YKQqwAMQIbg041xmkomACIiACIhAKgR69uxpuYX5/Ttnzhx7EOq3IxcxD46vvvpqK3hHZEL//v394pRfeWg5YsQIC/3ldzS/b9hPvFQ7qeyU30Q1atRIZVWtU0Rgq6Ifmb9JsEIiAiIgAjlEgBsihBe+GEozQjD79Olji/GM4aY5mHeNoiDVqlUzTyZuhFesWGFCht8f4cFnnHGGec4wb968eZZPyy/XqwjgHUbl1WHDhpkHFkQICcVLjIIM2B//+Ed3+eWXmxgT9EK1hRH7w88CQksRAoMegPQLIZBw56jnmqOPH3/8sf2A9Z7B/IA89NBDi10fwhg6nqgj5I0cOTLmtUxoLz+0ySuZTIQlPIdzFY9OwoJluUEAQY+HCKlayZ/nfB8i9pbFbr755pRvzrj5GjBggO2+5LHLckytKwIiIAK5SoAH/j7Pd7Oq3V2VrXfM1abGbdf3//3Kzf/gAVtGBEAw13ncDco4k4eHDRo0sK2IbCEPdkkjMgGxbf369Y7c0jwo5kFyRS34HZTqvRg5AIngIvKBh5+y1AgoBDg1TlpLBEQgBAJ8GSCsXHTRRXYj+8orr1hYIp4U999/v4UuevGPm90xY8YUE/9oMiGZhM5h5Afki43cbtwkE1rXvHnzmPiHpxdfJjIRgAA3wY899piJQoRA+PDL6dOnm/DHjw0KgPDDh7DZa6+91pIlDxw4MFY8IookEZjwGiLPIWEVVKLFqHSMtxGfQzzmEM+jaAhsfP7pC+IfDwx4ok3YLF6OYRk/qp966iljjthH2whD5jo1c+ZMa2My8Y+2M2ZPPPGEQzCSiYAIiIAIiIAIxCGAD1QUpzhdSdesGTNmxHZV2gMnHgB37NjR1sMjHbFOFi0CW0eruWqtCIhAoRHAg+/BBx+0qbS+8wSMPFgnnnhi3FV4ivXhhx86hBnCqBBtSlrLli1tHyXn6//CJPD+++9baC95TzC8wygO0rt375hHKpXRxo0bZ14zFGcYPXq0+/zzz92QIUPcPffcY2GZV1xxRWRDMBGbCP9lQvzDIxCvOXLkUSiD0Fk+e3/6058iEf78008/2ec/WPF7n332Ma+6RF7Gmf4EIDRThIjiHf4JNuHHCMrMo+BHeSzqXprl6XMub4MgWxFRFg9CJpkIiIAIiIAIZIIADhYYv3kT5ZfF484bD4VPOukk/69eI0BAHoARGCQ1UQQKlQAFF/CEOfPMM60IA6GH5KSg6AKeMWeddZZ58yFG4MmXyAht4ovtnHPOMfd/PAbxcmI7ijyQTD1MESBR27Us+wTwBEMsxs4//3wTvPr16xf3HEEEI+QSrzjychEe/N1331noBDkpEZzLm9ck+z2Pf0TCfwn5oGAIgh9ecwhqCKWLFi2yUBDyBuaiIbAR9sOPVC/+0R885ehTmJ97Qqyvu+468z704h8pCfBOvPHGG8st/uXiOKhNIiACIiACIiACuUuAsF6MdCPcb5VmPAD35rfx/4fxykNUIrwISeY3Hb/L27RpY9Fj/FaVFSdQ+sgWX0//iYAIiEDWCZCPi8mH+Va0AY0aNXJMMhFIRmCnnXYy7z5CyKkEm4rhKYeXIOcrYebkccNrbvLkyTbxhPTKK690xxxzTNI8bqkcL4x1+GHFZ5K8mohpiJ78uEJgY0KkJ08g/HLBENj4YUgRIIwftPywJbVAmB5yhPs++eST7oYbbnAUXsHgSk7TU045JbLnh3VEf0RABERABEQg1wn8UhQCzBQlC7TX/3ZI1HyEsFSN6A5fnCrZdvzexUuQHNn+wWqqx8nEeuTj9jm52T9peZhIoUIaqKlTp7rDDjssE4eO5D4lAEZy2NRoERABERCBTBNo1apVuQ5B+GaPHj0sdyUVqvnxsXr1aqumRkW1+vXrmxBI2HmYIlS5Ovd/G5H3EBGQwjqE15NfE69H/yMM4RQhkNdU8tZVpC3xtiV/HgWBaJs3RD/EP7x/wzK8Ed966y0L7cVzEuNHNF6ACMdheiOGxUTHFQEREAEREAERKBsBfksmM35zpGrkR/aWSjE7LwCSIiYs4zf0CSec4Pg9fdRRR1keZ/qxcuVKS8uDd+K6devccccd5yhwQgSLzDmFAOssEAEREAEREIEMEMDbjMIyq1atcs8991yswAw/Qtq3b29FNh599NFYpdcMNCHjuyQUmIp6eDUeeeSRFp7PQcndyQ+wpUuXWt5APN6yYRwHr0TCfb34t/POO5voevjhh4cq/pEs+5prrjEvZC/+tWvXzgTB0sLLs8FMxxCBeATwBsGLuXbt2va5JvUGN5z33nuv++9//xtvk9g8PJ/Jl1q9enXbljyWFNohH2+idAhsxwMDpmT5EnnAwHrx8iKyrd8P+6SAE3laKfRDUSOW9e/fP9ZevREBERCBQifANd9bKg9KK1eubKvz8DcsmzZtmnvxxRftASpCYM2aNV3jxo1dz5497cF7p06drGk8nO7Vq1dYzcy548oDMOeGRA0SAREQARHIJwLcbBLWyYQghkcgldYITe3evbsVp7nsssscFde4yY6i0UfCf8mrSSEUbro/++wzx5NYbvjxisRbcN99992iUne6+ovoSD5Q/zSaH7CIDhT6oH1hGaIkeUbJ6ffJJ59YMwhFoVAMhYvCbFtYTHTc3CbAzRLXqzfeeKNYQ5ctW+aY8GTmGhbPg/mRRx4x72dEt6Bt3LjRMVE4iYJciN7ZMELayKtZsi/ZOLaOIQIiIAKZIsDDZH7fpMuCEQhEUSQzf43n911YRs6/0oxIlbFjx7olS5bYb8Pp06dbWDDRIIVu8gAs9DNA/RcBERABEcgaAZIU88SSkASKg/ADhTwl3AwjCt1yyy3u008/zVp70n0gxCzCfvEawtvmD3/4gwlcPCFG8MTzjerBqfy4TLVtPLV+88033YoVK0z8ow2EeZDvE8ExLIGN0BtEB3I/4vmE+IfAiwCMVygFiMJqW6pstV5hEqDwFtcoqpjPnTvXPluI2D6H0qxZs9yDDz64BRyKaeGRx40hIWQ333yzfeZfe+01K+iF993PP/9sIe/333//FttnYkaXLl3ME6Rjx45W7IvrBDeCXItlIiACBUzg16LIhChO/zdkiH/k6ks0lWV0gw+g/YPURNuT/w9LJVw40X4yuYxIHL4DvC1YsMC/LehXeQAW9PCr8yIgAiIgAmEQoIIanjAIfhR+GD16tHnOIQ6N8UBAFwAAL25JREFUGDHCcbPKzXfVojC3qBo/JqmCTN49cgQidFIwhOrKVA/mKSxegeV9eoxnHftFUPQhxiSmhm3YP0jxRqQiNCKJbxsVyBlf+i3hL6pndWG023v5NWvWLNZhRP2TTz7ZEUqPh+CoUaNM2PYr8Nm+6KKLHMI3nz/EfsKxvPFA4KyzzrKiSiSv79u3r2vbtq2F5Pp1MvHKwwG8QII3gfRFJgIiIAIi8BsBPAB33313i96gqFsiI9LDC4Ckgcll4zvLG79DZcoBqHNABERABERABEIjgBg0bNgwy1uHYLTnnntaMQ0EQXLqERacKGdWaA0vw4ER+BDlyMtC4RC8HhHFqBxHrj76F0w+ncquCevDq4iQQvZFLhrExjp16oQq/uHdNKEo/1itWrVM1KVtNWrUcC+//LKbOHGiPamX+JfKCMdfBy/KZ555xt10002uRYsWsXxuMI2XCy7eXhgTvNsYJ4r11KtXz84f9sE0f/78eJsV1LzLL788lrM02HG8e7kmYXxuqbLtDa86n3eT6tZB8c+vg+BPXkHs22+/tWrpflmmXo8//vhi4l+mjqP9ioAIiEDUCXixjN9WiXK9EtHhzXuG+/9z7VW/ubYcEYUAb8lEc0RABERABEQgqwTwXLv++uvNo+2+++5zBxxwgIXKPfnkkxZKSw4rPGrKUtEtqx1I4WDk5DvwwANNCDz00EOt4i39+fjjjy1HC0VD8JxL1EdEA8JqCaHlPT/sqhZ5SRLu68ONU2hK2lehzYQWkoT60ksvdQiUFB+56667bD4V6PQjtOLYyTN56qmnWg65559/3jwVyrpXCu9QnAIhizDU5cuXpzUkvaztycX1zz333FKbhciOcc7jyeuNROwY5znpDUozvP74bGB+m9LWTcf8RH1Jx/61DxEQARHIFwLHHnusdQXvPn7TlGbBUFqKwOWy8cDPG2lhZPIA1DkgAiIgAiIgAjlDAG85vJLeeecdN2nSJHfUUUdZ20i6T1J+vFnIv4UXU1SNysHk6ONHI95xPlyXoiH84CSxNSGGQSEQzzqeSOP153MkEqrSsGFDK/RBnpcwjDYi9hGu3bRpUyuQQDsI4eZHJ1XnUqmmF0bbo35MziHyK5bVgucV3qiEg+I9KvuNAB67pRlegN6Cnrtr16612Ty8wJO5NOPzgIcs5rcpbd10zMeTWiYCIiACWxAo+v4u+qERvWmLjqRvxumnnx7b2UMPPRR7H3zD70+KPWEU4eABZ64aXozjx4+PNa9Jkyax94X8Rh6AhTz66rsIiEBkCVxzzTXmaYG3BVMqYWvPPfecVUMkYTAhk7ziWcZ8WW4RQNA6++yzzdMNTyefiwtxjPl169a1H2DpLKaRbQJUECWJNbnBEGG8sLB582Yr6vHqq69amDD5wniPtxE/PBFJCS9ERNh+++2z3ezY8RAlyeNIO/iBibBEuxYuXGjhpWEWIIk1Ms/eEPqLAI7XKPkfCZUvqxHiRJ5NxGTONURnil7IfiOw3Xbb/fZPiXfByr98BrzhvYtRCTyZ4a2L+W2SrV+R5XhXy0RABERABJITqF+/vkVpsCa/b/ieLGl33nmnW79+vc3u2bOnpXUJrsP9iL83STU1R3D7VN/PmzfPffHFF6WuTl7arl27xtpK9ECu5ysstTNpXhDOI/M0d0K7EwEREIFCIkAIJKGFqRqiCcnZ+TIPGslwmWbMmGFfktxMB2/uguvqfTgE+BFF4n2mpUuXWhEJxmvDhg3ukksusVBI8nURzhis4BZOa8t3VPqINx8TOcUQdvAAJMQ3mGeGcxPvIvKI4UUYliH0IcRSxICwZQyRgYIuVPvFq0yWGQIDBgyo8I65wWGSZYYAn+dcsjCvFbnEQW0RAREQgVQI3HPPPRah8d1335mX/XXXXWdefvz/xBNPuDFjxthuDjnkEHfllVemssu465CHN2jc23jjwfcHH3zg/7Vicj482c98+OGH3WmnnWYTD8lJLbPTTjs5KhjzYI92+vBfHkzRL9n/CEgA1JkgAiIgAhEi4MU83Nr5QiMpfjIjt5wX//BWuvrqqy0XG9VYhw4dal5mVEkkbGvw4MHJdqflIRFo0KCBmzZtmol/JNInBIOk+/369TNhEPEJUTBR+F1ITU/5sOQGw0OLm3ZfUCC4Mec9T3XDuKlH+OPz1r9/f0ceOf5H7EB8HTRokCM/Xa6JH0F2ei8CmSTgPXgR75MZHpyY38avH3wAxXddIvMVKBOto2UiIAIiIAJlI8B9wuTJk915551nXvIIgCUN8W/27NkVevDsC0qV3Df/33777cVmd+rUyZUUAFkBse/xxx+3qdgGgX9I8YFwyQNk2f8IKARYZ4IIiIAIRIgAoWvLli2zqqpdunRJ2nJyyVFlFiNslKqrhJBS+ZLXxYsX23yWIyqRZ02W2wR4yolgS0gsXmjk0CMMgh9M5O7q06dPsSenud2b31qHoEa4L+eoF/8I9yVckJBoBAG8Azln33rrLfvh99vWmX2H8PjAAw9YqDLCK231nyfGIswCJJntufYuAqkRIJ8nxnXJ5+mMtyUCPkV8ML+NXy/oxfz555/72Vu8EjpMzlCZCIiACJSbwC9F+f+iOJW7w6lvSLjsm2++6Xr37u0Q+0gLQb4/fvfwW5Nr+EEHHZT6DjOwJqmQhg8f7tq1a2ffJTyEJQKD38QUnGvfvr2bMmWKtdVXN85AMyK5S3kARnLY1GgREIFCJPD3v//d3XjjjdZ1xAjyXySzu+++2yFeYCNHjrT8acFt+FJnPsUUWI8vU6rQynKfwB//+EcTbXk6SzVTxpobb0K5EaXOOuss+/EWhST4FBMg3Nfnc8HDr1q1alYsBK8gzk3C1fkMfP/99yYQIhLusccermrVqhaCm4kRQ+gjBw5hLvwYxghVHjhwoOvWrZsJk5k4rvYpAlEjcOKJJ7oHH3zQxHGSx+NpHs+mTp1qof4sY5ugEUrPTSbXAaozl2Z4c/DZlImACIiACGSGAOlWSDdUlpRDtIRw3FSuz6msk6hnhx12mGOi2JqsbATkAVg2XlpbBERABEIjcOmll5rXE67wVBxNZny5zpw501bDM4xiC/GM+XiVYaxf0S/leMfQvMwR4KYZERDvuFGjRlmYA8n5n3zySRN2qeq2aNGinBxXvIFIJr1kyZKY+EdhECoEI+z5kEA8APkxyvzq1avHKgdTgRehgJx8hOem69xlP4QpIvI1b97cxD/aQi5N2tu9e3eJf5k7pbXnCBLgOkPhG4yQ+DVr1mzRi3/84x/mtcwCHj7FCwHzVRr5LiJNRUkj/6l/EFZymf4XAREQAREQARFITEACYGI+WioCIiACOUEAMeeZZ56xnEk+pDdZwwjF8qGUyQRDvxwvq2Di3WTH0PLcIUC4LDkACfueNGmSVaSldXPnznWnnHKKJXGmgmqy3FrZ6BEC2z//+U8L9+UVI/yP0HTCAqlSHc8Q4RAZEK2puIu3EEbxkNWrV5u3HudwRfqIKIkXLHlw4IiRfxFPQDxvybGoXH+GRX9EIEagUqVKlnSdzwbVlRHr8ZSlgjcFjPAuJ3zMfyfxPYYHb0nr0aOHzSLhPJ4k5K+l2A7VtW+++Wb7LJI7MMq5Tkv2Wf+LgAiEQAAv4ihOIaDSIfOLgATA/BpP9UYERCAPCRAO1bNnT+sZuTfi3TTF67avfsUyPAATWXA5Hk6y6BLAW478jtw0U0ntuOOOs86QO5L53ISTx+7HH38MpZOczwgCnGeIbbSX8w+RzQt6yRqGyIAAgGDIREEcjMIAnPfkCUTI9uHvyfbHckRJRIZGjRo5cssgYnAMwhrZH5VjJfylQlLrFCqBVq1aOcJ/EfAJ67/ppptMCESwJzcpXrqE91NsiocV8YyK51dccYUt4uFA165dXZ06dczrnUrbFAp6+umnzYMw3vaaJwIiIAIiIAIiUDoBCYCls9ESERABEcgJAuRSIhwRj4pUCn/4RnvPKv7fb7/9/Oy4r/vvv39sPmFasugTQKziZvrll182we3MM880AYsQOm6+CaW955577EY9G7394YcfrHgHQiTiAEYeQ85rzr/yimuIhkcddZQJd+yP/XCsv/3tbxb6zCv/l2YIf3glXXjhha5FixYmICJSEHKPSIkAgUgpEwERSE6AFBXk8+ShFfmZtt9+e8s9S1J2QupJHk/l8kTGdYnKjoQD77TTTrY9aSquvfZae7DBfmUiIAIiIAIiIAJlJ6BftGVnpi1EIG8JcBNMldk5c+ZY7p1vv/3WQk7xriEsDzEBEYEf5CWNEDzyjxGmSggi/5MsH28jbggoSJDsBh9RYMyYMVZaHi8eKv1VqVLFqjlR/h3vJcSCQjJyt1HQAQGC8MNkDINsvMjCPKpiJTJu0rx9/fXX/q1e84QA3mtPPfWUQ/yj2jMegHzeyR04dOhQy22HKOg96dLZbcJxEZXfe++9mEceXjx4/cW7lpT32JzDVHpDaKBYCAI4HoB4Ar744ovmxYdnX7AoCl6QXLeGDBkSEyW5xiBA1K5du0yft/K2W9uJQC4Q6N+/v2NKZoTlIponsqpF+TspSlQR69Chg2Mqzfhcl2adO3d2TDIREAEREAEREIHiBCQAFueh/0SgYAkgNLVu3drC3oIQCNlhWrt2raPyHuGnrBe0l156ycqtf/bZZ8HZ5rWGIMjUsmVLN3ny5FKFKG7Q+bFPUv+gESL4xhtv2HTvvfcmvfEIbhv194gTFB3gZqt3794mwpalT1RL9UZ+pkQWzLlG7iVZfhLAiwZBecCAAXaDjqhMSC4iIOJ/x44dzXOHG/h0GCI+3kCE5mKchwcffLCj0EdZxOyytIVzmWMccMABJgJSHAXxkxx+FBYg3yVVfTl+3759rX3sf++99zYh8Pzzz7cwxbIcU+uKgAiIgAiIgAhEjECShxkR642aKwIpEZAAmBImrSQC+U2A8Di868h5RSJ+PIHIG4Y3ECIUxSRI5D19+vQtQLzyyisWNodQxw305ZdfbuF4JOrHwwjRb+LEie7ZZ581T0BuxEvavHnzbB946xB6xw14mzZt3J/+9CeHiIU34HPPPecoYFBIRp4kxBM4kPy8rIb3pLdk+d6CIZIUk5DlNwFCZfEExAPw/vvvN483hH48cEm6j6cvObuC3nJlIcLnFk/gTZs22WaIbYT5VqtWzW2zzTZl2VW518VrFiGTzw/XEzwC8UScP3++TXgh4qnMNeeyyy6zzxgVlWUiIAIiIAIiIAIiIAIikI8EJADm46iqTyJQRgKIeIh1GHl3Snr4kcAb7zyq+BEW7A3R77zzzrNE/lQZRdzbbrvt/GILoWNf5PHBk23atGlWkbR58+axdRAK2AfiH9vOnj3bKv/FVih6Q1J+8nAVUm46hL/bbrvNMIwcOdLyKAWZpPIeMddbsrBe76HF+snChf0+9Rp9AgheiIB4mE6YMMFRmZNQ3SlTptjEZxUhsHHjxil57BHui8cd+/CVeDkG4b5hnVdUDubBBPkzCQHGY5k2Iv5heAoS7htW+6J/FqkHIiACIiACIiACIiACUSCgIiBRGCW1UQQyTIACE94Q60ozPGqCObsICSYPD55m5BQLin/BfZD4mxxkGCJD0HwuMubh8UZ+odIsWKiitHXyZT5iK157eEwhusK65ERYtjcKPfjlXswLFv4IFgTx2wRfg+JqIXEOMijk93h94vlLjsBJkya5mjVrGo65c+eady4ewVTe9KJePFZU9sVTeOPGjbYeobhHHHGEVfAMU1wjhJ68pg0bNnTjx4838Q/PwFq1alk3aC95SskdSN6yZGJ5vL5rngiIgAiIgAiIgAiIgAjkOgEJgLk+QmqfCGSBAPm4vD300EP+bdJXBAGMnFp77rlnwvW9sEgerqCRHxAjgT9Coex/BHxILp5UPhl6yddgOPXAgQNj63366ae2EwoieMOjMJEFl6vCYiJS+b0MkZ90ACtXrnTPP/+8pQKgx1Tu5fyjqM/DDz9s4rQn8dZbb7mTTjrJ4S1IxV3CffGqo5jGH/7wh5Q8B/2+0vmK8IenH+0+44wzTJgk/Ji8f+QVpY+I6BQLoN+I4HhCEjJ84403Wu7TdLZH+xIBERABERABEcgRAuT/i+KUI/jUjOgSUAhwdMdOLReBtBGgwi6eZohNvXr1co899pjdMCPa1atXzxL3xzvY8uXLbfYLL7yQ8k1+0NuQjVetWmX7qFOnTqkehLaC/pSZACKMz8W4YMGChNsvXLjQlpMbDu8oWWETQMSj6jfT66+/7m6//XbLAYqHYI8ePdytt97qLr74YhPYHn30UUsDALEVK1ZYQaDSvIGzRZXUAnjR3nnnnc4XtTnhhBPMw6969eqx6xXveeiBgI733+jRo93nn39u/SNEmMIostwhsHjxYhNyfYuCRaPw5CzpYY64G89Krocg7A3hG892bwcddJDjO1ImAiIgAiIgAiIgAlEnsFXRE/Ii+VsmAiJQ6AQotPHXv/7VrV+/vhgKQgMRAqkO2r59+2LVMfHaC+YELLZhgn+Clx32z806+yaEVZY6gf79+1s1V7agkEq88GnEGoo8YHhfks+xpC1ZssTCI5nP+vfdd1/JVfS/CFh4MIVD8AAkZyfedOQBxfD0GzRokHkPhomKawsFg8j1xwMNjJB22t22bVtHPsBEhvhHZWQ+A1RGR0SX5Q4BBD3Ov1Qt+F0T3AaBO1UjPLykYJjqtlpPBERABEQgdwiQDsenuWm2d2dX5fc75E7jUmjJ9z9/7eZvmmBrErUQTPWTwuZaRQSMQOJfwoIkAiJQMAQIF12zZo15+Vx44YUOrwcM7xk8/M4991zXoEGDYmFxP//8s63TokUL25btU5lsI/3JCgE8OqlyilEIwXtD+YPzP/MxwiBZXyYC8QgceuihVhDEi8iIf5wzeNGRN48cgFQMD8MQet599117iIHQh/hXqVIld+211zpClHnAkEz8o90ULOnXr595Nkr8C2MkdUwREAEREAEREAEREIFMEVAIcKbIar8iEEECCEWnn366TTT/o48+sjxgeMMQ2sdE2N/06dOtd7vvvrtVD6ZYRY0aNcrV4z322MPxRI5jydJP4JBDDnFXXXWVGzJkiCNkm7xseEdR8ADBhNBOH4bNegcffHD6G6E9Rp7A5s2bzduUkFi8/7DTTjvNIQrikYUAOGbMGDd27Fh31llnmVB45JFHZqXfFL256667LOTX584kdJkQYKoPl8XbyzfYi+b+f73mBgE88dLhjVeaZ2Bu9FKtEAEREAERyDiBX35xbquiKUpGm2UiUEEC8gCsIEBtLgL5TIDiIBdccIGFjtauXdu6StEO70Xmq2giLCEClsf8ftlHecKJy3PMQtuG0Ey8OjHEPoo8kNuRVy/+denSxfKeFRob9TcxAar+UqkbIRmRDfGPIjEvvviimzlzphs6dKjlSyPMnDyirD9lyhQLKedhArklMyW2cKxZs2bZuYzAjfhH/kqK4zz77LPWzvKIf4mJaKkIiIAIiIAIiIAIiIAIRJOABMBojptaLQJZJUCuLyr9YggAX3zxhb3HAwgjWX5ZqgfbRv/359RTT7V3iH94EMnST4DQx3HjxrnZs2e7Nm3aWGEQwiMpEML/iCV4bqUSIpn+1mmPuUyAkHByoG3atMntuOOOJgKuXr3aUVDDG3k8u3fvbjkCyeNZs2ZNWzR37lxHeoDjjjvOUTEcwS4dhqBIteEzzzzTRGwq/VapUsXdcMMNloKA+TqX00Fa+xABERABERABERABEcgnAioCkk+jqb6IQDkJkOwebz+f96/kbvDua9iwoVu5cqXbYYcdrEomub/wuCFklES0zEdgomBIaUYFR/IGejGR9SgAwnH/9a9/WRVgxKjg8uC+CBVWwtsgEb0XgcwSwEMUb1FygBIuTrGPZIZAh/iHVx7FabzhRYigiOdp5cqV/ewyvRJqTEEPQpG913GrVq0s3JfriDz+yoRTK4uACIiACIhAwRAoVgRkj/OjWQTk34/aeKkISMGctmnvqATAtCPVDkUgegSoJjtw4EDXuHFjx800ubv23HNPC/V95513rCrm66+/bh3r2bOnu/vuu2OdpIIs1WcRA8mbxc09oX8k0Mfjh9x+5A4kbyAFQkaOHOkuu+yy2Pa8QSQ46aSTzLsQYfH888+3fSD2sd+3337bvNTwIuJ/mQiIQPYIUNijvAUxli1bZsLhtGnTYqHAPGzgGkDYOV6FqRjXkhkzZliBDn7AY+SxvPPOOy0XoYS/VChqHREQAREQAREoXAISAAt37NXz3whIAPyNhd6JQMESQAAcMGBA0v4TLjpp0iRHyF/QEAHbtWtnnoDB+fHeUzCgY8eOWyyi0nCHDh3Mu3CLhYEZmconFjiE3oqACKSZwIYNG9ywYcOsYAjVg7FddtnFXXTRRe6SSy5xe+21V9wj8nln2759+8a8Cbn+UN2Xedttt13c7TRTBERABERABERABIIEJAAGaeh9oRKQAFioI69+i0CAAGF1c+bMscT+hPx9+OGH7pNPPrE1CPmrX7++iXZ4B5ZmeOZRnZGk/Ozj3//+t+XhwpOQogGE9VIdlKqhpRm5BSkmQKERbvr5f/vtt7cQYUKLzznnHAtHLG17zRcBEchtAlxb8CB+4IEH3FdffWWNJX8fXr94Fwc9Dak8TNgxVci9aIh3MV5/rCevv9wea7VOBERABERABHKJgATAXBoNtSUsAhIAwyKv44qACIiACIhAgRLwYj9ioH/YQOEOCnj07t3bkXrg+uuvt4cRICJ/4PDhw62oiIS/Aj1p1G0REAEREAERqACBYgLg7udFMwfgZxONgHIAVuBEKPBNVQW4wE8AdV8EREAEREAEsk2A8N9+/fq5Dz74wLx+q1WrZjlDp06d6o455hh3wQUXmPiHB/Ctt97qqDzcsmVLef1VcKAQW/Gwvummm0xM3WOPPYwpomrnzp3LvPfnnnvOnXHGGVacicIu5G3lf+bLREAEREAEREAEREAEcovA1rnVHLVGBERABERABESgUAiQz6979+6ua9eu7qmnnnLkI6XoD4bgR/jvn//8Zwl/aToh9t5777TsiaIs5G8cN25csf1RzZ2Jgi2M6ejRoy0VRLGV9I8IiIAIiIAIiIAIiEAoBOQBGAp2HVQEREAEREAERMAToPp3+/bt3bp166w6MOISnmpVq1aV+Ochpfn1T3/6k1VfL89uCc/24l+tWrWsOBSV4ikSxf/Y2LFj3Q033FCe3WsbERABERABERABERCBDBBQDsAMQNUuRUAEREAEREAERCDXCNx8881WSKlevXoOb0BCsH3hlU6dOlkhp2RtJj9j9erV3X//+19Xt25dt3DhwmKV4b/99lsr+rR8+XKHsLt+/Xor5JRsv1ouAiIgAiIgApkkUCwH4K7nRjMH4OePGSLlAMzkmZLf+5YHYH6Pr3onAiIgApEmQG6yVKZmzZol7afylSVFpBXynMCAAQNc69atTfwrb1cp3IL4h40cObKY+Me87bbbzubznvUo3iITAREQAREQAREQAREIn4AEwPDHQC0QAREQARHIIAHylZGPjJxy5CYjR9mPP/4Yy1XG/G7dulkRigw2Q7sWgcgT+PXXX93MmTOtH3/5y1/c0UcfHbdPzD/00ENtGeuznUwEREAEREAEREAERCBcAioCEi5/HV0EREAERCAFApdcconr0aNHqWtSLbY0K5mv7Oqrr3YHHnige/fdd93QoUPdqlWrLF/Znnvu6QYPHlzabjRfBAqewPvvv2/VmQHRtGnThDxYvmHDBhPag6HGCTfSQhEQAREQAREQAREQgYwRkACYMbTasQiIgAiIQLoI7LXXXq5GjRpl3h35yoYNG2bblcxXRh600047LZav7I477nAXXnih8pWVmbI2KBQCFGnxhgdgIgsuJw+gzzWYaBstEwEREAEREIFsEPj111+KvNN/ycah0naMqLU3bR3XjtJKQCHAacWpnYmACIiACOQSAeUry6XRUFuiToAE6t72228//zbu6/777x+bT7JymQiIgAiIgAiIgAiIQLgEJACGy19HFwEREAERyBAB5SvLEFjttmAJfPXVV7G+77DDDrH38d4Ew/K//vrreKtongiIgAiIgAiIgAiIQBYJSADMImwdSgREQAREIHsEypqvjJZRIIR8ZTIREIEtCXz//fexmZUqVYq9j/emcuXKsdnfffdd7L3eiIAIiIAIiEDoBKhN9UvRnyhNqqcV+mmTDw2QAJgPo6g+iIAIiECeE5gyZYo7/PDD3Xbbbed23HFHd/DBB7tOnTq5efPmldrziuQrK3WnWiACBUygSpUqsd5TSTuR/fDDD7HF2267bey93oiACIiACIiACIiACIRDQAJgONx1VBEQAREQgTIQQMyjkACeRIQTbty40T3yyCPu+OOPd2eccYb78ssvt9ib8pVtgUQzRKBCBBDfvSUL6/3mm2/8qi5ZuHBsRb0RAREQAREQAREQARHIGAFVAc4YWu1YBERABESgogTw+KNS7wknnOCoKoqQ8Omnn7oFCxa4Bx54wH322WduxowZrk2bNm7u3Llum222iR1S+cpiKPRGBNJCIFj4Iyiwx9t5sPBHsCBIvHU1TwREQAREQAREQAREIPMEJABmnrGOIAIiIAIiUE4C5OTbZZddtti6efPm7vLLL3ctWrRwq1atMkHw/vvvd1dccUVsXeUri6HQGxFICwHC8L29/fbb/m3c1+Dyww47LO46mikCIiACIiACoRD4tSihHlOULGrtjRLbAmqrQoALaLDVVREQARGIGoF44p/vw9577+2mTp0a8/obOXKkX2SvyldWDIf+EYEKEzjggAPcvvvua/vBCzeRLVy40Bb/8Y9/dFWrVk20qpaJgAiIgAiIgAiIgAhkgYAEwCxA1iFEQAREQAQyQ6BatWoOb0CMvIAffvhh7EDKVxZDoTcikBYCW221lYXbszM8/JYsWRJ3v8z3HoCE57OdTAREQAREQAREQAREIFwCEgDD5a+ji4AIiIAIVJBAMCyRkGFvylfmSehVBNJHoFevXu73v/+97ZAwfArzBI3/mY9tvfXWjvVlIiACIiACIiACIiAC4RNQDsDwx0AtEAEREAERqACB0ryLgsKg90Yq7TDB5cpXVholzY86gcWLF5unrO/Hv//9b//W5k+YMCH2P286d+5c7H/+OeSQQ9xVV13lhgwZ4pYvX+6OOeYYd80117gDDzzQvfvuu+7222+3vJysy3oHH3wwb2UiIAIiIAIikDsEfvmlqC1METJrc4Taq6bmJIGtfi2ynGyZGiUCIiACIiACKRBo3bq1mz17tq1JZVJyjmF8veEFSFgwFYTXr19v8+P9QfRDBGRbqpeWJirG21bzRCAqBBD0Hn744ZSbW9pPxF+KbkK6devmxo8fX+q+unTp4saMGeN+9zsFm5QKSQtEQAREQASyRoDfiL4qfdMd2rkqv9s+a8dOx4G+/+Ubt+DrJ21X/FYNRrqkY//aR2EQ0K+ywhhn9VIEREAE8pLA+++/7+bOnWt9wwPJi3/MUL6yvBxydSoHCCDqjRs3zoR3cvxRGKRSpUr2yv/PPvusGzt2rMS/HBgrNUEEREAEREAEREAEPAF5AHoSehUBERABEcgpArNmzXItWrSwPGLxGrZp0yZbvmrVKlt85513uj59+hRb9Z133nGEAv/888+ubt26jsqk2267bWwd8pU1adLEQhnJV7Zu3TqFLMbo6I0IiIAIiIAIiIAI5AcBeQDmxziqFxUjoByAFeOnrUVABERABDJEgEICP/30kzvrrLNcw4YNXdWqVU28I2/Z/Pnz3ejRo53PYXbssce6Sy+9dIuWKF/ZFkg0QwREQAREQAREQAQKmwBZ0KKWCS1q7S3sMyxney8PwJwdGjVMBERABAqbAILf//t//y8pBARCwg132WWXuOsqX1lcLJopAiIgAiIgAiIgAgVDoJgH4PZto5kD8JspNl7KAVgwp23aOyoPwLQj1Q5FQAREQATSQYBiBQsWLHCvvfaae++998zbb/PmzW6HHXawJM6NGjVynTp1Mu/ARMfz+coQCilKsGzZMtvXHnvs4erVq+cuvvhiCyVOtA8tEwEREAEREAEREAEREAEREIEoE5AHYJRHT20XAREQAREQAREQAREQAREQAREQARFISCDoAdhk27Mi6QG48LunrI/yAEw41FqYgICqACeAo0UiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEHUCEgCjPoJqvwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAgkICABMAEcLRIBERABERABERABERABERABERABERABERCBqBOQABj1EVT7RUAEREAEREAEco7A999/70aNGuVOOOEEt+eee7pKlSq5fffd17Vs2dI98cQTOddeNUgEREAEREAECobAr786F8WpYAZIHc0UAVUBzhRZ7VcEREAEREAERKAgCWzYsMG1adPG8Rq0jz76yDE999xz7qGHHnJPPfWUVbUOrqP3IiACIiACIiACIiACIpAJAvIAzARV7VMEREAEREAERKAgCXzyySeuefPmMfGvbdu27plnnnErV660V/7H5syZ484+++yCZKROi4AIiIAIiIAIiIAIZJ+ABMDsM9cRRUAEREAEREAE8pTALbfc4v7xj39Y726++Wb35JNPulatWrlatWrZK//fdNNNtnz27Nlu6tSpeUpC3RIBERABERABERABEcglAlv9WmS51CC1RQREQAREQAREQASiSODnn392u+++u/vyyy/dn//8Z/fuu++63//+91t0hfWqVavm/v73v7s6deq45cuXb7GOZoiACIiACIiACKSPwD//+U+3//772w6bVD7DVdlq+/TtPAt7+v7Xb9zCH6bbkXjQuN9++2XhqDpEvhGQB2C+jaj6IwIiIAIiIAIiEAqBv/3tbyb+cXDCgOOJfyxjPsuxFStWuPfff9/e648IiIAIiIAIiIAIiIAIZIqABMBMkdV+RUAEREAERCADBPAaI7S0bt26Vl22SpUq9kS7cePGFlq6du3ahEelAMUZZ5xhT44rV65sr/zPfFnFCHz22WexHey9996x9/HeBJcvWrQo3iqaJwIiIAIiIAIiIAIiIAJpI6AqwGlDqR2JgAiIgAiIQGYJjBw50vXr18998803xQ5EWAvT4sWL3ebNm93dd99dbDn//PLLL+6iiy5y48aNK7bsX//6l2OaMWOG69q1qxs9erT73e/0fLAYpBT/2WGHHWJrEgacyILL161bl2hVLRMBERABERABERABERCBChOQAFhhhNqBCIiACIiACGSewK233upuvPFGO9AhhxziunXr5urVq+d23nlnh+fZqlWr3PTp00sV766//vqY+EdBiquvvtodeOCBlqdu6NChtv3YsWPNq3Dw4MGZ71AeHuGggw5y22yzjfvpp5/cwoULE/YwuByvTpkIiIAIiIAIiECWCFgZhF+ydLA0HUalG9IEsrB3oyIghT3+6r0IiIAIiEAECLz00kvuxBNPtJZ27NjRIdQhNMWzH3/80VWqVKnYonfeecdVr17d/fe//7XQYcSnbbfdNrbOt99+65o2bWrFKLbeemu3fv16h5glKzuBU045xb3wwgu24eOPP+46dOiwxU4mTZrkzjnnnNj81q1bu1mzZsX+1xsREAEREAEREIH0EihWBKTS6UVFQLZL7wEyvLfvf/3WLfxxhh1FRUAyDDuPd68YnzweXHVNBERABEQg+gQI3b3kkkusI0cddZR58ZUm/rFSSfGPeYQEI/5hhBEHxT/mbbfddjaf96w3fPhw3srKQaB///4OERXr1KmTw3MTDz+8Annlf+YHx+m7774rx5G0iQiIgAiIgAiIgAiIgAikTkACYOqstKYIiIAIiIAIZJ3AnDlzHNVlsWuuuSYmLqXakF+LQkZmzpxpq//lL39xRx99dNxNmX/ooYfaMtZnO1nZCcCRPIqIgIh+hG3/+c9/NsGPV/5n2V133RXb+Y477hh7rzciIAIiIAIiIAKZJfDrL7+6KE6ZpaK9FwIBCYCFMMrqowiIgAiIQGQJTJkyxdq+1VZbOUJFvf3nP/8xYZDXRPb++++7Dz/80FYhzDeR+eUUBfnggw8SraplCQhceOGFbunSpVZtefvtt4+tifB32mmnuZUrV1ootl+w6667+rd6FQEREAEREAEREAEREIGMEJAAmBGs2qkIiIAIiIAIpIfAkiVLbEdVq1Z1eIqRV+6II45wu+++u6MYCK947g0bNsz98MMPWxw0WGEWD8BEFlxOHkBZ+QnUrl3bTZs2zX3xxRcW+rtx40b31VdfmTcmnL1XJ0cgP6NMBERABERABERABERABDJJQAJgJulq3yIgAiIgAiJQAQLk/3v77bdtD3vssYfr2bOnO/fcc93atWuL7ZUiH1dddZU7/vjjTXAKLiTptbf99tvPv437uv/++8fmk2BaVnECeP3BlYrLVapUie1wxYoVsff169ePvdcbERABERABERABERABEcgEAQmAmaCqfYqACIiACIhAGgh8+eWXDhEQW7NmjRsxYoTbZ5993MSJEx2hv1TvXbBgQSyv36uvvuoIPw0aXmfedthhB/827mswXPXrr7+Ou45mVpzAzz//bN6B7AlxsFGjRhXfqfYgAiIgAiIgAiKQGoFfi35bRXFKrXdaSwRKJSABsFQ0WiACIiACIiAC4RL45ptvYg34/vvvrVrvvHnzzAuQvHFU823SpIl7+eWXHRWCsen/v737eaUuCAM4/lz3LdkqGz+KRJGUtayU8qMsWNgKWSklWbCTsLMWdhKbN3+AkhSh/AUsKBEL2XhfdO/rmZoxrnPR29zcc/tO3cx55pw5cz7Xxpg5z+/f5v1z9kK9zhY/86yN+T+Li4vdIZlpHUXwyurqqtkWrB2Pjo5KMpkMfg86RAABBBBAAAEEEEDAF2AC0NegjgACCCCAQB4J+FtGdVjDw8MuU68/TJ0InJubc6HNzU1X9/t4enpy8aiK/w5B7ZPyfwKaRCVb0cna8fFx06zvcJyYmMh2KnEEEEAAAQQQQAABBIIJ/ArWEx0hgAACCCCAQFABTfrhl46ODv/wXb29vV30fXMvLy9yfHzs2vw+vtrW6684/Gq7sLsBlQ8CTU1NohmVu7u7TYIPXVl5cXFhVmeur6+bbd2lpaWytbX17r2AHzoigAACCCCAAAIIIIBAIAEmAANB0g0CCCCAAAKhBXTiqKysTG5vb03XfpKOzHvpSj9NFHJ9fe3O13P8xB9+QpDM6/XYT/zx2b2iriX2JvD8/Gyy/W5vb78FvZpm/dWJQLtt22uiigACCCCAAAI5Fkin0pJOpHN8l7Ddp9PxGm/Yp6e3UAJsAQ4lST8IIIAAAgjkQEAni2zR5BGfFduuKwFtaWxstFWXUdgFMio247CGGxoaMlo5/K7AysqKDA4OmtV/utJP371YUVEhnZ2dsra2Jqenp0z+fReT8xBAAAEEEEAAAQSCCLz9hRCkOzpBAAEEEEAAgZACmuRjd3fXdHl+fi4tLS2R3T88PMjd3Z1p08kmW2pqaqS8vFyurq5MxmAbj/q5t7dnwnp9dXV11CnEviEwMDAg+qEggAACCCCAAAIIIJAvAqwAzJdvgnEggAACCCAQIdDX1+eimuE3W9E2uz2kra3NnZZIJKS3t9cc6wq/w8ND1+ZXNG5XAOr5eh0FAQQQQAABBBBAAAEECkOACcDC+B55CgQQQACBAhVobm42W0f18TY2NmRnZ+fDk+p7/2ZmZkxct5vq9lO/aNbZZDJpQmNjY/L4+Og3m2ONa9HtwzZL7buTOEAAAQQQQAABBApBIJ0SieOnEOx5hh8VYAvwj/JzcwQQQAABBL4WWFpakoODA7m/v5eenh4zQdfV1SUlJSVydHQk8/PzYhN8zM7OmvfN+b3W19fL5OSkLCwsyMnJibS2tsrU1JTU1tbK2dmZLC4umvfS6TV6Xl1dnX85dQQQQAABBBBAoGAE/sqf1wnAeD2OGXO8hsxo81Ag8bpdKGa/+nmoyJAQQAABBBDIscD+/r709/fLzc1N5J10y+709LToBGBUSaVSMjIyYpJQRLVrbGhoSJaXl6WoiA0C2YyII4AAAggggED8BPQfpVVVVfEbeMSILy8vpbKyMqKFEAKfC/wDyharC0/6c2QAAAAASUVORK5CYII=\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure()\\n\",\n    \"ax = Axes3D(fig)\\n\",\n    \"#ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([0.7, 1, 0.7, 1]))\\n\",\n    \"X = vol_df.columns\\n\",\n    \"Y = vol_df.index\\n\",\n    \"X, Y = np.meshgrid(X, Y)\\n\",\n    \"x = X.reshape(np.size(X))\\n\",\n    \"y = Y.reshape(np.size(Y))\\n\",\n    \"z = vol_df.values.reshape(np.size(vol_df))\\n\",\n    \"f = ax.plot_trisurf(x, y, z, linewidth=0.2, cmap='viridis')\\n\",\n    \"plt.xlabel('hour')\\n\",\n    \"plt.ylabel('sec')\\n\",\n    \"plt.colorbar(f)\\n\",\n    \"ax.invert_yaxis()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/MarketImpact.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Market Impact\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import environment\\n\",\n    \"import utility\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from scipy import stats\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.rcParams.update({'figure.max_open_warning': 0})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tmin=9.5*1e9*60*60  # 9:30\\n\",\n    \"tmax=16.5*1e9*60*60 # 16:30\\n\",\n    \"symbols_liquid = ['AAPL', 'INTC', 'MSFT', 'SPY']\\n\",\n    \"symbols_illiquid = ['ABBV','WRB-C', 'BPOPN','GDXJ', 'ALPN', 'EOS', 'VFMF', 'PIN', 'EWGS', 'WNS']\\n\",\n    \"\\n\",\n    \"symbols = symbols_liquid + symbols_illiquid\\n\",\n    \"\\n\",\n    \"dates = ['01302019','03272019','05302018']\\n\",\n    \"LOCATION = '/Volumes/LaCie/'\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def avg_price(q,book_slice):\\n\",\n    \"    \\n\",\n    \"    total_liquidity = np.inf\\n\",\n    \"    \\n\",\n    \"    if q>0:\\n\",\n    \"        if q > book_slice['1_ask_vol']:\\n\",\n    \"            prices = book_slice.iloc[3::4]\\n\",\n    \"            vols = book_slice.iloc[4::4]\\n\",\n    \"            total_liquidity = book_slice.iloc[4::4].sum()\\n\",\n    \"                \\n\",\n    \"            remaining_size = (q-vols.cumsum())[(q-vols.cumsum())>0].iloc[-1]\\n\",\n    \"            remaining_price = prices[((q-vols.cumsum())>0).values].iloc[-1]\\n\",\n    \"\\n\",\n    \"            avg_price = (((vols.multiply(prices.values))[vols.cumsum()<=q]).sum()+remaining_size*remaining_price)/q\\n\",\n    \"\\n\",\n    \"        else:\\n\",\n    \"            avg_price = book_slice['1_ask_price']\\n\",\n    \"            \\n\",\n    \"    elif q<0:\\n\",\n    \"        q = -q\\n\",\n    \"        if q > book_slice['1_bid_vol']:\\n\",\n    \"            vols = book_slice.iloc[2::4]\\n\",\n    \"            prices = book_slice.iloc[1::4]\\n\",\n    \"            total_liquidity = book_slice.iloc[4::4].sum()\\n\",\n    \"            \\n\",\n    \"            remaining_size = (q-vols.cumsum())[(q-vols.cumsum())>0].iloc[-1]\\n\",\n    \"            remaining_price = prices[((q-vols.cumsum())>0).values].iloc[-1]\\n\",\n    \"            \\n\",\n    \"            avg_price = (vols.multiply(prices.values)[vols.cumsum()<=q].sum()+remaining_size*remaining_price)/q       \\n\",\n    \"        else:\\n\",\n    \"            avg_price = book_slice['1_bid_price']\\n\",\n    \"    \\n\",\n    \"    else:\\n\",\n    \"        avg_price = (book_slice['1_bid_price']+book_slice['1_ask_price'])/2\\n\",\n    \" \\n\",\n    \"    if q>total_liquidity:\\n\",\n    \"        return np.nan\\n\",\n    \"    else:\\n\",\n    \"        return avg_price\\n\",\n    \"    \\n\",\n    \"def mkt_impact(ticker, date='01302019', plot=True):\\n\",\n    \"    Q = np.linspace(-100000,100000,200)\\n\",\n    \"    T = np.linspace(0.3,0.7,11)\\n\",\n    \"    P = np.zeros((len(T),len(Q)))\\n\",\n    \"    a = environment.ts(date,'NASDAQ',ticker,PATH=LOCATION,levels=20)\\n\",\n    \"    \\n\",\n    \"    for i,t in enumerate(T):\\n\",\n    \"        book_slice = a.book.iloc[int(len(a.time)*t)]\\n\",\n    \"\\n\",\n    \"        mid = (book_slice['1_bid_price']+book_slice['1_ask_price'])/2\\n\",\n    \"\\n\",\n    \"        for j,q in enumerate(Q):\\n\",\n    \"            P[i,j] = (np.abs(avg_price(q,book_slice)-mid)/mid*1e4)\\n\",\n    \"\\n\",\n    \"    df = pd.DataFrame(P, index = T, columns = Q)\\n\",\n    \"\\n\",\n    \"    try:\\n\",\n    \"        if plot:\\n\",\n    \"            m = df.dropna(axis=1, how='any').mean()\\n\",\n    \"            s = df.dropna(axis=1, how='any').std()/np.sqrt(len(T))\\n\",\n    \"            plt.figure()\\n\",\n    \"            ax = m.plot()\\n\",\n    \"            ax.fill_between(m.index, m - s, m + s,alpha=.25)\\n\",\n    \"            #plt.tight_layout()\\n\",\n    \"            plt.ylabel(\\\"mkt impact bps\\\")\\n\",\n    \"            plt.xlabel(\\\"size (shares)\\\")\\n\",\n    \"            plt.title(a.stock + ' ('+ str(int(mid))+'$) ' + date)\\n\",\n    \"            #sns.despine()\\n\",\n    \"            plt.show()\\n\",\n    \"    except:\\n\",\n    \"        pa\\n\",\n    \"    \\n\",\n    \"    return df\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Results\\n\",\n    \"\\n\",\n    \"For some securites the market impact is defined as \\n\",\n    \"$$I_t(q) = \\\\frac{\\\\overline p_t(q)-M_t}{M_t}$$\\n\",\n    \"where $p_t(q)$ si the average price aid when sending a merket order to the exhange of $q$ shares, and $M_t$ is the mid price at time $t$. \\n\",\n    \"$p_t$ is computed using order book data at time $t$. \\n\",\n    \"For each security is recorder $I_t(q)$ for different times troughtout the day and in the picures is highlighted the mean $\\\\pm \\\\sigma$ \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAQABJREFUeAHs3QecHGX9+PFvkqu59Ep6hxCKCCT0rhRpgogUhSAg0iwg4s9C8acvxAYqIGDAgPgD/iIiEKr0QEIIgqGFJJfkcpd2d8kl1/v+n+/gDDN3t3u7tzuzu7Of5/UKO/Up75kj2e89pV/EJCEhgAACCCCAAAIIIIAAAggggAACCCCAQCgF+oeyVTQKAQQQQAABBBBAAAEEEEAAAQQQQAABBCwBAoC8CAgggAACCCCAAAIIIIAAAggggAACCIRYgABgiB8uTUMAAQQQQAABBBBAAAEEEEAAAQQQQIAAIO8AAggggAACCCCAAAIIIIAAAggggAACIRYgABjih0vTEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIO4AAAggggAACCCCAAAIIIIAAAggggECIBQgAhvjh0jQEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIOIIAAAggggAACCCCAAAIIIIAAAgggEGIBAoAhfrg0DQEEEEAAAQQQQAABBBBAAAEEEEAAAQKAvAMIIIAAAggggAACCCCAAAIIIIAAAgiEWIAAYIgfLk1DAAEEEEAAAQQQQAABBBBAAAEEEECAACDvAAIIIIAAAggggAACCCCAAAIIIIAAAiEWIAAY4odL0xBAAAEEEEAAAQQQQAABBBBAAAEEECAAyDuAAAIIIIAAAggggAACCCCAAAIIIIBAiAUIAIb44dI0BBBAAAEEEEAAAQQQQAABBBBAAAEECADyDiCAAAIIIIAAAggggAACCCCAAAIIIBBiAQKAIX64NA0BBBBAAAEEEEAAAQQQQAABBBBAAAECgLwDCCCAAAIIIIAAAggggAACCCCAAAIIhFiAAGCIHy5NQwABBBBAAAEEEEAAAQQQQAABBBBAgAAg7wACCCCAAAIIIIAAAggggAACCCCAAAIhFiAAGOKHS9MQQAABBBBAAAEEEEAAAQQQQAABBBAgAMg7gAACCCCAAAIIIIAAAggggAACCCCAQIgFCACG+OHSNAQQQAABBBBAAAEEEEAAAQQQQAABBAgA8g4ggAACCCCAAAIIIIAAAggggAACCCAQYgECgCF+uDQNAQQQQAABBBBAAAEEEEAAAQQQQAABAoC8AwgggAACCCCAAAIIIIAAAggggAACCIRYgABgiB8uTUMAAQQQQAABBBBAAAEEEEAAAQQQQIAAIO8AAggggAACCCCAAAIIIIAAAggggAACIRYgABjih0vTEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIO4AAAggggAACCCCAAAIIIIAAAggggECIBQgAhvjh0jQEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIOIIAAAggggAACCCCAAAIIIIAAAgggEGIBAoAhfrg0DQEEEEAAAQQQQAABBBBAAAEEEEAAAQKAvAMIIIAAAggggAACCCCAAAIIIIAAAgiEWIAAYIgfLk1DAAEEEEAAAQQQQAABBBBAAAEEEECAACDvAAIIIIAAAggggAACCCCAAAIIIIAAAiEWIAAY4odL0xBAAAEEEEAAAQQQQAABBBBAAAEEECAAyDuAAAIIIIAAAggggAACCCCAAAIIIIBAiAUIAIb44dI0BBBAAAEEEEAAAQQQQAABBBBAAAEECADyDiCAAAIIIIAAAggggAACCCCAAAIIIBBiAQKAIX64NA0BBBBAAAEEEEAAAQQQQAABBBBAAAECgLwDCCCAAAIIIIAAAggggAACCCCAAAIIhFiAAGCIHy5NQwABBBBAAAEEEEAAAQQQQAABBBBAgAAg7wACCCCAAAIIIIAAAggggAACCCCAAAIhFiAAGOKHS9MQQAABBBBAAAEEEEAAAQQQQAABBBAgAMg7gAACCCCAAAIIIIAAAggggAACCCCAQIgFCACG+OHSNAQQQAABBBBAAAEEEEAAAQQQQAABBAgA8g4ggAACCCCAAAIIIIAAAggggAACCCAQYgECgCF+uDQNAQQQQAABBBBAAAEEEEAAAQQQQAABAoC8AwgggAACCCCAAAIIIIAAAggggAACCIRYgABgiB8uTUMAAQQQQAABBBBAAAEEEEAAAQQQQIAAIO8AAggggAACCCCAAAIIIIAAAggggAACIRYgABjih0vTEEAAAQQQQAABBBBAAAEEEEAAAQQQIADIO4AAAggggAACCCCAAAIIIIAAAggggECIBQgAhvjh0jQEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIOIIAAAggggAACCCCAAAIIIIAAAgggEGIBAoAhfrg0DQEEEEAAAQQQQAABBBBAAAEEEEAAAQKAvAMIIIAAAggggAACCCCAAAIIIIAAAgiEWIAAYIgfLk1DAAEEEEAAAQQQQAABBBBAAAEEEECAACDvAAIIIIAAAggggAACCCCAAAIIIIAAAiEWIAAY4odL0xBAAAEEEEAAAQQQQAABBBBAAAEEECAAyDuAAAIIIIAAAggggAACCCCAAAIIIIBAiAUIAIb44dI0BBBAAAEEEEAAAQQQQAABBBBAAAEECADyDiCAAAIIIIAAAggggAACCCCAAAIIIBBiAQKAIX64NA0BBBBAAAEEEEAAAQQQQAABBBBAAAECgLwDCCCAAAIIIIAAAggggAACCCCAAAIIhFiAAGCIHy5NQwABBBBAAAEEEEAAAQQQQAABBBBAgAAg7wACCCCAAAIIIIAAAggggAACCCCAAAIhFiAAGOKHS9MQQAABBBBAAAEEEEAAAQQQQAABBBAgAMg7gAACCCCAAAIIIIAAAggggAACCCCAQIgFCACG+OHSNAQQQAABBBBAAAEEEEAAAQQQQAABBAgA8g4ggAACCCCAAAIIIIAAAggggAACCCAQYgECgCF+uDQNAQQQQAABBD4R+OY3vyn9+vWTwYMHy9atW0PLsssuu8jxxx+fcPtuvfVWy6d///7y1ltvJXw/NyCAAAIIIIAAAghktgABwMx+PtQOAQQQQCCEAkceeaQVbNGA1A033BC1hXre/efMM8+Mem3XE+4y7rzzTuf0woULPXm68+/r9ssvv+zk39NGY2Oj/OMf/5DLLrtM9ttvP5k0aZIUFRVJSUmJTJw4UbSu3/ve9+TFF1+USCTSUxZJHXv77bflT3/6k5WHljN27Ng+5af1dxvNnj07Zj7Nzc2e6933xrOtQcug0qWXXipTpkyx/K+44gpfnoO7Lc8++6x89atflVmzZlnvwciRI2WfffaRH/zgB7Jq1Sr3pb1uV1ZWylNPPSU33nijnHzyyaJBULfv0qVLe83DfcGaNWvkvvvuk29/+9tyzDHHyIwZM6zAcV5engwZMkRmzpwpX/7yl61r9N1ONLW0tIj+HGqgVs31Z2H8+PFy6KGHyi233CLbt2+PO0u99uGHHxZ9Vw488EAZPXq0FBQUWPVU27PPPts6397eHnee7gvXrVsn1113ney7774yatQoGThwoNV+/X/RE0884b60121t97Jly+T222+X+fPny5577ikDBgxwnlUy7/vy5ctF39u99tpLhg8fbplOnTpVvvCFL8if//xnaWpq6rV+XIAAAggggEDoBcw/tEkIIIAAAgggEKDAEUccoVEu68/1118ftWT7GvvTBDUi7777btTr3SfcZfzxj390Tpkvw07Zdr7Jfr700ktO/u4N84U/8rvf/S5iAjJxlzl58uTIXXfdFTEBC3dWSW0fddRRVvlDhw6N7Ny5s095vfrqqxH1d1vttttuMfMyQQfP9e5749mO9W5EK9gENyPHHXdctNMxj99xxx1OfR966KGY1/b1pAlYRU4//XSnnJ4cCgsLI7/5zW/iKuKzn/1szLw0/yVLlsSVl32RCUr1mqddbxO4izz++OP2rb1+vvfeexET+IqZ/7hx4yLPPfdczLxqamoiJ5xwQiQ/Pz9mXnY9TRAz8sYbb8TMs+tJfR+Ki4tj5q91MAHYrrd22//1r3/da10vueSSbvf1dqCuri5igpwx66gG+rNqgo+9Zcd5BBBAAAEEQi2QZ/5SJCGAAAIIIIBAFgiYf5HIj3/844R73ribtvvuu8vll1/uPtRtW3vrbdq0yTo+d+5cmTdvXrdr3AcmTJjg3rW2q6ur5bTTTpPFixd7zpnghpigjdVTSYebbt68WT744AMpLy+3rtuwYYOYQICsXLlSfvvb33ru7cvOCy+8ICZAad2qvdy0B1eiSXsuXXzxxQn3itMeY71Zu+vyn//8x+N17rnnuk/7vn3BBRdYPVK1R50JPsoZZ5xh9dBKVcHqeMopp3jauPfee1vvg/akM0FWa3i2Xnf11VeL9lr7/ve/H7N4+z2NeVESJ6dNmyYmeGT1KjSBSdmxY4esWLFCPvroIytXLf+LX/yi3H///dLb81q/fr3Vo1B9Nen7bwL1Mn36dNmyZYvou6q9RvVn4qSTTrL2tVdgT0nr8fTTT3tO6c+W9rDVHq6tra3yzjvvyPvvv29dU1paavW01V57xx57rOe+nnZM8M/z7moPzSNNT10dQm+CmKK9ajVpHbSX3SuvvGL1DuwpLz1WVVUlbW1t0U736bj26jv66KM9Q9a1R7GaaT3V+7XXXrNMP/74Y/nc5z5nvWOf+cxn+lQeNyGAAAIIIJD1AqEOb9I4BBBAAAEEMlDA3TsvVi8v84+MHnu2xNOjyV2GuwdgPBzue2PVL1pe5st+RHscuetvhmZGzNxy0W6xejaaIbZOL6G+9AbqKXPzpd+qhwm2RMrKynq6pNdjP/rRj6w8tAegu7dRbz0Ae824ywUm6OOYHXzwwV3Odt/t6OiIPProoxETqIuY4Y4RMzzTuV97bplgSER7P5ohnBEToIloj8ze0rXXXuvk8cgjj/R2eULnr7nmGidvrevf//53z/0m+BX51re+5Vyj3mb4ruearjva41F7wZnAV8QMIY3cc8891nvmfvfi+Xlx5/uNb3zDqpu+x9GSGXIaMcNNnbqagFPEBAOjXW4dN4F053oT9IuY4JznejM3ZeSwww5zrhkzZkxEe7j1lMzQXOs6E5iLXHXVVRHtWdhT0t65Zsi9k+eIESMiJjjf06XOMROIjrh7QX7961+PNDQ0OOd1wwT+IiaY7uRrguue81137PfKDHm23tebb745Yob8R/T/C/azSvRnXsu079X6am/jrj2HN27cGPn85z/vXKc/J/qekRBAAAEEEMhFAf1tNgkBBBBAAAEEAhSIN8Bmf7nVTzO/l/Ml1sxL1mtt3WUEGQDs7Oy0hqDaddcv5gsWLOi1vvYFa9eujRx00EGRRIMB9v3uTw2K2PXo67BYDYbYwywvvPDCiFraeaYyAKjBH9Nj0Mlbh0HHStu2bYuYHlnO9XadYn3q8O/e0urVq508TU+q3i6P+3xFRUXEzHXn5G3mwIt676mnnupcpwHMWEmHxHcN6HQdep1oADBWee5z+syGDRvm1FWHuUZLGuy0n406mB6uPV6qQ9TdATszr2GP12mw8ac//Wmktra2x/Pug/pMzXybTvlm3lH36W7b7qCc+uvPdE/J9BR28tSfETN3Yk+XWcc0YNnTUOGvfOUrTh6J/Myb3n2enxfTWzhq2fp+mJ6mTjmxnlPUTDiBAAIIIIBACARYBMT8a4yEAAIIIIBApgvoRPzmS7ZVTfew1kyrt+mBJbrAg510GK8JnNm7vX7qkEsdTnjWWWf1em1vF9x7773OJSbQ4GzHu2F62MlFF11kDV3UxRV++ctfxntrwtf99a9/tYa86o26KESs+pp/f4qZR0/ci6/oIhJqpgur6CIr5513npjgTcJDnnWBCx2irUmHb5vgkbWd7H/0WZhAjJWNLtRw/vnnR83yV7/6lbUwhF6gw7ft4bY93aDDOXVobjqS6aEnJ554olO0DrmNlnThCzvpcHIdVtxT0iHqOvzaTibg3OPQcx3u+5Of/MQa6mpfG+1Tn6n7Z3DRokXRLrWG4rsX9zDBMudZdL1Jhz7bQ5R1eO/dd9/d9RJnXxfk0J+hVCWdpsBe2ETf/SuvvDJq1vp+mGCpc/73v/+9s80GAggggAACuSRAADCXnjZtRQABBBDIWgENjLm/xOtcgJmWTE8h0eCNnQ455JCYX8zt67p+aqBT5xtLJmldHnzwQSsLnWtN555LNJkhhc78Yro6qxk+mWgWcV+vq87aSQMrZsESe7fb55NPPmkFSfWEGSZrmZvhzVZ7Bw0aJHPmzLFWqNVVlXWV2Oeff1501VadkzCepHM32umBBx6wN5P6fOyxx5z7da7BWElXr9V3x04a7MnU5A5qmeG6PVbTLNhhzT1nn+yt/RrI1SCwJp0b0PRgtG/t86fbU+fGi5bcz0nnZ9TVf2MlXc3XTkE+pzfffNMu1prTsLd3W+c9tK/ReUbNdATO/WwggAACCCCQKwIEAHPlSdNOBBBAAIGsF9Cgnx0YMCt6ylNPPZVRbdKeiatWrXLq9N3vfjdq7yHnIp82zBxtVvBEs9dAhi5ikEgyQxatHlZ6j5lDrNcFHhLJu+u1uqiELgBip1i94/Saf/7zn/alYuark+9973tOcMM58d8NMwTbWvzg4YcfjrsNurCCnTTYmGwyw1Tl3//+t5NNPMFd7b1oJw1kZmr68MMPnappL7eeki5EYfdWM0OGZZ999unpMueY9uLUxXfslIr2a6DYTtqzNVqyF8zR84k+J+0taoZ6R8s6pcfN8GsnPzOvoLMdbcPMiSnaY9NO+v8qEgIIIIAAArkmQAAw15447UUAAQQQyFoBXW3XLJTh1F8DgjocNFOSO1ChQQwzl1vaqqa93uxkFlawN+P+1MCarkyrAVcdhulncvf+0+GMGnCMlTQ4aSf3EFT7WLRPdxAo2jV6XFd9tofV6rBWM99grMt7Pecewqu9sHQIcG/J3fPMfX9v9wV5XlfAdb9numpyT8ldfw3+xfMcUt1+XbnXTjpEPFpy19Vdh2jX6wrG7t6q7vuj3ZOK48n+f09XHichgAACCCCQawIEAHPtidNeBBBAAIGsFvjBD34gOsxTkwZnzOICGdMe7elkJw0e2EPu7GNBfrqHCGoPwESSWaBC/vWvf1m36DxrZkXjRG5P6FrtGabz/9npq1/9qmivvXiT3bMs3uvjuU6HYOswYk0aaEl2uOTHH3/sFKtB7Hjei8mTJzv3mAUvJNrwWueiADbUYseOHaK9b3XOOQ1w24EoHZ4fLdDsbn88vdW0Ke72mwVDkmqdviPuodxmZewe89OegWYhD+dcOurqFN7LhnvotQ7p7S2ZhWHErOrsXBZUoNIpkA0EEEAAAQQyQIAAYAY8BKqAAAIIIIBAvAL6xffb3/62c7kuGKDz3WVCcs8ttscee6S1Sjqs1k6zZ8+2N3v91KGFV199tXWdtuGaa67p9Z5kLtAFU9zDGXsb/qtl6Rx5dnIHD+1jqfjcfffdnWzcw5OdgwlsuHsQjh07Nq47d9llF891OpdhOpL2stUee/pH55IcPny4NT/hbbfdZi0Oo8FSDRL/6U9/ilq9ZNufbNtvvfVWsXuNavDVrLbbY111qLY7oNyXZ5VsXXusWA8H99tvP+eo9sKMNaxZL9SAvi5UYqeg6mmXxycCCCCAAAKZIEAAMBOeAnVAAAEEEEAgAQGd803nEtOkc5D5FQRKoErWpe4v1Xb9Es0jFddrEMM9F9nEiRPjzlZ7dmk7NOCjq5raKy/HnUGCF95///3OHTrvm93zzjnYw4Z7hWDtAaorFcfTC6qHrKIe0p56dnIHdu1jiXzW19c7l+tcbPGkrte584jn/iCu0Z6lb7/9trXCbKxhve66d21XtHq6r3PfH+36aMe1l7CuIG6nSy+91BNAto/rZ9dy3HVwX9d1231d1zy6XpuqfV3UxzbXn/VYw/Q18HfDDTd4is6EHqWeCrGDAAIIIIBAAAIEAANApggEEEAAAQRSKaDBNQ0C2unGG2/09Nyxjwf5qT1wdJidnexhyvZ+kJ/V1dWeHkHxLgDy+OOPy9/+9jerqjoH4MEHH+xrtXU4qZZpp3h6/+m1ukCGe/XVe+65R6aaBSj23HNP0RVnN27caK0629DQYGed8OeoUaOce3Ql2mRSc3Ozc3tBQYGzHWvDnoPQvsb9btnHgvg88MAD5fLLL7f+fPOb37RWU951112torWXqfZE03k5YwW+km1/X9uuPwenn36683OpweWbb745Kpu7nnpRX55VX+satVJRTmgPVW2bna666iorCNi1N7S+u7qqtnsRGr0nqHra9eMTAQQQQACBTBAgAJgJT4E6IIAAAgggkKCADgO258EqLS2Ve++9N8EcUnu5zluXjp5APbWia+Br4MCBPV3mOabDH+0FVnT46S9+8QvPeT92dGVeO+iiwZazzz477mIWLFhgDT21A2U6F50ubNDa2irvv/++HHHEEdbKx7pISF9Wi3abdfWMu5L/vdBeuVp3tX7xpJaWFs9l7nfLc8LnnZNOOkl0uK/+0V5m+sx0Tr9ly5ZZwT/tXabHjznmGInmlGz7+9J2XcBGe8nZvTd16PIjjzzi+RntSueup57ry7PqS1271iPe/TvvvNMKfOv1+hz053fatGlyzjnnWKtjH3/88da+vv869Pnkk092sh48eLCzzQYCCCCAAAK5IkAAMFeeNO1EAAEEEAiVgPaw+5//+R+nTf/7v/8rXYMmzsmANkaMGOGUpL3bMiXZCzXEqs/3v/99q+ecXqNzpgUxhNk9/FeDE26/WHXVcxpw/elPf2rN7XbTTTfJQQcd1K3Hlr4PGvzQIOBxxx2X0Gq+8Zj1Vkf7vLs3aLw9r7pe587Dzjednzpc+5VXXpHPfvazVjU0IPjDH/6wxyq56961XT3eYA66r3PfH+1693EN3H3pS1+SJUuWWIc1mLto0SJxz+vovt7e7lqOuw72NT19uq/rmkdP16fqmPZSff311z09dXUo/IMPPmjNyajza2qAXVcp1mPam9NOQfx822XxiQACCCCAQKYIEADMlCdBPRBAAAEEEEhQQOfzsudq620erASz7tPlOgzVTjo3YbpSSUmJp2h3gMJz4r87GijR+f40aa8h9xx7/70k5R+rV6+2VpO1M453+K99vf05btw40ZWhdWVanddMe4XqqsXaBg182Om5556zAoG9LZZgX+826+ppXxPvp3sItnvBk1j3dx12nEhwNFa+qTynLr/85S+dLPUd6mkocLLtT6Tt+nzPPfdceeaZZ6x6ac/Sf/zjH1aA2KlolI0hQ4Z4Vmjuy7NKpK5RqpHQ4fHjx1tBQB1Krz1otQeg9kLUQKQOidegrPaMPeOMM0SHRNtp0qRJ9iafCCCAAAII5IxAXs60lIYigAACCCAQMgEdsqerlGogUJMOW7344osl2YBNX5kOPfRQ68u43q+LI+hiHDr0LuikPYO0h5wd7NIv/rGG/Okqt3aPNx1O7e4p1LXulZWVzqGysjLPtT/72c/kc5/7nHM+1oa799+YMWPkhBNOiHV5XOc02KMr1c6cOVMeeugh0WGg+k5o71BNb775pvzlL3+R+fPnW/ux/lNVVeWc7roir3Mizo3ddtvNuVLnJ9Tnos8nVnIvaqJBnljPL1Y+fp878sgjrXdc33XtbabvvQ6/did3+/WdiSe52x/vKtb6Dn/961+3hvpqGWqsPd+OPfbYeIq0rtd3Z+XKldb1ftY1rgolcJH2oHUP8e3pVg0E2kl7cJIQQAABBBDINQF6AObaE6e9CCCAAAKhErjwwgutXi/aKO2x8/vf/z5t7Tv66KOdsnU+NPcCF86JADY06Oju4eNeEbi34rVnngbKov1Zt26dk4UGfNzXuXsYORf1sKGBGg3E2Ul7bPkRKNWhnzpMWBc0sdM///lPezPmpwbq7OTu2WkfS+TTPfRUA2Xvvfder7e7F21w39/rjQFfoM/NHZzctm1btxq46+8ONne70HWgL+3XxUrswLKukPvnP//Zs1CGK/uom+666grCvaW1a9fKzp07ncvc9zsHM2BD5whcvny5UxO/F/hxCmIDAQQQQACBDBIgAJhBD4OqIIAAAgggkKhAfn6+XH/99c5tv/rVrzxfyJ0TAWxo77dZs2Y5Jd1yyy1OzzrnYEAbe++9t1OSLtqQSUnnjnP3rurr8N942+Qe0mwvCtHbvR999JFzyWc+8xlnuy8bOrR03333dW59+eWXne1oG2pkJ3dg2T6WKZ86VNo932VPQ2APO+wwJ8CrqzRrEDBW0p6bb731lnNJPO2/+uqrrcVI7Jtuv/12+drXvmbvxv2pK0zbKdHnpD/7EydOtG/PqM/nn39etm/fbtVp7NixcfeKzKhGUBkEEEAAAQSSFCAAmCQgtyOAAAIIIJBuga9+9atiDxPUAMNvfvObtFRJh59ec801TtmLFy+WO+64w9mPd0N768QTfIiV37x585zTvQVcvvnNb1qBSu2Z19sfXfHVTjq00339WWedZZ+K+Xnfffc55zW4lmyAzcksyoYG4Oykw4R7S+pvz+GoPclSMVzyi1/8olPswoULne2eNnQY9muvveacct/rHMyQDV1kRd8BTWrlHu5rV1FX4D388MPtXemt/e7VoTVYFWtIumb6k5/8RH772986+eu8hPa0AM7BODdOPfVU50r9uXn33Xed/Z423G3J1Oekz+eGG25wqq+9pvUXJyQEEEAAAQRyTYAAYK49cdqLAAIIIBA6AXtFWLthuoptvMNR7XtS9alfrj//+c872X3nO9/pNeDhXGw2dIitzqumc9glk9x10EBkpiQdGv3II4841YlnPj7n4v9uuHuHdT3X0/4TTzzhHN51112d7WgbuqKtriSrSVe5dS9iEe2e3o7r3HQ6Z6UmDSw98MADUW/RFZntoJq+C3PmzIl6bapP9DSEN1oZOk/itdde65zWYaW6KEtP6bLLLnMO62IhOtS8p6QLudx4443OKQ3kaWA9WtJgn849aafrrrvOE4S3j8f7OXnyZDnppJOcy90Bfefgfzf0vXr11VetPR0K7R5q3vXadO7raun2z4z2UNRFc0gIIIAAAgjkokD0f1HkogZtRgABBBBAIEsFdJXLffbZx6q9BhHcE94H2SQNVvz1r3915iXUOd8uuOACOe2008Q9r1nXOq1YsUKuuOIKqweVrmibbNp///3FXrxC804ksJNs2bHuf/TRR52VYjVocs4558S6vMdzV155peiCKzrHor3QSY8XmoM61+BNN93knP7yl7/sbEfbePHFF51T7mCQc7APG7patdbbTtrrUlendaeWlhb57ne/K2qkSXvU6SImQabzzjtPtCenDhmNZqvH1f6ggw4S7a2oqbe6fulLXxK7V6oOG/7CF77g9LK026cLzJxyyinO8HBdHOaqq66yT3f71N617gCkXusOHna7Ic4DP//5z51FWv71r3/JRRddJO5VoTUbXVVareykiw/pAiJBJp3T73vf+564h6u7y9d5LHV+zZtvvtk6rL8oWbBggWfORvf1bCOAAAIIIBB2gbywN5D2IYAAAgggkAsCGoDQ1V57WwkzCIvRo0fL0qVLraCfHcx77LHHRP/oiq46H5yu1Kt13rJli7z//vtSXl7uqZp7YQXPiTh3NBB59tlni85D2NnZaQVsNBCZ7mQv0qD10JV/NcjTl/T666+LDtfUIaK66qyaqrsuTKKW2ovuySef9ARHtFekBph6S+7AnAZQUpX0/VyyZIloj0ztCXn66adbw5+1l6HOe6fz/ulCNnbS4N8BBxxg7/b4qb0p3T3g9CK796B9gwaqdEEUd9JA6I9+9CP3IWtbg3s6BFf/6GraOjx7ypQposOotVfk5s2brcUk3D1s9T2+7bbbrKBstwxdB7RXqw7n1UDfmjVrZK+99pIjTQ/HadOmWe1+4YUXnECbDtXWtkX7OdDglwbM7aT10wCq+5h9rqdPDZxNnTq1p1Oi82f+7ne/c/K65557RBeP0bpqfXQRF/eCGvvtt5/8+te/7jEv+6Da2QFQ+5h+uufC1Pbq/zfcqbCw0Fpox33M3q6vr7emO9ApD7Qt+gsQ/XnSd0sDs9qTVX/2NWnwT3udHnfccfbtfCKAAAIIIJB7AuYfSSQEEEAAAQQQCFDABGx00jDrj1nAI2rJ9jX6aXq5RL3OfcIEGJy87fvNvHXuS3rdjrd+vWVkAhIRMzdZxHwp71Ynu25dP81CAhEzR17EfHHvLftez5tAhVOu+eLf6/XxXKCWdp3NfG/x3OJcYwJzEROYdO7/+9//7pxLZMP08ooMHTrUyceuT6xP0+MsYlZr7bUYMzTVyfeQQw7p9fpELzALMURMb1CnjJ7qbIJfEbOYTVxZu59HT3lFO3bJJZf0mL8JkMasW9f89H01veF6zKung/pO7rnnnjHLMD1XI88++2xPtzvHnn766Zh5dK1n130TiHXyirZhFhKJFBcXxyzn+OOPj5igbbQsnOOmB2HMfLrWz943AUAnj64bL730Ulx5mvlRIya43PV29hFAAAEEEMg5AXoAmn9hkBBAAAEEEAiLgA7fO+aYYzKiOdqLSYd0mmCLmICFNazyzTfftHpA6ZBc7ZWjCyTovHTa00uHm5qgU8rqbgItoisT6zBGHdKpvY20N1e6kg7HtXsk6WqxfR1ea8/zpu3SoZjvvPOO1aNMV6PVXmCatPeazkenrrpIjAnUxNVs7e1lJ52/MdVJn7cO8X3mmWdEe0Pq+6C96rSn16RJk6x66pDTeOYqTHXdND+tm9bpZbNSsc4bt2rVKtGhpNrbTN9nE3i1euxpj0vtgak/a7Hm6OtaR30ntfecDpPXXoa62Ir2CFSXGTNmWL0itadqT6sJd83L732dt1B7qeo7sWjRItmwYYPVU1PfK+31p6sMx9Oj1K96am9KrZf2nNSexps2bRKdl1EX+NDh//qMdOi11lGfHQkBBBBAAIFcF+inIc9cR6D9CCCAAAIIIBBOAZ3Pzg6I6nxpQc8pF7SqBj50KKQG2BJNOnxYh1LqMFwNwGlwSoO0JAQQQAABBBBAAIHsF2ARkOx/hrQAAQQQQAABBKIIHH300XLUUUdZZ81wUTHDYKNcyeGFCxc6c/DpYhIE/3gnEEAAAQQQQACB8AgQAAzPs6QlCCCAAAIIINCDgJlPzhqmWVtbK7feemsPV3BIF2mwV0vVxRq+8pWvgIIAAggggAACCCAQIgECgCF6mDQFAQQQQAABBLoL6HxlF198sXVCVyvVOddIXgHtHbl+/XprZWZd0VZXtiUhgAACCCCAAAIIhEeAOQDD8yxpCQIIIIAAAgjkuEAycwDmOB3NRwABBBBAAAEEQi1AADDUj5fGIYAAAggggAACCCCAAAIIIIAAAgjkugBDgHP9DaD9CCCAAAIIIIAAAggggAACCCCAAAKhFiAAGOrHS+MQQAABBBBAAAEEEEAAAQQQQAABBHJdgABgrr8BtB8BBBBAAAEEEEAAAQQQQAABBBBAINQCBABD/XhpHAIIIIAAAggggAACCCCAAAIIIIBArgsQAMz1N4D2I4AAAggggAACCCCAAAIIIIAAAgiEWoAAYKgfL41DAAEEEEAAAQQQQAABBBBAAAEEEMh1AQKAuf4G0H4EEEAAAQQQQAABBBBAAAEEEEAAgVALEAAM9eOlcQgggAACCCCAAAIIIIAAAggggAACuS6Ql+sAtD89As3NzfLee+9ZhY8ePVry8ngV0/MkKBUBBBBAAAEEEEAAAQQQQCDMAu3t7VJVVWU1ca+99pKioqIwN5e2RREg6hIFhsP+Cmjwb968ef4WQu4IIIAAAggggAACCCCAAAIIIOAILFu2TObOnevss5E7AgwBzp1nTUsRQAABBBBAAAEEEEAAAQQQQAABBHJQgB6AOfjQM6HJOuzXTvobiHHjxtm7fCKAAAIIIIAAAggggAACCCCAQIoENm/e7IzAc38XT1H2ZJMlAgQAs+RBha2a7jn/NPg3ceLEsDWR9iCAAAIIIIAAAggggAACCCCQUQLu7+IZVTEq47sAQ4B9J6YABBBAAAEEEEAAAQQQQAABBBBAAAEE0idAADB99pSMAAIIIIAAAggggAACCCCAAAIIIICA7wIEAH0npgAEEEAAAQQQQAABBBBAAAEEEEAAAQTSJ0AAMH32lIwAAggggAACCCCAAAIIIIAAAggggIDvAgQAfSemAAQQQAABBBBAAAEEEEAAAQQQQAABBNInQAAwffaUjAACCCCAAAIIIIAAAggggAACCCCAgO8CBAB9J6YABBBAAAEEEEAAAQQQQAABBBBAAAEE0idAADB99pSMAAIIIIAAAggggAACCCCAAAIIIICA7wIEAH0npgAEEEAAAQQQQAABBBBAAAEEEEAAAQTSJ0AAMH32lIwAAggggAACCCCAAAIIIIAAAggggIDvAgQAfSemAAQQQAABBBBAAAEEEEAAAQQQQAABBNInQAAwffaUjAACCCCAAAIIIIAAAggggAACCCCAgO8CBAB9J6YABBBAAAEEEEAAAQQQQAABBBBAAAEE0idAADB99pSMAAIIIIAAAggggAACCCCAAAIIIICA7wIEAH0npgAEEEAAAQQQQAABBBBAAAEEEEAAAQTSJ0AAMH32lIwAAggggAACCCCAAAIIIIAAAggggIDvAgQAfSemAAQQQAABBBBAAAEEEEAAAQQQQAABBNInQAAwffaUjAACCCCAAAIIIIAAAggggAACCCCAgO8CBAB9J6YABBBAAAEEEEAAAQQQQAABBBBAAAEE0idAADB99pSMAAIIIIAAAggggAACCCCAAAIIIICA7wIEAH0npgAEEEAAAQQQQAABBBBAAAEEEEAAAQTSJ0AAMH32lIwAAggggAACCCCAAAIIIIAAAggggIDvAgQAfSemAAQQQAABBBBAAAEEEEAAAQQQQAABBNInQAAwffaUjAACCCCAAAIIIIAAAggggAACCCCAgO8CBAB9J6YABBBAAAEEEEAAAQQQQAABBBBAAAEE0idAADB99pSMAAIIIIAAAggggAACCCCAAAIIIICA7wIEAH0npgAEEEAAAQQQQAABBBBAAAEEEEAAAQTSJ0AAMH32lIwAAggggAACCCCAAAIIIIAAAggggIDvAgQAfSemAAQQQAABBBBAAAEEEEAAAQQQQAABBNInQAAwffaUjAACCCCAAAIIIIAAAggggAACCCCAgO8CBAB9J6aAsAlEIpGwNYn2IIAAAggggAACCCCAAAIIIIBAiAUIAIb44dI0fwSq6lv8yZhcEUAAAQQQQAABBBBAAAEEEEAAAR8ECAD6gEqW4RbYtKNZ2jo6w91IWocAAggggAACCCCAAAIIIIAAAqERIAAYmkdJQ4IS6OjslI01TUEVRzkIIIAAAggggAACCCCAAAIIIIBAUgIEAJPi4+ZcFdha2yzNbR252nzajQACCCCAAAIIIIAAAggggAACWSRAADCLHhZVzRyBTrMOSEVNY+ZUiJoggAACCCCAAAIIIIAAAggggAACUQQIAEaB4TACvQlU1bVKfUt7b5dxHgEEEEAAAQQQQAABBBBAAAEEEEirAAHAtPJTeLYLbNhGL8Bsf4bUHwEEEEAAAQQQQAABBBBAAIGwCxAADPsTpn2+CuxsapMdja2+lkHmCCCAAAIIIIAAAggggAACCCCAQDICBACT0eNeBIzAhu2NEomYSQFJCCCAAAIIIIAAAggggAACCCCAQAYKEADMwIdClbJLoKGlQ6rqW7Kr0tQWAQQQQAABBBBAAAEEEEAAAQRyRoAAYM48ahrqp0BFTZN06tLAJAQQQAABBBBAAAEEEEAAAQQQQCDDBAgAZtgDoTrZKdDS1ilbapuzs/LUGgEEEEAAAQQQQAABBBBAAAEEQi1AADDUj5fGBSmwcUeTtHV0BlkkZSGAAAIIIIAAAggggAACCCCAAAK9ChAA7JWICxCIT6C9IyKbTBCQhAACCCCAAAIIIIAAAggggAACCGSSAAHATHoa1CXrBbbsbJbmto6sbwcNQAABBBBAAAEEEEAAAQQQQACB8AgQAAzPs6QlGSCg64BU1DRmQE2oAgIIIIAAAggggAACCCCAAAIIIPCJAAFA3gQEUixQVdcqDS3tKc6V7BBAAAEEEEAAAQQQQAABBBBAAIG+CRAA7JsbdyEQU6BsG70AYwJxEgEEEEAAAQQQQAABBBBAAAEEAhMgABgYNQXlksDOpjbZ2diWS02mrQgggAACCCCAAAIIIIAAAgggkKECBAAz9MFQrewXKNvekP2NoAUIIIAAAggggAACCCCAAAIIIJD1AgQAs/4R0oBMFWho6ZCqupZMrR71QgABBBBAAAEEEEAAAQQQQACBHBEgAJgjD5pmpkeg3KwI3KlLA5MQQAABBBBAAAEEEEAAAQQQQACBNAkQAEwTfF+LrayslCeffFKuu+46OeGEE2TUqFHSr18/68/8+fPjyraxsVEeffRRufTSS2Xu3LkyfPhwyc/Pl5EjR8pBBx0kN9xwg2zZsiWuvLgotkBLW6ds2tkU+yLOIoAAAggggAACCCCAAAIIIIAAAj4K5PmYN1n7IDB27Nikcl2xYoUccsghUl9f3y2f7du3y9KlS60/t9xyi9x9993yla98pdt1HEhMYGNNk4waVChF+QMSu5GrEUAAAQQQQAABBBBAAAEEEEAAgRQI0AMwBYjpymLy5Mly7LHHJlR8bW2tE/zTQOBNN90kzz//vPz73/+WZ599Vi655BLp37+/6HXnnnuuPP300wnlz8XdBXQE8PptLAjSXYYjCCCAAAIIIIAAAggggAACCCAQhAA9AINQTmEZOvRXh+3qH+0NuH79epk2bVrcJWhw78wzz5Trr79e5syZ0+0+DSjq0OLTTjtNOjo65Morr5TVq1dbQ4y7XcyBuAVqGtpke0OrjCgpiPseLkQAAQQQQAABBBBAAAEEEEAAAQRSIUAAMBWKAeZx4403JlXawQcfLPonVjr11FPl9NNPl7///e9SWloq77zzjuy7776xbuFcHALaC3Bocb4M6N8vjqu5BAEEEEAAAQQQQAABBBBAAAEEEEiNAEOAU+MYulyOOuoop00aBCQlL6ALguh8gCQEEEAAAQQQQAABBBBAAAEEEEAgSAECgEFqZ1FZLS0tTm0HDGDxCgcjyQ1dEbiptSPJXLgdAQQQQAABBBBAAAEEEEAAAQQQiF+AAGD8Vjl15SuvvOK0d/fdd3e22UhOIGIWBFlXzYIgySlyNwIIIIAAAggggAACCCCAAAIIJCLAHICJaOXItf/5z39k0aJFVmv32msv6UsAsKKiIqbW5s2bY54P88mdTW1SXd8iowYVhrmZtA0BBBBAAAEEEEAAAQQQQAABBDJEgABghjyITKmGDv296KKLrBWAtU4///nP+1S1SZMm9em+XLmpzCwIMswsCJI3gE64ufLMaScCCCCAAAIIIIAAAggggAAC6RIg+pAu+Qwt94orrpDly5dbtTv//PPl5JNPztCaZne1WtsjUsGCINn9EKk9AggggAACCCCAAAIIIIAAAlkiQA/ALHlQQVTzpptukgULFlhFzZ07V26//fY+F1teXh7zXh0CPG/evJjXhP3kltpmGT24UEoK+TEM+7OmfQgggAACCCCAAAIIIIAAAgikU4DIQzr1M6jsu+66S374wx9aNZo9e7Y89dRTUlJS0ucaTpw4sc/35sqN9oIge04YmitNpp0IIIAAAggggAACCCCAAAIIIJAGAYYApwE904p88MEH5bLLLrOqNWXKFHn++edl1KhRmVbNUNanrrldKk1PQBICCCCAAAIIIIAAAggggAACCCDglwABQL9ksyTfxx9/XM477zzp7OyUcePGyQsvvCD03gv24W3Y3ihtHZ3BFkppCCCAAAIIIIAAAggggAACCCCQMwIEAHPmUXdvqAb7zjzzTGlvb5eRI0daPf9mzJjR/UKO+CrQ1hERDQKSEEAAAQQQQAABBBBAAAEEEEAAAT8ECAD6oZoFeb7xxhty6qmnSktLiwwdOlSeffZZ2WOPPbKg5uGsYmVti9Q1t4WzcbQKAQQQQAABBBBAAAEEEEAAAQTSKkAAMK386Sn83XfflRNPPFEaGhqshT4WLVok++23X3oqQ6mOwPrqRonoyiAkBBBAAAEEEEAAAQQQQAABBBBAIIUCrAKcQswgslq8eLGsWbPGKaq6utrZ1uMLFy509nVj/vz5nv3S0lI57rjjZMeOHdbxn/3sZ1YPwPfff99znXtnzJgxon9I/grUt7TLVtMTcJehRf4WRO4IIIAAAggggAACCCCAAAIIIJBTAgQAs+xxL1iwQO67774ea/3666+L/nGnrgHA1157TSorK51Lvvvd7zrb0Tauv/56ueGGG6Kd5ngKBcprGmVESYEU5NE5N4WsZIUAAggggAACCCCAAAIIIIBATgsQZcjpx0/jM02g3SwIUratIdOqRX0QQAABBBBAAAEEEEAAAQQQQCCLBfqZOceYdCyLH2C2Vr2iokImTZpkVb+8vFwmTpyYNU15u2y7tLb7+2Oz+7jBMmxgQdaYUFEEEEAAAQQQQAABBBBAAIHMFMjm79+ZKZqdtaIHYHY+N2odcoG11Q3S0elvkDHkhDQPAQQQQAABBBBAAAEEEEAAAQT+K0AAkFcBgQwUaGnrlAozHyAJAQQQQAABBBBAAAEEEEAAAQQQSFaAAGCygtyPgE8Cm3c2S4NZGZiEAAIIIIAAAggggAACCCCAAAIIJCNAADAZPe7NSYG2js5A2q2zc66tahCm6QyEm0IQQAABBBBAAAEEEEAAAQQQCK0AAcDQPloalmoBDcQ99s5Gufyv78iayrpUZ99jfvWmB+CW2uYez3EQAQQQQAABBBBAAAEEEEAAAQQQiEeAAGA8SlyT8wLvVeyUM+5cIt95+F3Z1tAqC99YL50BLaBdvr1JWto7cv4ZAIAAAggggAACCCCAAAIIIIAAAn0TIADYNzfuyjGBpWu3ydtlNU6rS83Q3NdWVzn7fm7oasDrq1kQxE9j8kYAAQQQQAABBBBAAAEEEEAgzAIEAMP8dGlbygTOP3iqTB9d4snvwWXl0tgazCId202vQ/1DQgABBBBAAAEEEEAAAQQQQAABBBIVIACYqBjX56RAQV5/+clJczxt39nUJv8wcwIGldZVN0h7QAuQBNUmykEAAQQQQAABBBBAAAEEEEAAAf8FCAD6b0wJIRE4arcxcvTsMZ7WPP3+Ftm8o8lzzK+d1vZOKa8Jpiy/2kC+CCCAAAIIIIAAAggggAACCCAQvAABwODNKTGLBbQXYF7/fk4LdH6+vywtc/b93thqVgSua27zuxjyRwABBBBAAAEEEEAAAQQQQACBEAkQAAzRw6Qp/gtMG1UiJ+49zlPQO+U75N3yTxcI8ZxM8Y4uPKxDgSMBrUCc4uqTHQIIIIAAAggggAACCCCAAAIIpEGAAGAa0CkyuwXO2G+CDC3O9zTi/iVlgc3P19DSIZt2NnvKZwcBBBBAAAEEEEAAAQQQQAABBBCIJkAAMJoMxxGIIjCwIE/OnjfJc3azCcg988EWzzE/dzaauQCb2zr8LIK8EUAAAQQQQAABBBBAAAEEEEAgJAIEAEPyIGlGsAKHzRotM0aXeAp99N8bZUdjq+eYXzs696AOBSYhgAACCCCAAAIIIIAAAggggAACvQkQAOxNiPMI9CDQv18/mX/wVM+ZJtMj7+G3yj3H/NzZ0dgm1fUtfhZB3ggggAACCCCAAAIIIIAAAgggEAIBAoAheIg0IT0CM8cMlsNmjfIU/sqqKimtqvcc83OnbFuDtHV0+lkEeSOAAAIIIIAAAggggAACCCCAQJYLEADM8gdI9dMrcPa8yVKU/+mPkVmkV+57Y31gq/S2tkdEg4AkBBBAAAEEEEAAAQQQQAABBBBAIJrAp5GLaFdwHAEEogoMH1ggp+0zwXN+dWW9LF5T7Tnm505VXavUNAQz96Cf7SBvBBBAAAEEEEAAAQQQQAABBBDwR4AAoD+u5JpDAifsNU7GDin0tPjBZRsCXaV3bXW9tDMU2PMM2EEAAQQQQAABBBBAAAEEEEAAgU8ECADyJiCQpED+gP7y1QOneHKpMQt0PPbuRs8xP3d0KPD6bY1+FkHeCCCAAAIIIIAAAggggAACCCCQpQIEALP0wVHtzBLYb/Jw2XvCUE+lFq3YLFtrmz3H/NypqmthKLCfwOSNAAIIIIAAAggggAACCCCAQJYKEADM0gdHtTNLoF+/fnLeQVNlgPm0U3tnRB5YWmbvBvK5trqBocCBSFMIAggggAACCCCAAAIIIIAAAtkjQAAwe54VNc1wgQnDi+XYPcZ6arm8rEZWVOzwHPNzp7W9k6HAfgKTNwIIIIAAAggggAACCCCAAAJZKEAAMAsfGlXOXIEv7TtRhhTleSp4/5Iyae/s9Bzzc4ehwH7qkjcCCCCAAAIIIIAAAggggAAC2SdAADD7nhk1zmCBksI8OXPuJE8NN+5okuc/3Oo55vcOQ4H9FiZ/BBBAAAEEEEAAAQQQQAABBLJHgABg9jwrapolAkftOkamjhzoqe0jb1fI9oZWzzE/dxgK7KcueSOAAAIIIIAAAggggAACCCCQXQIEALPreVHbLBDo37+fnH/wVE9NG1s75N7X10kkEvEc93OHocB+6pI3AggggAACCCCAAAIIIIAAAtkjQAAwe54VNc0igdm7DJFDZ47y1PhtsyDIG6XbPMf83mEosN/C5I8AAggggAACCCCAAAIIIIBA5gsQAMz8Z0QNs1TgawdNkSHF+Z7aL3xjvexoZCiwB4UdBBBAAAEEEEAAAQQQQAABBBDwVYAAoK+8ZJ7LAkOK8uXrh0z1ENS3tMufX18f+FDgIIOOngazgwACCCCAAAIIIIAAAggggAACaRcgAJj2R0AFwixwwLSRcsC0EZ4mLlu/XZau3e455vdOaVWDtHd0+l0M+SOAAAIIIIAAAggggAACCCCAQAYKEADMwIdClcIlcMEh02RwUZ6nUX9+Y53UNrV5jvm5o6sCl21v9LMI8kYAAQQQQAABBBBAAAEEEEAAgQwVIACYoQ+GaoVHYKiZB/CCLqsC1zWbocAmCBhkqqxtCXT+wSDbRlkIIIAAAggggAACCCCAQFACOwPszBFUmygn/AIEAMP/jGlhBggcOH2kzJ063FMTHQa8bB1DgT0o7CCAAAIIIIAAAggggAACGSzQ3NYha6vqM7iGVA2BngUIAPbswlEEUirQr18/syDINBlU6B0KfM/r66SuOdihwOU1TSltG5khgAACCCCAAAIIIIAAArkg0NkZkVVb66QzEsmF5tLGkAkQAAzZA6U5mSswbGCBnN9lKLDOA3jfkrJAK721tll0NWISAggggAACCCCAAAIIIIBA/ALrtjVIQ0tH/DdwJQIZJEAAMIMeBlUJv8AhM0bKvpO9Q4FfX1Mty8uCGwqsv6xaZ1YFjvBbq/C/cLQQAQQQQAABBBBAAAEEUiJQWdcsOq86CYFsFSAAmK1PjnpnpYAOBb7w0GlSUjDAU/97Fq8LtFee9gDcYnoCkhBAAAEEEEAAAQQQQAABBGILNLa2y/rqxtgXcRaBDBcgAJjhD4jqhU9gREmBfO2gqZ6G7Whsk78sWe855vdO+fYmaWmn+7rfzuSPAAIIIIAAAggggAAC2SvQYc37Vy/6SUIgmwUIAGbz06PuWStw+KxRss+kYZ76v7q6Wt7ZUOM55ueO/gVWto3fYvlpTN4IIIAAAggggAACCCCQ3QLrquulqZWOE9n9FKm9ChAA5D1AIA0COhT4IjMUuDjfOxR4gRkKrN3Lg0rb6lulpqE1qOIoBwEEEEAAAQQQQAABBBDIGoFKM21SVR3fl7LmgVHRmAIEAIO9qX4AAEAASURBVGPycBIB/wRGDiqUrx04xVPAdhOMe2BpsKsC60pWupw9CQEEEEAAAQQQQAABBBBA4BOBBjNv+rrqBjgQCI0AAcDQPEoako0CR+42WvaeMNRT9Zc+rpIVFTs8x/zcaWnrlIqaJj+LIG8EEEAAAQQQQAABBBBAIGsE2js6ZdXWOqGfRNY8MioahwABwDiQuAQBvwR0KPDFh0+Xonzvj+Ldr64NdCjwpp1NgZbnlyf5IoAAAggggAACCCCAAALJCqw1Pf+aTUcJEgJhEvBGHcLUMtqCQJYIjDJDgc+Z5x0KvM0MBb5/SXBDgSNmBPDaKrq3Z8krQzURQAABBBBAAAEEEEDAJ4EtO5tF50onIRA2AQKAYXuitCcrBY7ZfYzsMX6Ip+6vrKqS5eu3e475uVPX3C6Vdc1+FkHeCCCAAAIIIIAAAggggEDGCtSbef/KzBzpJATCKEAAMIxPlTZlnUB/MxT4ksNndFsV+E9mVeDaprbA2rNhW6O0mfkuSAgggAACCCCAAAIIIIBALgkw718uPe3cbCsBwNx87rQ6AwVGDy6U8w/2DgXW4N+CxWslomN0A0htHRHZsL0xgJIoAgEEEEAAAQQQQAABBBDIHIE1VfWiCySSEAirAAHAsD5Z2pWVAofPGi37TRnuqftb62tk8ZpqzzE/dyprW6S2Obheh362hbwRQAABBBBAAAEEEEAAgd4EttY2S00D34F6c+J8dgsQAMzu50ftQyagqwJfdOg0GVyU52nZwjfWm4loWzzH/NxZZxYECarXoZ/tIG8EEEAAAQQQQAABBBBAIJZAa3sno6BiAXEuNAIEAEPzKGlIWASGDSwwQcDpnuY0tnbIna+USmdAQ4G1vE1m9SsSAggggAACCCCAAAIIIBBmgfVm0Y92MxUSCYGwCxAADPsTpn1ZKTBv2gg5bOYoT93f31Qrz3+41XPMz50KMxdgc1uHn0WQNwIIIIAAAggggAACCCCQNoHtDa1mpFVr2sqnYASCFCAAGKQ2ZSGQgMD5B0+VESUFnjv+780NsnlHk+eYXzud5pdg+tswEgIIIIAAAggggAACCCAQNoEO84VnXTXfd8L2XGlPdAECgNFtOINAWgVKCvPkksO9Q4FbOzrlDjMUWP+yCiLpRLhBzj0YRJsoAwEEEEAAAQQQQAABBBDYYEY86fx/JARyRYAAYK48adqZlQJ7Txwmx84Z66n7msp6eeI/mzzH/NzRXoBtJvBIQgABBBBAAAEEEEAAAQTCIFDX3Ca68i8JgVwSIACYS0+btmalwDkHTJZdhhR56v7I2xWBdVdvbY9IGUOBPf7sIIAAAggggAACCCCAQHYKRMzCimurGiSg9RWzE4lah1KAAGAoHyuN8lOgX79+fmbfLe/CvAFy2ZEzxF1sh/nb6o8vrwmsZ15VXavUmAlySQgggAACCCCAAAIIIIBANgtsNHOqN7ay2GE2P0Pq3jcBAoB9c+OuHBaYOLw48NbPGjtYTv3MeE+55TVN8rfl5Z5jfu6sNRPktjMU2E9i8kYAAQQQQAABBBBAAAEfBZrbOmSj+R5FQiAXBQgA5uJTp81JCYwZXCRDi/OTyqMvN39p34kyZcRAz61PrtgsK7fUeo75taMT5JaZiXJJCCCAAAIIIIAAAggggEA2CpRW1UtA6ylmIw91DrkAAcCQP2Ca54/A9NEl0j/YkcCSN6C/XHbUTMlzFaxrAf/x5VLR32QFkSprW2RnY1sQRVEGAggggAACCCCAAAIIIJAygUqz6EdtU3vK8iMjBLJNgABgtj0x6psRAkX5A2T8sOCHAk82PQC/vP8kj0FlXYs8sLTMc8zPndLqeung12Z+EpM3AggggAACCCCAAAIIpFBARzNtYDRTCkXJKhsFCABm41OjzhkhMMEEAIsLBgRel5P2Gie7mTkB3emFlZXydlmN+5Bv2y1tnVLOX56++ZIxAggggAACCCCAAAIIpFagbFuDWUBRx0+REMhdAQKAufvsaXmSAv3NUNxpo0qSzCXx27XcS82qwIV53h/fu14tlZrGYFbq3aLd55sZCpz40+MOBBBAAAEEEEAAAQQQCFJgh/mOVF0fzPekINtFWQgkKuCNICR6N9cjkOMCuhjI6MGFgSuMHVIkXztwiqfcuuZ2az7Azoj/v9nSItZWNUgnQ4E9z4AdBBBAAAEEEEAAAQQQyBwBnbpobXVD5lSImiCQRgECgGnEp+hwCEwZOVDyBwS8IoihO3r2GJk7dbgH8b2NO+Xp97Z4jvm109TaIRU1TX5lT74IIIAAAggggAACCCCAQFICOnWRTmFEQgABEQKAvAUIJCmQb1bnnWyCgEGnfv36ycWHTZfhA/M9RT/41gZZF9BvuTbtbJL6FlbS8jwAdhBAAAEEEEAAAQQQQCDtAvo9RacuIiGAwCcCBAB5ExBIgcCYwUUypDgvBTkllsXgony57MiZ4u5/qN3cb3tptbS0dySWWR+u/mQocL1EAhh23IfqcQsCCCCAAAIIIIAAAgjkoIB+P1lbpd9TcrDxNBmBKAIEAKPAcBiBRAWmjxokZn2OwNOeE4bKSXuP85S7aUez/GVJmeeYXzsNLQwF9suWfBFAAAEEEEAAAQQQQCBxgbJtjaLfU0gIIPCpAAHATy3YQiApgeKCATJ+WHFSefT15jP3n9RtReIXVlbKW+u39zXLhO7buKNJGlsZCpwQGhcjgAACCCCAAAIIIIBAygW2mmG/m3cy9DflsGSY9QIEALP+EdKATBKYYAKAGggMOuWZeQivPGqmFOZ5f6TvfnWtbG/wf8l77VpfWtnAUOCgHzzlIYAAAggggAACCCCAgCOwo7E1sPnQnULZQCBLBLzRgiypNNVEIFMF+psxwNNGlaSleuNM8PH8g6Z6ytaJb//48hrpDGDyCy2L37R5+NlBAAEEEEAAAQQQQACBgAR0RNLqSub9C4ibYrJQgABgFj40qpzZAkOL82X04MK0VPLI3UbLvGkjPGW/v6lWFq3Y7Dnm10759kZpamWuDb98yRcBBBBAAAEEEEAAAQS6C7R1dMrKLXXS3sGqH911OILAJwIEAHkTEPBBYMrIgZI/IPgVQfr16ycXHzpdRpQUeFr18PLyQLrCmwWIpdRabYu/eD0PgB0EEEAAAQQQQAABBBDwRaDTfAn52AT/Wto6fcmfTBEIiwABwLA8SdqRUQL5Zk6+ySYImI40qChPLj9yhrjDjx3mL8U/vLhamtv8751X19wum5h0Nx2PnjIRQAABBBBAAAEEEMg5gbXV9aLfQUgIIBBbgABgbB/OItBngTGDi2RIcV6f70/mxjnjh8op+4z3ZKHz892/pMxzzK+dCjMUmFWB/dIlXwQQQAABBBBAAAEEEFCBippGqarzf9FDtBEIgwABwDA8RdqQsQLTRw0Ssy5IWtIZ+02UGaO9C5K89HGlvLlum+/1sYYCsyqw784UgAACCCCAAAIIIIBArgpU17dI+famXG0+7UYgYQECgAmTcQMC8QsUFwxI21DgvP795YqjZklhnvfH/E+vrZVt5i9Lv5OuClxRw1/IfjuTPwIIIIAAAggggAACuSZQ19wmpWbFXxICCMQv4I0MxH8fVyKAQJwC44YWy/CS/DivTu1luwwtkgsOmerJtKGlQ+54uVQ6I/4v1LFxR5M0mEAgCQEEEEAAAQQQQAABBBBIhYDOa75qa535PpOK3MgDgdwRIACYO8+alqZRYMboQVKQl56xwIfPGi0HTh/haf2Hm2vluQ+2eo75saMxxjXmN3O6MhcJAQQQQAABBBBAAAEEEEhGQBc31BV/W9v5fpGMI/fmpgABwNx87rQ6YAFdFXjm6MEBl/pJcf369ZMLD50uI0sKPOU/9NYG2Vrb7Dnmx05ja4doT0ASAggggAACCCCAAAIIINBXgYjpXbC6ss4sNtjR1yy4D4GcFiAAmNOPn8YHKTB0YL5MGFYcZJFOWYMK8+TSI2c4+7rR0t4pd74S3FBgnaeDhAACCCCAAAIIIIAAAgj0RaBsW6PUNGTGd4pW812KhEC2CRAAzLYnRn2zWmDSiGIZXJSXljbsMX6ofH7OWE/ZK033+ec/DGYocGlVA0OBPfrsIIAAAggggAACCCCAQDwClWbk0uad/o9eiqcuja3t8sN/vCe/fX6VaK9EEgLZIkAAMFueFPUMhYAOx505ZpDkDUjPfIDnzJssowcVeiwfXBbMUOAm01W/vKbRUzY7CCCAAAIIIIAAAggggEAsAR1JtK66IdYlgZ1r7+iUW0zgb111o/z+hdXyP4++J3qMhEA2CBAAzIanRB1DJVCUP0CmjixJS5u07G8cPt1Ttg4FvuvVYIYC62/tahkK7PFnBwEEEEAAAQQQQAABBHoW0KG2q7aaRQUzoKNdp+ntp1Movb+p1qnsQ2+Vy0+f/NDZZwOBTBYgAJjJT4e6hVZg9OBC0T/pSHtOGCqf2907FPijzQEOBTarAuvqXSQEEEAAAQQQQAABBBBAIJqADq9dtVVX/M2MHnY6cur10m2e6o4aVGAWXJzmOcYOApkqQAAwU58M9Qq9wLRRJVKUn54fQR0KrH9ZuVNQQ4Gb2zplw3aGArvt2UYAAQQQQAABBBBAAAGvwHqz6Eddc7v3YJr2nnpvszy5YrOn9IEFA+Te+XNlSppGd3kqww4CcQikJ/oQR8W4BIGwCwzo309mjR0sZlrAwFOx+cvqksO7rwoc1FDgLWYo8M7GzFjBK3B8CkQAAQQQQAABBBBAAIGYAlV1LaLfGTIhLSmtlr8sLfNURb/L3XHuvrL3xGGe4+wgkMkCBAAz+elQt9ALDCrMk8kjBqalnZ8MBR7jKVuHAv8rgFWBtdDSaoYCe/DZQQABBBBAAAEEEEAAAalvaZe1VfUZIfHBpp1yx8ul3epy6RHT5cjdvN+lul3EAQQyTIAAYIY9EKqTewLjhxXLsIH5aWn4OfOmdBsK/H8BrQrcYoYCr9+WGat5pQWfQhFAAAEEEEAAAQQQQMAj0GZW1NV5/zJhyvAy813lN8+tkvYulfnK3Ely1GyCf54Hx05WCBAAzIrHRCXDLjBj9CApyAt+LHC0ocB3v7rW/KXr/0IdlbUtsqOxNeyPl/YhgAACCCCAAAIIIIBALwK66Mdqs+KvdhRId9IhyL94ZqU0tXV4qnLsnLFy6mfGe46xg0C2CBAAzJYnRT1DLVCQ1180CJiOpEOBj+nyG6wPN9fKvz7aGkh1dHJf/cuehAACCCCAAAIIIIAAArkroAsF7mxK/zzh9WbhkV8885HpqOCty7xpI+T8g6aaOdyD77iRu28FLU+lAAHAVGqSFwJJCAwbWCDjhxUlkUPfbz3ngO6rAv/fmxukstb/iXebWjtkc4ZM8Nt3Qe5EAAEEEEAAAQQQQACBvgpU17fIph3+f/forX6t7Z3yq+dWdqvL7F0Gy+VHzpT+ZvEPEgLZKkAAMFufHPUOpcCk4QNFFwYJOg0syJNv9LgqcDBDgStqmkT/siUhgAACCCCAAAIIIIBAbgk0tuqiH+mfG7zTzPX3hxdXmzkIvQuQTBxeLFcfu5uZsonwSW69meFrLW9w+J4pLcpiAf2N0qyxgyRvQPC/WdorjUOBO8xfttrln4QAAggggAACCCCAAAK5I9BuFv34eEud6PeBdCadkujPb6yT5WU1nmqMKCmQHxw/Oy2dNDwVYQeBFAgQAEwBYpBZVFZWypNPPinXXXednHDCCTJq1ChrDgKdh2D+/PkJV+Xpp5+W0047TSZOnCiFhYXWp+7rcVJ6BIryB6RtPsB0DgXWiXbrmr3zbKTnCVAqAggggAACCCCAAAII+C2gQbc1VfXSnAGLfvzjnY1mDvRKT5NLCgZYwb+Rgwo9x9lBIFsFgh9rmK1SGVLvsWPHpqQmnZ2d8o1vfEPuueceT34bN24U/fPYY4/JRRddJHfddZeZ54A4sQcpgB39TdO4oUWBz42nQ4EvPmy63PT0SqeVLWZo7l1mVeAfnbi79Pd5wtv11Y2y54QhTKzr6LOBAAIIIIAAAggggEA4BXQaoJqG9HcAePnjSvnb2xUe5HwzIkuH/U4aMdBznB0EslmAyE4WP73JkyfLscce26cW/OhHP3KCf5/97GflwQcflGXLllmfuq9pwYIF8uMf/7hP+XNT8gJTRg6UwUXBx+j3njhMju5hVeBn3t+SfKN6yaG+pV20JyAJAQQQQAABBBBAAAEEwiuwvaFVNACY7rSuukEWLF7nqYZOxnT5UTNl93FDPMfZQSDbBQgAZtkT1KG/TzzxhGzZskXKysqsHnqJNmHVqlXy61//2rpt//33l9dff13OOussmTt3rvW5ePFi0eOafvWrX8maNWusbf4TrIAO6545Jj3zAZ5rVgUeaXohutNDb20IZJ4+nQtQ5wIhIYAAAggggAACCCCAQPgEmts6pNQM/U130nrcZhb96Dr/4PkHT5UDpo1Md/UoH4GUCxAATDmpvxneeOONctJJJ0kyQ4FvvfVWaW9vtyr6hz/8QYqLiz2VHjhwoOhxTXrdLbfc4jnPTnAC6ZoPUIcCf/OIGZ6GtnVE5LaX1vi+Wq+Wkwm/DfQ0nh0EEEAAAQQQQAABBBBIWkBX2l21tc78wj+9i35oQx5YWiabdjZ72vSFvcbJcXvs4jnGDgJhESAAGJYnGWc7dKLVf/7zn9bVs2fPlgMPPLDHO/X4brvtZp3T6/U+UnoEdD7A8cOKAi98T7MqsP4F6E7lpnfew6YnoN9pS22zNLZ+EqT2uyzyRwABBBBAAAEEEEAAgWAE1pohtw0tHcEUFqOUt9ZtlxdWehf9mD6qRM6eOynGXZxCILsFCABm9/NLuPbr1q2TTZs2WfcdccQRMe+3z+uiIOvXr495LSf9FZhsJp9Nx3yAZ5m/ALVsd3rKzAW4omKH+1DKtzXerAuCkBBAAAEEEEAAAQQQQCAcApXml/yZMN+3zj9492trPaiFef3liqNnSt4AQiQeGHZCJcDbHarH2XtjPvzwQ+ci7QEYK7nPf/TRR7Eu5ZzPAvZ8gLoaVZAp3/wFeIWZALdruXe+Uip1zf6u2LWzqU221bMgSJDPm7IQQAABBBBAAAEEEPBDQBf70wU30p06TU+DP768RrQ+7qTz/o0b6p0ay32ebQTCIBD8EqNhUMviNlRUfLq8+cSJE2O2ZNKkT7s/l5eXx7y260l3OV3P6f7mzZt7OsyxGAL2fIArt9TFuCr1pyaZHoBnz5ss9y8pczKvaWyzVsv6zjGzRIOTfqUyM+R4+MAC6d/fvzL8qjv5IoAAAggggAACCCCAgJlX3izwp/P+men/0p4Wrdgs72+q9dTjgGkj5MhdR3uOsYNAGAUIAIbxqcZoU13dp8GjQYMGxbhSpKSkxDlfX5/YKk3u4KGTCRtJCww38wFOGFYsG3c0JZ1XIhnoRLjvbtghKzbudG5bZubNeHV1lRyx6xjnWKo3Wto6rbZqEJKEAAIIIIAAAggggAAC2Sewxqz4q/+uT3fSHogPL/d2bNH51i86bLqvnRrS3W7KR8AWYAiwLZEjn83Nn65yVFBQELPVhYWFzvmmpmADTk7BbHQTmDSiOPD5APubXn6XmFWBBxV6f2ew8I31stXM5eFn2mSCnc1t6Z8o2M82kjcCCCCAAAIIIIAAAmEUqKhplJoGf6cOisdNv0/84cXV0uHqhqhjjC430x11/Y4TT35cg0A2ChAAzManlkSdi4o+XU22tbU1Zk4tLZ/Ov1ZcnNh8CDpkONafZcuWxSybk9EFdMjtrLGDus3LF/2O1JzR3459w/x2zJ2azW/ybn9pjecvUvf5VGzr39EbzFBgEgIIIIAAAggggAACCGSPwE4zbVBFTWZ0JPnL0jLZvNPbceGUfcbLnHFDsgeUmiKQpIC3O0+SmXF75gsMHjzYqWRvw3obGj6dpLW34cJOpv/d6G1+wa7Xs5+YQGHeAJk5ZpB8tPnTId2J5dC3q+ea+TGO2m20vPRxlZPB6sp6eezdjfKlfWPPKenc0IeNbfWtsnNwmwwdmN+Hu7kFAQQQQAABBBBAAAEEghRoae+Q1ZV1YtbcSHt6y0xd9OLKSk89ZowukTP28+/7i6cwdhDIEAF6AGbIgwiqGu7AXG8LdbgX/mBOv6CeUPzlDDOLY+h8gEGn8w6aKmOHfDo8XMt/9N8VstpM7OtnWr+twfwDIgP+BeFnI8kbAQQQQAABBBBAAIEsF9B/s6/eWi9tHen/t/v2hla5+7W1HtHCvP5yxVGzJK8/4RAPDDuhF+CND/0j9jZwzpw5zoGVK1c62z1tuM/vvvvuPV3CsTQL6HyAQ4qD7cirqxFfYebKcC/Mq8N0b395ja9z9TW2dsgWn+cbTPPjpHgEEEAAAQQQQAABBLJeYP22Rqlrbk97OzpNIPIO8x2lvsVbl/kHT5Vdhn46NVbaK0oFEAhIgABgQNCZUsy0adNk/PjxVnVeeeWVmNV69dVXrfMTJkyQqVOnxryWk+kRsOYDHDNYCvJ0Ctvg0kxT5uldhvxurW2R+5es97USOodIa3v6VxDztZFkjgACCCCAAAIIIIBAlgpU17fIli5z7aWrKU+u2CwfbKr1FH+AmdLoiF1He46xg0CuCBAAzJUn/d92asDo1FNPtfa0h9/SpUt7FNDjdg9AvV7vI2WmQIHpwj5r7GDzjIKt3xf3mSCzzDyE7qRzA+ocG36ldjOMYKNZFZiEAAIIIIAAAggggAACmSXQ2Noua6s+nUc+nbVbW1Uv/++tck8VRppFDS8yixry3dbDwk4OCRAAzKGHbTf1O9/5jgwYMMDavfLKK6WpyRtQ0X09rikvL0/0elJmCwwpypcpIwcGWskBZgzw5WYocFG+938jOseGzrXhV9pqhgE3t3X4lT35IoAAAggggAACCCCAQIICHWZOoFVm3j/9THfS7wq3vbRGOlzzh2tficvMd5dBhcFOn5RuC8pHwC3A2+/WyILtxYsXy5o1a5yaVldXO9t6fOHChc6+bsyfP9+zrzu77rqrXHPNNfKLX/xCli9fLocccohce+21MmPGDCktLZWbb75Z3nnnHes+vW7WrFnd8uBA5gmMG1pszbWhK+YGlcYOKRKdQ+POVz6dWFfn2LjrlVK59oTZZp7A1HdL1L/HK2oazSrIn65oHVR7KQcBBBBAAAEEEEAAAQS6C5SZBfuazJzd6U66AMnCN9bL5i7DkE/dZ7zMGTck3dWjfATSKtDP/ICkP0SfVoLsKlwDevfdd1/clY72eDs7O+Xiiy+We++9N2peF154odx9993S34fVkXQFYntlYV1t2L06cdQKcaJXAf2N23sbdwb6l6++Y797YbW82WXo79lzJ8kpZpiwX2nviUOlhN/g+cVLvggggAACCCCAAAIIxCWws6lNPuwy115cN6b4Iv1e8uCyDfKEmfvPnWaMLpEbTtkjpav+6hzs+00Z4S4mo7f5/p3RjyewynnH7gVWLAWlW0CDevfcc48sWrTImhNQFwYpKCiwFgjROf+eeuopWbBggS/Bv3S3Pczl67DcXccOEv0MKukcGhceOk2GD8z3FPnw8nJZudk76a7ngiR3NmxvTDIHbkcAAQQQQAABBBBAAIFkBDpNBwSdby8T0iNvV3QL/ul0RVccNSulwb9MaCt1QKAvAvQA7Isa9yQtwG8gkiaMmYGuvrXazMERZPpg0075+VMfibtPsQYFbzp9bxla7A0Opqpec8YP8S3vVNWRfBBAAAEEEEAAAQQQCKuADv3dtKM57c179N8V8jcTAHQn7RJx5dGz5KAZI92HU7JND8CUMJJJwAL0AAwYnOIQCEJg1KBCGTe0KIiinDL2GD9UvrTvRGdfN2oa2+QOMwFvpzsq6LkiuZ1yegEmB8jdCCCAAAIIIIAAAgj0UUDn/u46114fs0rqtsff3dhj8O+SI2b4EvxLqrLcjEAaBQgAphGfohHwU0BXBR5cFOw6P6eZOf/2nDDU06wVZk7Cx97Z6DmWqp265nZfVxxOVT3JBwEEEEAAAQQQQACBMAnofHs69Nen3/PHTfXUe5vlwbfKu11/4WHT5IhdR3c7zgEEclmAAGAuP33aHmoBnZtvlpkPULunB5X6m7kHrzhqpgzrMh/gI6ZLvg4R9iNpL8Boi934UR55IoAAAggggAACCCCQ6wIbdzRJQ0t6V/199oMt8pelZd0exQWHTJVjZo/tdpwDCOS6AAHAXH8DaH+oBQrzBsjMMYPFxAIDSzrfn8614S5TfzN424trZEdja8rr0djaIVVmzkMSAggggAACCCCAAAII+C/QZP79vbGmyf+CYpTwr4+2ysI31ne74msHTpFj5+zS7TgHEEBAhAAgbwECIRfQgNykEQMDbeWccUPkzP0mecrc0dQmt+l8gGalsFSnCvMPED/yTXU9yQ8BBBBAAAEEEEAAgWwW0JE3pWborw//pI+b5aWPK+Wexeu6XX/OvMnyhb3GdTvOAQQQ+ESAACBvAgI5IDBhWLGMKCkItKWn7DNePjPROx/gB5tq5e/veFfnSkWlWto6ZUtt+lcfS0VbyAMBBBBAAAEEEEAAgUwV0H9z6zzc6Uqvra6SP726tlvxZ+4/SU7+zPhuxzmAAAKfChAA/NSCLQRCLTBjdIkU5Qf3I9/fjAG+zMwH2DXw+I9/b5QVFTtSbr3JzEPS3tGZ8nzJEAEEEEAAAQQQQAABBESa2zqkfHv6hv6+UVotf3ylVLqOJzp93wly2mcn8IgQQKAXgeCiAb1UhNMIIOCvQN6A/rLr2MFi1ukILA0pypdvmfkA3WXqX9i3m6HA2xtSOx9gW0dENu+kF2BgD5eCEEAAAQQQQAABBHJKYG1Vg3Skaezvm+u2Wd8huq46fIrp9XfGvhNz6jnQWAT6KkAAsK9y3IdAFgqUFObJ1FElgdZ8t10Gy1lzJ3vKrDXDBv7w4uqU/wNCA4Ct7fQC9GCzgwACCCCAAAIIIIBAkgKVdc2y08zpnY60vGy7/OEFM5d4l65/J5r5/s6aO8ksPhhgD4d0AFAmAikSIACYIkiyQSBbBMYOKZKRg4KdD/DEvcfJvpOHeYhWbqmTR94u9xxLdkd/I7nRDAUmIYAAAggggAACCCCAQGoE9BfsZdsaU5NZgrl8tLlWfv+C6TjQpevfcXvsIuceMJngX4KeXJ7bAgQAc/v50/ocFZhmegEW5AX346/zAV56xEwZ1SXw+Ni7m+Td8pqUPoWtZmJinZ+EhAACCCCAAAIIIIAAAskLrN/WYOba7tL9Lvlse82hfHuj/Pq5j0Wn+nGnz+0+Rs4/aArBPzcK2wjEIRBcBCCOynAJAggEI5Bv5gOcOWaQ+UszmPK0lEFFedZ8gAO6FHr7S6Wyrb4lZRXRXw5W1KTnN5QpawQZIYAAAggggAACCCCQAQL67/Rt9amduzueZmm5v3hmpTS2en+xf8Suo+WCQ6YR/IsHkWsQ6CJAALALCLsI5IrA0OJ8mTCsONDmzjKLkJxjuuq7U31Lu/w+xfMBVtW1SoPJl4QAAggggAACCCCAAAJ9E2jv6BTt/Rd00u8HNz29stuigftNGS4XHzbdLDAYYC+GoBtPeQj4KEAA0EdcskYg0wUmDi+WwaZnXpDphD13kf3NX97utGprvbz8caX7UNLb5fQCTNqQDBBAAAEEEEAAAQRyV2C9mfevtd07/NZvDZ1v8Ddm2G/Xeb1nmdFLVx49Uwb0J/jn9zMg//AKEAAM77OlZQj0KqArZulQ4LwBwf1FqmVecsQMGT2o0FO///d2henin7peezUNbWlbqczTMHYQQAABBBBAAAEEEMgygZqGVqmqS900PfE0v9Ms6Hf7y2tEFwt0p/HDiuSa43aTwrwB7sNsI4BAggIEABME43IEwiZQlD9Apo4sCbRZgwrz5BuHT/eUWdvUJo//Z5PnWLI7OnEwCQEEEEAAAQQQQAABBOIXaGnvkNKq+vhvSMGVETOR931L1suydds9uQ0bmC8/OH53M2op33OcHQQQSFyAAGDiZtyBQOgERg8ulNGDCwJt154Thsq+k71DgZ96b3NKf9NY19ye0gVGAgWiMAQQQAABBBBAAAEEAhbQQNxqMz1P15V3/a6GdgR47sOtnmKKTUeFa4+fbb6neEcOeS5iBwEE4hYgABg3FRciEG6BaaMGSVF+sP9LONcsCOJeFVj/ofHQWxtSCq0TF7eZCYxJCCCAAAIIIIAAAgggEFugoqZJ9JfoQaZXV1WZ7wDlniJ1rr+rPr9r4COVPJVgB4GQCQT7bT9keDQHgTAJ6F+yukpvkItqjTerEH9+zlgP4xul28xvHb3zfnguSHBHJy4uS8PqZQlWk8sRQAABBBBAAAEEEEirwM7Gtm6Lb/hdoXfLd8jdr67tVsxlR84QHTFEQgCB1AkQAEydJTkh8P/ZOw/4OIrz7z+WTu2kU6+2ZFldlo3BNjbGgI0rpkMgoQSckARCJ4Se3v4EXhJaSOgBAglJaDGmGWyMCwZjsMFNVu+993YS7zxrzt6R5JN0t7t3uvs9n498O7NTvyvrZp+Z53kmPQH2zZcUaTZ0Ht+aN42C/WWHvi9+WkZsfqCVNHT0U7NwZAwBARAAARAAARAAARAAARAYSYCj7xY2dIg1+Mh7euUUCz+DD2/Mp8FhnV5xUjItTovWq1tN2g0JgE9CTUCiEUMJQAFoKG50BgLuT2BqWCCFBRn3hcYOfb81L1ECU1DfSZ8WN0l5ziZKGtmXCUyBneWI+iAAAiAAAiAAAiAAAp5FQPH7V99BbDljlNS199L9G/KoTyge1XLWcQl09pwEdZbbXbPv9My4ELcbFwYEAmMRgAJwLEK4DwJeRmCKsAFOiw0mP98phs18tTADjg8NlPr712flYhEiLwikAhNMwBR4gsBQHARAAARAAARAAARAwCsIsN+/9h7j/P619QzQfe8eEn0OSHxPTosi9hHuzpIgDkukx7LbJOPeldyZB8Y2uQhAATi5nhdGCwKGEAgw+VJqjHG7WiZfH7p8ofxl39jZT+/tr9F0vjAF1hQnGgMBEAABEAABEAABEJjkBIz2+9dnHaQHNhyiWnECUC05CaF03dI08nFjxVpSZBDNiA5WDxvXIDCpCEABOKkeFwYLAsYRiAz2p7jQAMM6PHFGBM1MsEj9/e/LauIdQi0FpsBa0kRbIAACIAACIAACIAACk5WA0X7/hoSvv8c/KqKihi4J2XThg/y21ZnCAsk91ROsk0yNCabECGN9pUuQkAABDQi45/8wDSaGJkAABJwnMCMqmMzDAnQ43+roLfAx+isXzSD1YfqegUF69YuK0Ss4mAtTYAfBoRoIgAAIgAAIgAAIgIDHEGC/f4XC77aRfv9e+bySdpY0SwyjQ/zprjXZ4p3DJOW7S8JHvJxkxIaIgxGyuyJ3GR/GAQITIQAF4ERooSwIeBkBH/GNlxlnIV/+5jNAUsSR+tMy5Ihfmw7VU0Vzt6a9wxRYU5xoDARAAARAAARAAARAYJIRqGrt0dzSxh6CbQUN9L8vq6QifNCAlX9seeSOwu9A2fGhFBVinFWUO3LAmDyHABSAnvMsMRMQ0IVAkPhi5iPvRsklC6aTv+r4v9icpJd2lmnePUyBNUeKBkEABEAABEAABEAABCYBAXaxw4E/jJK82g56amux1B2fL7hlRYbbmtVyQMScqaEUZvaTxo0ECExmAlAATuanh7GDgEEEosWuV7yIeGWE8A7guccnSF3trWyjLytapTxnEzAFdpYg6oMACIAACIAACIAACEw2AgODQ4rpL2+yGyF1ItjHnz/II+uQ3OH3F8+gOYnhRgxhwn0E+PnQ7GlhFBLgnmbJE54QKoDANwSgAMSvAgiAwLgIzIgykyXQmC/Bc+ZMpfBhu20vfVpGg8MWDuMauJ1CMAW2Awe3QAAEQAAEQAAEQAAEPI7AYb9/Q4bMq7vfKiL+5lFHr1Xqb82seFqVEy/luUuCzZJniZN/gX6+7jIkjAMENCMABaBmKNEQCHg2AQ7SkS4c4PJxeL2Fv3AvXZAkdcN+Sj4U/gC1FpgCa00U7YEACIAACIAACIAACLgjAV5Pt3YPGDI03rh/ZFMBcZ9qOSEpnK5YlKzOcptrPuzAyr8AE5R/bvNQMBBNCUABqClONAYCnk2AFXOsBDRCTsuIIQ4KohaOCMw7iVoKTIG1pIm2QAAEQAAEQAAEQAAE3JEA+/3TOrCevXn+45NSYjc+akmMCKKblqcbFmBQ3fdY16z8m5kQSiaVL/Kx6uA+CEw2AlAATrYnhvGCgIsJhJv9hbPeIN1H4SNOHF5x0nSpn3ZhPrDuy2opT4sETIG1oIg2QAAEQAAEQAAEQAAE3JFAR+8AcSAOo/z+bThQS+8frJNQhAoF251nZJHZ3xiXQlLnYyTY519WvMUtFZNjDB23QWBCBKAAnBAuFAYBEGACrAAMC9I/IlbO1DA6MTlCgv7OvhqqF86EtRaYAmtNFO2BAAiAAAiAAAiAAAi4mgBbz7DyT2tf2seaFwfue+GTUuk2uxC6bXUWxViMCSoodT5GwleEI86Kswg3R1CNjIEKtz2AAH7LPeAhYgogYDQB9geYERdC/ib9/4RcLk4B+or+bMIRxF7eVW5LavYJU2DNUKIhEAABEAABEAABEAABNyDQ0z9IuTXtNDAoR+DVa2hsYvyo8Ps3/KThj5ekUaZQsrmjpMUEUzCi/brjo8GYdCCg/9u7DoNGkyAAAq4nwLtkrARU6eZ0GVRCWBCtnhUntf1pcTPl13VIeVok2BS4qbNPi6bQBgiAAAiAAAiAAAiAAAi4jEDvwCAdFMo/3uQ2QtqFj0GO+Nsj+lXLt+ZNo1PSo9VZbnPNVk1RIQFuMx4MBAT0JgAFoN6E0T4IeDCB0EA/mh5p1n2G35qbKHbm5GhcL39WLnYXtV/QFDd2Ee+WQkAABEAABEAABEAABEBgMhLotw4pJ//40wjhfv78QR41DNtIPzk1ii6el2jEECbcR2SwPyUZ8B4z4YGhAgjoSAAKQB3homkQ8AYCU8ODiL9A9ZQQ4TSYlYBqOSR8mewRPka0FqswkeDThUb5SdF6/GgPBEAABEAABEAABEDAewkMDB5W/vUOGKP84w35Z7YVi/VzpwSdTWuvXZomrIWOuvKRCrgwYfb3pfTYEBeOAF2DgGsIQAHoGu7oFQQ8igB/wQeK6Fl6yqqcOIoOkRWN/95VQUPCJ6DW0i1OABY1yIsYrftAeyAAAiAAAiAAAiAAAiCgJQGrUP4dqukgXssaJR/lN9C2wkapuyhxOOB2EfTDCH/hUsfjSHBAEkT8HQcoFPFIAvq+sXskMkwKBEBgOAGT8AfIjn1FEC3dhH0OfufEJKl9djS8fdiCQyrgRKKps5+qW3ucaAFVQQAEQAAEQAAEQAAEQMAYAmy9whYynX1WYzoUvdS29dILO0ql/vhQwB1nZFG4Wd64lwq5KMGHETPEO0ugn+xayEXDQbcgYDgBKAANR44OQcAzCXD0rJToYF0nd0pa9Aifg698USGcG+tj4lAuFIxtwqExBARAAARAAARAAARAAATclQBbxLALm45e45R/1qEh+utHhdQ3bB1+3dJ0So7S953A0ecwQ4wrLMjP0eqoBwKTngAUgJP+EWICIOA+BGJDAykuVL9IWj7iiOGlC+RTgI3ipN7G3DpdIHCMkcL6DrGwMc6MQpeJoFEQAAEQAAEQAAEQAAGPJMA++ArqO6m129hN6zf2VIl1suwyZ3l2LC1MiXRLzvyOEh8W6JZjw6BAwCgCUAAaRRr9gICXEOBTgHrurJ2QFE4zEywSTV6AdPfrs+PZbxWLKuHUWA9fg9IkkAABEAABEAABEAABEACBCRBg5R/7rW7u6p9ALeeL8mlDXn+rJV4cBLhyUbI6y22uLSKgoN6WSm4zWQwEBOwQgALQDhzcAgEQmDgBjvSVGRdCQSK6lh7C7V++cLrUNPs6Wf9VtZSnZYLNKUqburRsEm2BAAiAAAiAAAiAAAiAgFMEShq7qKHDWOUfb7r/dXMhsaWMTXzF+vzG5elu6VsvQPgk5KAf7hiN2MYPnyBgFAEoAI0ijX5AwIsIcFCQbPFFy1G29JD0WAstnCGbF7yzr5ZauvVbANW191F9R68e00GbIAACIAACIAACIAACIDAhAjVtPcTrU6OFg37Ud8j9XjQ/kdJiQoweypj9+Qr3QVki6AcHE4SAAAgQ4X8CfgtAAAR0IcDRtXi3Ta/IwJcIX4DqtvsHh+i1Lyp1mYut0ZKGLkMjq9n6xScIgAAIgAAIgAAIgAAI2Aiwf+qK5h5b0rDPT4ubaGtBo9QfK9jOP36qlOcuibSYYOJAhRAQAIHDBKAAxG8CCICAbgQsgX6UFqvPbuDU8CA6PStWGvvmvHqqadVvMSQCrCkR1gaEshECAiAAAiAAAiAAAiAAAq4gUN7UTYO8MDVQmjr76JntxVKPQWLD/4ZlacSB+txJhEWyeAcJpqgQ/YITutN8MRYQGC8BKADHSwrlQAAEHCIQLb54EyOCHKo7VqWL5iWSv+pIP6+D/vN5xVjVnLrfNzCkBAVhp8sQEAABEAABEAABEAABEDCSQJuI9tvYqZ/bm9HmMiTWvY9vKaKuvkHp9lWnzKAYi3tF1jUJF0Qz40Mp1s3GJYFDAgRcRAAKQBeBR7cg4E0EkiLNYnHgr/mUI4P9ac3seKndnSXNVFjfKeVpnWjrGaDKFv1OGmo9XrQHAiAAAiAAAiAAAiAw+QnwBnSJCwLTvbOvhg5Ut0sAT06LolPTo6U8Vyc44MesqaEUZvZz9VDQPwi4JQEoAN3ysWBQIOB5BFKjQ8gSqL0PjvOEz5HgADni8MuflYvIZPqe0GMFYHOXsbuvnvdbgRmBAAiAAAiAAAiAAAiMl0B1Wy/19Mun8MZb19FypULh+O9dsoVNlNiE/+EpKW4VWTdE+PqbPTWMzP7av284yg71QMDdCEAB6G5PBOMBAQ8lwL5BOChIoNiZ01LYse8FJ0yTmjxY005fVbZJeXokiho6DV+E6TEPtAkCIAACIAACIAACIODeBDjwR5XBFij91iF67MNCyd8ge/u7flm6WwXXYKugHHHyz9+k7XuGe/9GYHQgMHEC+B8ycWaoAQIg4CABP+GvL1v45GDfHFrK6px44p1ItfxbnAJkfyV6inXwaypu1NfcWM/xo20QAAEQAAEQAAEQAIHJQaDMBYE//rmzjKqGBdg7V1jf5CSEug20hLBAyowLIV83C0TiNoAwEBBQEYACUAUDlyAAAvoTCPL3FV/SFmEyoF1fvNv37RMTpQbLmrtpR1GTlKdHor3HShwVDQICIAACIAACIAACIAACehDgwB9NBgf++LKihd4/WCdNJyU6mL49X15zSwUMTPC7xIxos/gJditTZAMRoCsQmDABKAAnjAwVQAAEnCUQFuRHqTHBzjYj1T8tPWZEtOH/Cn8lA4NDUjk9EqxsHOIQxBAQAAEQAAEQAAEQAAEQ0JAArzGNDvzRLgLePbGlWJqFv7DkuUGY/prEp6uFT/vxgYKEsCBXDwX9g8CkIuD6/72TChcGCwIgoBWBWEsgTQvX7kubfQxeumC6NLwGcTJvU668cykV0CjRNzA0wjxCo6bRDAiAAAiAAAiAAAiAgBcTqGk3NvAHB9J7alsxtQkloFquWDRd07W7uu2JXPubpij+/tjvHwQEQGBiBKAAnBgvlAYBENCQwPQoM4Wb/TRrcd70cMoSu4FqeX1PFXX3W9VZulxXC/8ovQPGRmXTZSJoFARAAARAAARAAARAwC0I8NrS6MAfb4i18xdlLdL8eY29cmaclOeKhFm4EpolIv1yxF8ICIDAxAlAAThxZqgBAiCgIYFkoQTUyh/gFNHQZQvlU4AdvVZ6e1+NhiMevSm2AC4XpsAQEAABEAABEAABEAABENCCgNGBP3aVNNMrX1RKQw8VrnuuWZLmcj97lkCTUP6FUqCfrzQ+JEAABMZPAArA8bNCSRAAAR0ImP1NFB0SoFnLWfEWOjE5Qmrv7b01hgTqYOfMw80lpIEgAQIgAAIgAAIgAAIgAALjINDa3U/NXf3jKKlNkbKmLvrrR4VSY7xJf8PpacT+u10p3P9MEXnYHfwPupID+gYBZwlAAegsQdQHARBwmkBSZBAJF36aySULkqRThX3WIXp8SxENCZ8mektpYxex7xQICIAACIAACIAACIAACDhCQAn8IdaURglvYP/p/TziNbNarjgpmeYkhquzDL9md0HZYoOfA39AQAAEnCMABaBz/FAbBEBAAwIBJl9No3glRpjp9MwYaWQHqtvpHQNMgbv7B6muvU/qGwkQAAEQAAEQAAEQAAEQGC+B6jb2LS0r48Zbd6LlBgaH6KEP8qlRWLKoZalYS585O16dZfh1RLCf4t+bg/1BQAAEnCcABaDzDNECCICABgSmhgeKY/3afblfLnYso4ZFB/v3rgoqMWA3tbKlm3gxBQEBEAABEAABEAABEACBiRAwMvAHW638fXsJ5dV1SEPkoHo/PDXFpX7/OMpvZqyFoPyTHg0SIOAUASgAncKHyiAAAloRYJ8e08KDtGpOiQ52/bJ0UqsUB0Wkjsc2FwjzBn2j9Q4Mfk0VCAii2bNEQyAAAiAAAiAAAiDgLQQ48AcHlzNC3t1fSx/lN0hdRYf4062rMslPrM1dJVFiDJlxIVD+ueoBoF+PJeC6/9UeixQTAwEQcJRAfGggBfhp92cpRzgLPu+EqdJwqlt76aVPy6U8PRL1HX3U1WfVo2m0CQIgAAIgAAIgAAIg4IEEWkTQD6MCf3xV0Uov7SyTKAaYfOi21VkuDfrBCsiM2BCXnj6UoCABAh5EQLs3bQ+CgqmAAAi4hgAf8U+M0O4UIM/i4nmJlBodLE1oY24dfV7WLOVpneA4IKUimhoEBEAABEAABEAABEAABMYiwIE/jFo7Vrf20KMfFojAdfKorj89nWZEyetmuYS+qRiLP6VD+acvZLTu1QSgAPTqx4/Jg4D7EYgJCSCzv69mA2PT4huFKTDvaKrlqa3F1NItOztW39fiur3HKhwqIyCIFizRBgiAAAiAAAiAAAh4MoEK4UPaiMAfncJC5YENecSB69Ty7fmJtDAlUp1l6HWMJYDSYnDyz1Do6MzrCMhvxF43fUwYBEDA3QhMmTKFpkeaNR1WgvAtuPbkGVKbHb1WeuKjIuFjZdjWp1TK+QT7cWHfgxAQAAEQAAEQAAEQAAEQGI0Abxizmxq9hdekj24qoNp2ua9FqZF04dxpend/zPbjQgNw8u+YdHADBLQjAAWgdizREgiAgEYEIkTUr9Agk0atHW5mWVYMLZgRIbW5t6qNNhyolfK0TvRbh8SCrkfrZtEeCIAACIAACIAACICABxDo7rdScYMxbmNe+rSM9on1r1pShKuca5emucznXnxYIKWKk38QEAAB/QlAAag/Y/QAAiDgAAGtTwHyycKrT0ulCLOfNJp/7SynMp199bECsHdANrOQBoEECIAACIAACIAACICA1xGwDg5Rfl2nIdYimw7V0XvDNr7Dg/zoNhHxN8CknfudiTzEBKH8YwUkBARAwBgCUAAawxm9gAAITJCAJdCPokQUMC2F27xOODdWi1WYQvx1cyHxST29hC2A2RQYAgIgAAIgAAIgAAIgAAI2AkXi5F/PMF98tntafubWtNNz20ulJv18p9BPhfIvSvjfdoWw8m8GlH+uQI8+vZgAFIBOPvz169fTlVdeSWeeeSZdf/31tHv3bidbRHUQAAEbAT4FKA7uaSrHTQujs49LkNqsaOmhlz8rl/K0TjR39VNb94DWzaI9EAABEAABEAABEACBSUigSliI8PpQbylu6KQHP8inwWF+r9kyJiPOonf3o7bPAT+g/BsVDTJBQFcCUADawbt582aKjY2l6dOnU2tr64iSv/zlL+mCCy6gf/3rX/T+++/Tk08+SYsWLaIXX3xxRFlkgAAITJxAoJ8vxYoFgtZyyYIkSo6SA42wScSXFS1adyW1VypMjb8etviSCiABAiAAAiAAAiAAAiDg8QR4U7iiWX/rkIPi5N8f3s4ljvyrlnPnJNBpGTHqLMOuI4Wv77QYmP0aBhwdgYCKABSAKhjDL9955x1qbGykBQsWUHh4uHR77969dO+99yov8/xCz/f502q10o9//GMqLS2VyiMBAiDgGIHECDP5+mh7DNDP14duWpZBbPqglie2FFNbj36n9LqFiUdNmxx1Td0/rkEABEAABEAABEAABDybAPuFLqjvEO+O+s5zd3kL3fduLvUM80M9NymcLl0wXd/Oj9E6B/nLiA1xWcCRYwwL2SDgNQSgALTzqLdv3678cVq5cuWIUo8//rii8IuIiKAvvviCmpqa6LPPPqPIyEjq6+ujJ554YkQdZIAACEycgL/Jh9hHiNYyLSKIrlyULDXLyr8ntxQp/7elGxomeLd3+C6shs2jKRAAARAAARAAARAAATclMCQcQxeIoB8Dg/pq/z4ubKQH388f0c9s4Qrn5hUZ5KPx5vp4cIcEmCg7PtQlfY9nfCgDAt5AAApAO0+5pqZGuTtr1qwRpd566y1FOXjjjTfS3LlzlfsnnngicZpPAm7cuHFEHWSAAAg4RmBqeNCI03qOtSTXWjkzjuZNl0/37qlopQ9y6+SCGqY4IEhBXYch0d40HDaaAgEQAAEQAAEQAAEQcJJAiXAHo/dG8AcH65QAd8N9/i2YEUF3npFF7GLHaAny96XsBIvmVj1GzwP9gcBkJwAFoJ0n2NDQoNwdbv5bVFREVVVVyr0LL7xQauG0005T0lwGAgIgoA0BNgFmU2CtZYqIMHLNkjQKC/KTmn7p0zJih8l6Se/AEJU06te+XuNGuyAAAiAAAiAAAiAAAo4RqG/vpfr2Pscqj7PWui+r6O8fl9Dw84VLMqLplhWZYkPd+Nf/AD8fmimUf67oe5zYUAwEvIaA8X8BJhFam7P+trY2adTbtm1T0mFhYXTCCSdI96KiopR0d7f+Tl2ljpEAAQ8nEBcaIHYstf+Txcq/a5emSfTYLOOB9/OoqVO/RVpDRz81dOjXvjQhJEAABEAABEAABEAABFxGgE/9lTR26dY/v7e+/Fk5/XtXxYg+1syOpx+Lta7WPrVHdDRKhr9pCs0UZr8BJuNPHY4yHGSBgNcT0P5t2oOQxsfHK7PJzc2VZrVhwwYlfcopp0j5nOjqOvyHnX0DQkAABLQjwKf1kiK1PwXIIzxBOENeM+vw/3fbiFtFdLb/tyGPekTgDr2EF4LsCBoCAiAAAiAAAiAAAiDgmQQGBocoX7h/YTcwegj7FXx2ewm9+VX1iOYvmpdIa4XPax+xjjZaTCLYHvv8Y/NfCAiAgHsQgALQznNYtGiR4s+PA37YTvQVFxfTunXrFP9/q1atGlE7Pz9fybMpD0cUQAYIgIDDBKJDAogdCOsh3z1pOuUkhEpNl4uAHY9+WKCbv77BbxxB204bS50jAQIgAAIgAAIgAAIgMKkJ8BqPg370CfcveohVKBcf21xImw7Vj2h+7cnJdPH8RJdE3OUYI1nxFgrWad0+YrLIAAEQGBcBKADtYPrRj36k3N27dy/Nnj2bLr74YmKlYG9vLwUFBdHll18+ovbWrVuVvMzMzBH3kAECIOA8gek6nQI0CZ8ot67KpKnDIg5/KYKC/OOTUucHfowW2CSEFY0QEAABEAABEAABEAABzyJQ2dJDbT0DukyqzzpIf/4gnz4pbpLa58N+1y5NpTNnJ0j5RiW4/8w4C4UGyj62jeof/YAACBybABSAx2ZDy5cvp1tuuUU5BVhaWkpvvPEGNTY2KjUeeOABio6OlmqzYtB2OnDJkiXSPSRAAAQnp00nAABAAElEQVS0IRBm9qNw8aOH8OnCO9dkkyVQPmX4voim9t7+w1HB9ei3urWXWrv79WgabYIACIAACIAACIAACLiAQHNXP7ECUA/p7rfSfe8eIt6oVotJHL37iQj2sTQzVp1t6HVaTAhFBPsb2ic6AwEQGB8B+S13fHW8qtRDDz1EK1asoFdeeYVqa2spISGB1q5dqygHh4N48803KTQ0lDg4yLnnnjv8NtIgAAIaEeBTgG09bUI5r1GDqmbiQgPptlVZ9Ie3D5JV5azlHyIycKwlkOYl6+Pfs0hEHT5uWjj5m7Avo3ocuAQBEAABEAABEACBSUegS1h4FNZ36jJu9il47zu5VNQgBxUJEGvInwprljmJ4br0O55GU6KDKcYSMJ6iKAMCIOACAlOEXwIdXqFdMBN0OakIVFZWUlJSkjLmiooKSkxMnFTjx2BdT6CwvkNE0dXv1NzHhY2KTxX1THlh9ZvzZtGMqGB1tmbXHJE4Z6rsh1CzxtEQCIAACIAACIAACICA7gT6rUO0r6qN+FMPeWdfDb0oNqbVEiwCbbAVC5veukqmhgdSsk5rZFfNyZP6xfu3Jz1Nx+eCoyaOs0NNEAABFxJIjDCLiGb6DeCU9GjFcbK6hz6xkHtARAZmkw49hH3EVLXqYyqix3jRJgiAAAiAAAiAAAiAwFECHJE3r7ZDN+Vf78AgrRsW7Zc3kH95To5LlX+hQSbSy0/3Ubq4AgEQcJYAFIAOELRardTQ0KD88DUEBEDAeAKBfr4UPyxgh9aj+NbcaXSaUASqhZV/D2w4RLwA00MqRECQjl59nEXrMV60CQIgAAIgAAIgAAIgcJhAoXDpwgHe9JL3D9RS+7CgImz268qTd+y+JiPW4pJow3pxRrsg4KkEoAAc55M9ePAg3XzzzZSTk0OBgYEUHx+v/PD1zJkz6aabbqL9+/ePszUUAwEQ0ILA1PAgMvnqdwxwighjdvWSVMqOl80pSpu6FfNg3uXVWtgpA/uMsQr/LhAQAAEQAAEQAAEQAIHJQYA3cZs69bESYQIc+GP9Xjko3QlJ4S49+Xc44m8IfFhPjl9RjBIECArAMX4JhoaG6LbbbqPjjz+e/vrXv9KhQ4eI89h1Iv/wdV5eHv3tb3+juXPn0q233qrkjdEsboMACGhAwM/Xh1gJqKdwH7yzGi+Cg6jli7IW+udO2f+K+r4z170DQ1TSKDt2dqY91AUBEAABEAABEAABENCPQENHn24Rf22jfm9/7YjThRfPd60fdfaLbQn0sw0RnyAAAm5OAFGAx3hAl19+uRIB2BYrZdasWbRw4UKKi4tTatbV1dGuXbuU03+Dg4P06KOPUnV1Nf3nP/8Zo2XcBgEQ0IJAglDM1bb16uZrhcfIC5s7z8iiX715QFp4vSMWYmyGvConXoupSG00ih3kMHOvEnlYuoEECIAACIAACIAACICA2xBg1y3FwvRXT+Gowhz8Qy0nJkdQWkyIOsvQ6xiLv+7ueAydEDoDAS8gAAWgnYf873//m/773/8q/gz4BOBTTz1FCxYsGLUGKwGvvfZa2rNnD7366qvEdS+99NJRyyITBEBAOwI+IhJIUkQQFTXoe2IuQZw0vFWcBLz3nVwaVJn+Pr+jlGIsgcQmGFpLaWM3WQL8KEhEdoOAAAiAAAiAAAiAAAi4FwH2CZ1f10GqpaEuA2TlX1e/7H/alaf/zGJtmhLtOuWjLpDRKAh4AQGYANt5yKzwY8nMzKTt27cfU/nHZVgxuHXrVsrKylJMg5988knOhoAACBhAIMYSQLwQ0VtyEkLpmtNSpW54wffopgLlFKJ0Q4MEKxrzxKJyAP4ANaCJJkAABEAABEAABEBAOwLKOk2J+Ku9T2j1KPmE4bvC6kQtJ6VEuizwB/vfzoyzkK/YhIeAAAhMLgJQANp5Xl999ZVy+u+uu+6i4OBgOyUP3+IyXJaF60JAAASMIcDBOqZHmg3pbElmDF0oogOrpUfs/j6+pVD4/9R+AdgjdnvzxOJSj7bVc8A1CIAACIAACIAACIDA+Aiwe6iC+g4RmEM+lTe+2hMr9ZYI/MFrTZuw2u2iea7z/ZcaHQzrFNvDwCcITDICUADaeWD9/YejOM2ZM8dOKfmWrezAwIB8AykQAAFdCUQE+wtffcZ4Nfi2cLh8clqUNJ/8uk56a2+1lKdVoqPXKhaZncrpYq3aRDsgAAIgAAIgAAIgAAKOEShr6qaWLv3f99p6BmjDAfn032KxBk0yaON7OJ2p4YEUFRIwPBtpEACBSUIACkA7Dyo5OVm529bWZqeUfKu9vV3JsNWV7yIFAiCgJ4HkKGNOAfKJQzYFjguVF0CvfFFJ5c3dukyxuaufSsViEwICIAACIAACIAACIOA6AnXtvVQjAtAZIeu/qqY+69CRrsQS1GWn/0KDTIZZ3ByZMC5AAAQ0JQAFoB2cF110kXLi5rXXXrNTSr7FAUBYOXDhhRfKN5ACARDQnQBH640K8de9H+4g0M+Xrl2aRmrvJ1ZhAvy3jwrJqpPPPo52XN3aY8j80AkIgAAIgAAIgAAIgIBMoK17gEoa9Q08Z+uxpbuf3j8on/47LT2aODCd0eJv8qGMWIvynmt03+gPBEBAOwJQANph+dOf/pRSU1OJA3pwNOCxhJV/XDYlJYVuv/32sYrjPgiAgA4EkiLMYnGiQ8OjNJkdH0pnz0mQ7rBJyOt7qqQ8LRPcfmNnn5ZNoi0QAAEQAAEQAAEQAIExCLBf5nzh90+4/zNE1n1ZLQLBHe3MVyxwv+UC33+8rs6ICyFWAkJAAAQmNwH8L7bz/MLCwmjjxo00b948uuyyy+iCCy6g//3vf1RVVUXs489qtSrXnMcn/i655BKl7KZNm4jrQkAABIwnECSiAceKqMBGybfnJ1FihLwTu+7LKioUPvv0kiLRNvuEgYAACIAACIAACIAACOhPYEBYdxyqbRdWHkcVcnr22iQ2ezfl1kldLM2KEe5nAqU8IxLsYidUWNlAQAAEJj+BKSKCkTF/xSYhK19f3yOjZkxs2mtPxlOG22DFobdLZWUlJSUlKRgqKiooMdF1kay8/Vl44vz7ha+ULytaaVCHqLyj8WJTkF/+bz8Nqv6cTg0LpD9+a45uu6Um3yk0a2oomf2NCXwy2ryRBwIgAAIgAAIgAAKeTmBIrCcP1rQTB2UzSp7dXkwbc+uPdOfrM4Ue+s4JFGPgJjd3Hi1c62TEWY6MAxeTlwDevyfvs9Ny5DgBaIcmK/RsP1zMdn2sz/GU4bruJBzp+JlnnqEzzjiDEhISKCAggEJCQigrK4uuuuoq2rFjhzsNF2MBgXERYBOFBKGAM0pSooOFScY0qbtq4a/v5V3lUp6WCd6Bzq3pEI6hB7VsFm2BAAiAAAiAAAiAAAioCBQ1dBqq/Gvo6KXNeQ2qERAtz441XPlnFlY1qTEh0jiQAAEQmNwEcHTEzvP79a9/befu5L9VVlZGZ599Nh04cECaDCsF8/PzlZ/nn3+ebrrpJnrkkUfGPAEpNYIECLiYwFThIJmjtKl9p+g5pPNPmEa7y1uoqOGoY+j39tfSickR4qSePi4B+KRjXm0H5SSEkskX+zl6Pl+0DQIgAAIgAAIg4H0EKprZ93K/oRN/Q/iSVlux+AmrjwvEOtNICfDzoewEC/HJQwgIgIDnEIAC0M6z9GQFIPswVCv/5syZQxz0hE/+dXR00Pbt2+nPf/4zdXV10V/+8heaOnUq3X333XZo4RYIuBcBXrAkioAgRkVq4/6uOz2d7nl9r6R0fGJLEd1/0RzdTHW7+oRD6rpOmikWaWO5KXCvJ4TRgAAIgAAIgAAIgID7EqgXJ/EqW3oMHWCtsCDZki+f/ls5M44ig/0NG4e/aYqyuRxgOuoOy7DO0REIgICuBHBkRFe87tv4unXrjpz8O/nkk2n37t30ve99jxYtWkSrVq2i3/72t7Rlyxby8zvs8PX++++H70L3fZwY2TEIxIUGUKDYwTRKpolTh5cumC51x7vGL35SJuVpneCAIGyeAgEBEAABEAABEAABEHCeAK+tilVWHc63OL4WXt9dSWoX1v7CwuO846eOr7IGpdjHdHZ8qFg/Q/mnAU40AQJuR8C4N2O3m7p3D0jt2++ee+4hdcATG5n58+fTOeecoyRbW1spNzfXdgufIDApCPCJuPTYEHEyzrjhrpkdr+yaqnv8SOzkflHWos7S/Lqho5/Km7o1bxcNggAIgAAIgAAIgIA3EejpZ+uKDuH/3dhZV7X20PaiRqnT1bPiKNxszOk/tvbNirdQcACMBKWHgAQIeBAB/O+ewMPk6L18Um7fvn3U3Nys1IyMjKTZs2fTvHnzjpyWm0CTLivKfv5skpqaarsc8ZmWlnYkT13nSCYuQMDNCVgC/Sg5ykyljcYox3yEtvHapal012v7qGfgaICOp7cViyhqcyhUjEcv4YUjn3iMDTUuAIpec0G7IAACIAACIAACIGA0AfavnFvbThxszWh5TZz+UysdeU13rkGn/3izPFNE+9VznWo0T/QHAiAwkgAUgCOZjMhhP3i///3v6dlnnz2i+BteKCIign74wx/SL37xC7JY3D9UOvv6s0lxcTHNmjXLlpQ+i4qKlDSfpMrIyJDuIQECk4VAQliQEr2tySAnzjGWQLpyUTI9JZR+NmFTkr9vL6FbVmTo6quPfR6GBJp08zlomw8+QQAEQAAEQAAEQMCTCAwJ21s++dc3MGT4tDjYyKdFTVK/a2YlGKaQSxPRfiMM9DMoTRQJEAABwwjABHgM1Hl5ecoJvwceeICamprErszXo/7wicA//elPdNxxxxHXcXe57LLLKDQ0VBkm+/cbHDx6Usk29j179tDbb7+tJC+//PIj5W338QkCk4lAanSwof4AT8+KoblJ4RKinSXNtGPY4k4qoEGC/cYUiKAgvIiFgAAIgAAIgAAIgAAIjI9AofCn3NFrHV9hjUu9yqf/VG0GCR98Zx+XoMrR73JGtJliLAH6dYCWQQAE3IYATgDaeRRtbW20YsUKqqmpUZR+bOrLgTIWLlxIcXFxSs26ujratWsXvfDCC4ppcHl5Oa1cuZL2799PYWFhdlp37a3o6Gh68cUXiRWBH3/8MS1YsIB+8pOfUGZmJnV2dip5HAWYzX7ZvJmvJyKVlZV2izNTCAgYScAknCizacP+qjbJubJeY+BTs1cvSaU7X91LnX1HF5PP7SgREXtDdY3m1i1815SJneQUofSEgAAIgAAIgAAIgAAI2CfAfpSNshQZPpJdpc30mdgkVstZQvnHFh16S2JEELGlDAQEQMA7CEwRJ9rUmw3eMetxzvJnP/sZ3XfffYq53u9+9zviNL/UjyaM8Y9//KNiAsxl7rrrLrr33ntHK+pWeYcOHVKUe2zePPxXgZWcHCDk6quvJrPZPKFxH4vTaI1UVFRQYmLiaLeQBwKaE6hv7xURc7s0b/dYDX4iTvw9+mGBdPsEcTLwzjOyjvn3RCrsRCJbOHKGOYcTAFEVBEAABEAABEDA4wkYvTZUAz1Y3Ub3vXeIBlQ+B4MDfOnRS+fq7s4lLjSAUoXpL8Q7CPABnaSkJGWyeP/2jmc+2ixhAjwalW/y3njjDeUF/Tvf+Q79/Oc/t/uyzgovVhBecskliiKN67q78Om+f/zjH7Ru3boRyj8eO59ufOmll2jjxo3uPhWMDwTGTYADZBhp5nByWhTxj1q+rGilt/fpfwq2SJiy9FlHmverx4JrEAABEAABEAABEPBWAm3dA1Qs/Ce7Qthv85/ez5eUfzyOi+Yl6q78iw7xh6WIKx46+gQBFxOAAtDOAygrK1Pufv/737dTSr5lK2urK991nxQHNmFTZT61yP4L77zzTsrNzaW+vj5i0+f333+fTj31VPr888/pggsuoAcffHBCg+ddBXs/n3322YTaQ2EQ0JIA+wPk3VWj5AeLUyg8SI7++/Jn5XRA7PrqKbybXFTfNaqCX89+0TYIgAAIgAAIgAAIuDuB7n4r5dd3iHWS8SOtbetVTv71DMgbtcuzY2nNrHhdBxRu9qP02BC7h1t0HQAaBwEQcBkBKADtoLdF842NjbVTSr5lKxsS4t7HqX/zm9/Qtm3blMGz+S8HAsnOziZ/f38l2MeqVato8+bNtGzZMkV5cMcdd9BXX30lT9ZOik167f0kJBjj1NbOEHHLiwn4+Iio1rEW8hWfRgj7cPnx0jRS98YxOh7ZVCD8zfTpOgSOPlwtFpkQEAABEAABEAABEACBwwT6rUN0qLaDrCrTW6PYtHT30x/fzaV2sUZTy8IZkfTDU1J0VcxZxJqUfWJPxF2Teoy4BgEQmNwEoAC08/w4oi9LQYHsv8tOlSNlbXXtlXXVPfb19/e//13pnoN+cGCT0cRkMtHvf/975dbQ0BA9//zzoxVDHghMSgJB/r6UFmNckAz2+3fRfNnXJUeae2hjPvEiVE+pEAFB1IFI9OwLbYMACIAACIAACICAOxMYEruw+XUd1Deg7/prNAZdIjDcfe8eovoOeQM4RwSIu2FZOvEmtV7C1i/sH9qoDXC95oF2QQAEHCcABaAddj/+8Y+V028PP/wwsQJsLOEyDz30kLKjcs0114xV3GX32bcfm/2yzJ071+445s+ff+Q+BwyBgIAnEYgKCaD4sEDDpnTh3Gk0b3q41B8HJHl+R6mUp3WCTVsKxEJ3kI8dQkAABEAABEAABEDAiwkUCh/JvAlrtPCG75/ez6NysTGrlhlRZrptdSb5m/R7NY8SPv9mTQ0jk69+fajnhGsQAAH3JIC/AHaey7e//W266qqr6NNPP1X84NXW1h6zNCvVvvWtb9HOnTuJ/QByMBB3FT7ZZxOr1f6X38DA0aPp6nq2+vgEgclOIDnSTGwOYYT4iGBB15+eTvEiEIlaNufV06ZDdeosza97xS43O5uGgAAIgAAIgAAIgIC3Eihv6hbuV/oNnz5vwrLrFzY7VguvCe9ak61r0I/EiCDF7Bcn/9TkcQ0C3knAmLdeN2fLkXCPJUuXLqX9+/fTW2+9RampqbR69WpasGABsa8/9p3Air9du3YpQTM4gAbf4zrc5tq1a4/VrEvzIyMjFT9/7e3t9MknnxArAY+l3NuyZcuRsaakpBy5xgUIeAoBNrVgR8j7q9pGRGHTY47BASb66apM+uW6/SJC79GTxc9/XEqsjEwXvgn1kgZhbsKOn6PFyUcICIAACIAACIAACHgTgfqOXqpq7TF8yux+6eltxbS7vEXqmwPE3XNmtlib+Uv5WiVY4cfubtjiBQICIAACTGCK+IPk9TZhPj4+43KEyqiO5TB1+D0uN9bpOlf+Cl5++eX08ssvK0PggCC//vWvRwynpaVFiQR88OBB5d6GDRsUBeiIgg5kVFZWUlJSklKTowVzwBAICLiSQEtX/4hdWT3H80lREz36oexfNDLYn/7vgtm6LQR5PibfKXTctDAK9DMuCrKeHNE2CIAACIAACIAACIxFgIOi5da0uyTi7z93ltFbe2ukIZqFL+pfnZNDyVH6+KMO8POhLBHsgzeeISDABPD+jd8DJgAT4G9+D1iBN9YPFz1WmdHufdO0W3786le/IrPZrIyNFYDnnXcevfbaa7Rnzx7lVCD7MjzhhBPIpvxbsWKFZso/twSCQXk9gQihfGMTCaPk5LQoOmeOHA27WSghWSloHYfPUUfHydHuCus7lb9ljraBeiAAAiAAAiAAAiAwWQj09A8qQT9ccexl/VfVI5R/fmIz9o4zsnRT/oUGmZTNXij/JstvKMYJAsYRwJaAYF1SUmIccTfpKTs7m9atW0eXXXYZNTY20vr165Wf0Ya3fPlyeuWVV0a7hTwQ8CgCrABkp9C8S2yEXLpgOhWLICAHxY60TXJrOujlneV05ckzbFmaf/IcK1t6KEmYHENAAARAAARAAARAwFMJDAwOCQuPduINUKNlS349/euzcqlbDvJ7y4pMEY03VMrXKhEXGkAp0cHHtFrTqh+0AwIgMDkJQAEonltycvLkfHpOjnrlypXEkX2fffZZevfdd+nAgQPU2tqq+AOMj49X/BmyqTCfDjyW6bOTQ0B1EHArAvx7nhVvoYPV7dTZZz9AjhYDZ98st6zIoJ+9sY+axOk/m7yzv5ZSY0LolPRoW5bmn+wDJ1T4ngkTPxAQAAEQAAEQAAEQ8DQCQyLwRp4IusGB0IyWL8pa6KmtxSO6vWZJGs1PjhiR72yGWMIqir+4YYHmnG0X9UEABDyLAHwAetbznDSzgQ+CSfOovHKgvFvMSsBuYTJihBQ1dNJv1x+QgpD4+/rQ786fpZt5CM/L3+RDcxLDyE/0BQEBEAABEAABEAABTyJQWN9BDR1HN1iNmhufOLz3nVxpXcd9f/ek6cL9y1TNh8EmxRnC3x82dTVH61EN4v3box6nw5PBW5/D6FARBEDAUwmwQiw7wSICZRjzJzJNnPa76hQ5yna/UEI++EG+ricR+0UU4pLGLk99jJgXCIAACIAACICAlxKoaO52ifKvrKmLHtiQN0L5d67w+6yH8i84wJdmi+BuUP556S86pg0CEyRgzNvtBAeF4iAAAiDgagIBJl+amRCqnJIzYizLsmJpRXas1FV9Rx/9dXMhDenotbqps5/qO3qlfpEAARAAARAAARAAgclKoLGzT/F1bPT469t76b73Do2wIFmaGUOXLZyu+XAsgSaaNTVMbFj7at42GgQBEPBMAlAAeuZzxaxAAAQ0IMALqpniJCCbVhgh31s8g9JjQ6Suvqxopdd2V0p5WidKG7uFfxxjzJ21HjvaAwEQAAEQAAEQAAEbgfbeASqq77QlDfvkAHJ/fPcQtXbLgeTY39/Vp6Xq4k89KcJM7E8aAgIgAALjJQAF4HhJoRwIgIBXEjD7m4Q5cKghCyw2Pf6JCArCwTnU8vruKtpW0KDO0vR6UDjJLhSL5a91PGmo6YDRGAiAAAiAAAiAAAgMI8Cbmfki6IdY1hgq3f1Wul+c/KsVJwDVkiX88t28PEOXNaTZ35fCzPJ6Ud03rkEABEBgNAJQAI5GBXkgAAIgoCIQEmBSogMbsckaFRKgKAGH9/XEliL6vKxZNSptLzt6rS4xl9F2FmgNBEAABEAABEDAGwlwALfcmvYRvvf0ZsH9ss/m4T6VkyLNdMcZWbq5kkkIC9R7amgfBEDAAwlAAeiBDxVTAgEQ0J4AO1fOFDu5UwywtGDfg1csSpYmwbvZj24qoAPVbVK+lomq1h5i0xkICIAACIAACIAACEwWAmzJcKimQ7gzGTJ0yEOiX/bVfKC6Xeo3Rmzm3nNmNgWLDWQ9xN80haJFHxAQAAEQmCgBKAAnSgzlQQAEvJZARLA/ccReI5SAa2bF01nHJUisBwa/pj+9n0dFDfr4tmELYDYF5oU0BARAAARAAARAAATcnQC7L8mv66DOPquhQ+V+//5xCe0ska0zQkVgjnvOyqYIs79u44m1BJLPcFMR3XpDwyAAAp5EAApAT3qamAsIgIDuBGIsAZQSHax7P1OElvGKk6bT6SJynFp4d/s+4WS6sqVbna3ZdZ9of7gZi2aNoyEQAAEQAAEQAAEQ0JAAb4oOD7yhYfPHbOpVEaBt06F66X6QCB5395kzKSEsSMrXMsF6v7hQmP9qyRRtgYA3EYAC0JueNuYKAiCgCQFeeCVF6re4sw2SlYAcOW5hSqQtS/nkXW6ONNfQITublgo5kWjo6KOmzj4nWkBVEAABEAABEAABENCXQGljl1gL9evbySitbzhQSxygTS0moZn76apM3TeJo8VGtL8Jr/Bq9rgGARAYPwH89bDDKiUlhdLS0qiwsNBOKflWeXk5paamKvXkO0iBAAh4EoHECDNNC9dfCcgmHjcuS6c508IkfM1d/fR/7+SKXW99Fr7FYlHdZx2U+kQCBEAABEAABEAABNyBAPstrmnTZyPU3vw+KWqkF3aUSkXYPfSNy9Np9rC1mlRIowSCf2gEEs2AgJcSgALQzoMvKyuj0tJS6u8f/wv2wMCAUofrQUAABDybwPQoM8WG6u+E2c/Xh24Vu8oZsSES0Lr2PrpXnATUw++NVfgbZH+A7OMGAgIgAAIgAAIgAALuQqBeWECUN+njCsXeHPdWttJfPyqi4SujH5yaQielRNmrqsm9cLMfmf31CSyiyQDRCAiAgNsTgALQ7R8RBggCIODOBFKFP0CLcPistwQKvzJ3rskWpsdmqauK5m76f+8dEpHvtD+t195jdcnuujRBJEAABEAABEAABEDgGwItwgKiuKHLcB7sa/DBD/JHBEr79vxEWjkzzpDx4PSfIZjRCQh4NAEoADV+vG1tbUqLZrP8kq5xN2gOBEDATQiwn76seAsF+On/5zQkwEQ/OzNbOH+WTx0WiJN6D4lF6cDgkOZUWMHYZXBkPc0ngQZBAARAAARAAAQmPYGO3gHiNY/RxgkceI0DsPVZ5XXW6pw4unDuNEO4mv19KVzHyMKGTAKdgAAIuJyA/m+sLp+isQN46aWXlA6Tk5ON7Ri9gQAIuIwAm+hmxVnIl0Oz6Sy8+Pv5WTMpQpiBqGVvVRv9dXMhDQ0NN0xRl5r4NTfHi22t2534SFADBEAABEAABEDAWwn09A9SXm3HiBN4evOoEb4G/+/t3BHuVk5Oi6LvLZ5BvBFshOD0nxGU0QcIeD4B/e3WJhHD5cuXjzraq666ioKDg0e9Z8vs6+uj4uJiqq+vV74IVq9ebbuFTxAAAS8gECxO56XFBFN+Xafus42xBNLPhBLwt+sPSgvSnSXN9Mz2YiVysJYLUl50l4mTgCnC3BkCAiAAAiAAAiAAAkYS4KBkB2vahaWDtpucY82hvr2X/sAB13oGpKIcmO36pWnkY5Dyz893CkWHyNYf0oCQAAEQAIFxEoACUAXqo48+UpR3aqf3fL1r1y5VqbEvOQrwPffcM3ZBlAABEPAoAlFicZYolGWVLT26z4ujEN8tzIH/8PZB4f/vqEnK5rwGxUH0d0+arumudK2ItBce5EcRwf66zw0dgAAIgAAIgAAIgAATsAr3JodqOqh/mPmt3nSaOvvEGiuXmoXPQbVkxoUogdlMwvrDKIkLDSQfA6xMjJoP+gEBEHAdASgAVeyXLFkivTBv2bJFSc+fP9/uCUA+aRMYGEgJCQm0ePFiuvTSS+2WV3WJSxAAAQ8jwEE6ekRAjqZOecGoxzTTYkLo9tVZdL8IAqLeFX97Xw2xqcgKjZ1SFzd20pzAcGKTZwgIgAAIgAAIgAAI6EmA3Y8cEma/3WJz1Uhp6e5XlH8NQgmoFg78dpcIyMaB2YwS1vuxAhACAiAAAloQgAJQRZFPAKrFx+fwS+7zzz9POTk56lu4BgEQAIFjEmDFXO9Amwieof+CddbUMLp5RYYSBETt/u+FT0opVYxDS7PdfuvXVNLYRZnC3yEEBEAABEAABEAABPQkUCzWHB29Vj27GNF2uzD3ZZ9/tcL8Vy3TxQbvPWfOVKws1Pl6X7N1ib8JG696c0b7IOAtBPDXxM6TXrt2LfFPRESEnVK4BQIgAAIyAQ4Gwkoyf5MxjqFPTI6ka4UvGrXwicCHN+ZLPgLV9x295pONLcPMYRxtC/VAAARAAARAAARAYDQCreIUXkOHfAJvtHJa5nUKZeO9wudflQj8oZZp4UFKALaQQOPPziD4h/pJ4BoEQMBZAlAA2iHIJ/+ee+45xbTXTjHcAgEQAIERBNg8JEMoAY1y2XJaRgydNTteGke9WDg//lERDQlfplpKSVMXogJrCRRtgQAIgAAIgAAIHCEwKEwa+PSfkdLdb6U/vpurBD1T9xsvzG9/fvZMChV+kI2WMNEnB5mDgAAIgIBWBKAA1Iok2gEBEACBYQRCA/00NcEd1vyI5GUi8Ac7p1bL7vIWeuuranWW09d9IuiIEYFOnB4oGgABEAABEAABEJh0BCqau4nXGkZJr/DdzP6UhysdY4T57S+E8i/C7JoAaDj9Z9RvAPoBAe8hAAWgnWe9b98+4oi+GRkZVFVVZafk4VtcJj09ndLS0ig/P3/M8igAAiDg+QRixc6xUQs4k/BbesuKTAodZqLy788r6GB1m6awq9t6hFNuY/3yaDoBNAYCIAACIAACIOB2BDr7rCP87+k5yD7rID2wIY/y6zqlbiKD/RXlH/vgc4UE+ftShBgDBARAAAS0JAAFoB2aL730EpWWlipKvWnTptkpefgWl8nMzFTqcF0ICIAACDCB5CgzsRmHEcIL1huXZ5Da+yBbAD/6YSFxVDuthNssbjDWPEersaMdEAABEAABEAAB9yPwtVhcFDd0ksaeS4450YHBIXrw/Xw6WNMuleE12y/Omkm8iesqMWrz2FXzQ78gAAKuIQAFoB3uW7ZsoSlTptB5551np5R86/zzzxdfWl/Tpk2b5BtIgQAIeC0B/jvCprmBfsb8yT1uWhhdPD9R4t0moto9uqmA2K+OVsKR+eqHRcnTqm20AwIgAAIgAAIg4F0EOPhGV9+gIZO2CuXfwxsLaG+VbCFhEVYUPxfKvwQR+MNV4uc7hdj8GAICIAACWhMw5m1U61Eb1J7NjHfOnDnj7nH27NlK2by8vHHXQUEQAAHPJ2Dy9aHs+FAyiUWdEXLB3Gl0QlK41NWh2g76z65yKc/ZRLnw08M76BAQAAEQAAEQAAEQcJRAT/8gVbXI0XcdbWuserwZ+tjmQmI/yWoJFma3PxPKv6RIszrb8Os4cfLQx6gocobPDh2CAAi4kgAUgHbod3Ye9gUREiI71bdThWxl29vlo+T26uAeCICAdxBgfy4ZsSHiZLH+8/URnVx/ehpFh8j+Y9bvraHPS5s1G8DA4NdU1tStWXtoCARAAARAAARAwPsIFDd2koZGCscEOCQ6efyjQtpZIq+Fgvx86e4zZ9KMqOBj1jXiBuv9WAEIAQEQAAE9CEABaIdqRESEcre2ttZOKfmWrazFYpFvIAUCIAACgkC4iCSXGm3M4tIiohBzUBDfYbvIj28pojoNTXcbOvqITYwhIAACIAACIAACIDBRArwmae/RP7DYkHDT9OTWIvq4qEkaYoDJh+5ck0XpYpPW1RIlNm79xXggIAACIKAHAfx1sUOVo/+yvPfee3ZKybfeffddJYMjAUNAAARAYDQC7FR6mkG+ZXgxu/bkZGkY3cLM5qGN+dRv1c50t7SxS/F/KnWEBAiAAAiAAAiAAAjYIcBReNmdiN7Cyr9nthXT1oJGqSv2t3f76izFTYt0w0WJhDDX+R500ZTRLQiAgIEEoAC0A/uMM85QXmifeuopys3NtVPy8K0DBw7Q008/rQQOWbNmzZjlUQAEQMB7CUwXkYGHm+fqRWPVzDhanBYlNc9mu8/vKJXynEmwUrG6rdeZJlAXBEAABEAABEDAywiUNnaTVbgT0VM4QONzH5fQ5rwGqRuTsJC4bVUWzRbB09xBIoL9KDjA5A5DwRhAAAQ8lAAUgHYe7HXXXUfBwcHU29tLy5cvp7feeuuYpd98801auXIl9fT0UFBQEN1www3HLIsbIAACIMAE0mJCiKPN6S0chfjq01JHnDrcnFdPW/LrNeu+Uuzg9w4YE71Ps0GjIRAAARAAARAAAZcQaOrso+aufl37ZuXfC5+U0cZceb3D7lF+uiqTjh8WME3XwdhpnJV/mbFwIWUHEW6BAAhoQED/N08NBumqJqKjo+mJJ56gK6+8kurr6+n888+n1NRUOvXUUykhIUEZVk1NDW3bto1KSkqU04L8ov34449TXFycq4aNfkEABCYJAY7wlhVvof1VbUJxpp057mjTDxTOrW9dmUk//98+6lOZ/j67vURxeJ2sgdNrdt5dIkyBZyaEjjYE5IEACIAACIAACICAQsA6OESlTV260mDl30s7y2nDAdmfu694X/vJigyaO/2wv3ddBzGOxmNDAxT/0PweCQEBEAABPQlAATgG3e9+97s0NDREfBqwu7ubioqKqLi4WKrFXy4sfFqQlX9XXHGFdB8JEAABEDgWAT9fH8XvzIHqNuKIunrKtIgg5STgY5sLj3TDfT68sYD+78LZZPZ3/iuhtXuAeEc/KiTgSB+4AAEQAAEQAAEQAAE1gTJhNdBv1W/dw+9n/95VQe/sq1F3SxwX7aYV6XTijEgp31UJ9gnNbmEgIAACIGAEAZgAj4MynwAsLCyku+++m4477jilBn+p8A/v1MyZM4d+/vOfK2Wg/BsHUBQBARCQCAT5+1JGnEVZlEo3dEickh5Nq3PkE8q1IvreU1uLNQviUSr8Cw7ycUAICIAACIAACIAACAwj0CY2C+vb+4blapt8dXclvflVtdQoH7C7YVk6nZQi+0WWChmYmBFthvLPQN7oCgRAgGiKUGLhLW2CvwlWq5Wam5uVWpGRkWQyOX9qZoJDmPTFKysrKSkpSZlHRUUFJSYmTvo5YQIg4CyBho4+KqzvdLaZMesPCLOb364/QEUNsunNVYtn0OpZ8WPWH0+BhLBAmhEdPJ6iKAMCIAACIAACIOAlBIbEBuFXla26uj55XSj/XvmiUiLKxrXXnZ5Gp2XESPmuSLAikv1Ax1hgLeEK/t7aJ96/vfXJy/PGCUCZx7hSrPCLjY1VfqD8GxcyFAIBEBgHAV4IJgozXb2FzY5vWZEpIs35Sl29+GkZFTdoo4DkU4WdfVapfSRAAARAAARAAAS8m0BFCwcM08/v8ZtfVo1Q/jHxa5akuoXyj4OPZAv/z1D+eff/A8weBFxFAApAV5FHvyAAAiAwCoGkSLMhi0JeeF63NF0agVXsyj+yqYC6NFDc8dnyEnHCEIfMJcRIgAAIgAAIgIDXEuD1RU1br27zZ39/Lwu/f8PlR6em0OlZscOzDU/7+U4RgdIsFG72N7xvdAgCIAACTAAKQPwegAAIgICbEUiLCabQIP1dC8xPjqBz5hyOaG5DUC/MkJ/cWqSJ4o5PANbp7OPHNm58ggAIgAAIgAAIuC+BPusg5dV1iPWFPmPkSL9syTBc2L3Jipmy7+PhZYxIB/j50KypYWQJ9DOiO/QBAiAAAqMS0P8Nc9RuJ1/m5s2b6X//+x999dVX1NjYSD09PXZfkDk4CEcMhoAACIDARAnw348sERRkf3U79fQPTrT6hMpfsiCJ8sWCPL/uqOnvrtIW4oX0mtmycnBCDX9TuFxE+Qs3+1Ggn2xu7EhbqAMCIAACIAACIDD5CLDv4UM1HdSnk+kvn/wbTfl35aJkzXwbO0PdLIK9ZYuTfwEmrIWc4Yi6IAACzhOAAnAMhvX19XTppZfSli1blJLHMmfjF3b1PU5DQAAEQMBRAibhp499xByobqN+q07b5WJwJh8funl5Bt39+j7JZ99LO8spPdYifkIcnYJSj6MBlzR2CZOXUKfaQWUQAAEQAAEQAIHJR4DXAXm1HdStw4Ymv3u9sWd0n3+XL5xOZx3n/Eams8QtgSZlPcfrOggIgAAIuJoA/hLZeQIDAwN05plnKso//oI5/vjj6eyzz1ZqsILvyiuvVNIJCQmK8o/z5s+fT9/73vdo7dq1dlrGLRAAARAYmwCfmsuODyWT8Bmjp0SFBNANy9KkLnjB/simfEkpKBWYQKK1e4A4wjEEBEAABEAABEDAewjw+xNbGXT0ah8UjNt++bPyUQN+fOfEJDr3+KkuB80WEDliAxTKP5c/CgwABEDgGwJQANr5VXj++edpz549SonnnnuOdu/eTffdd9+RGi+88AKtX7+eqqqq6PXXXydWBB48eJDOOecc4vIQEAABEHCWQHCASTk9p7cS8ISkCDpv2GK5sbOfntyijT/AsqYucZJRv6h/znJGfRAAARAAARAAAe0IsIKusL6TeBNQaxkSbT+3o5TW760Z0fRl4uTfhXOnjcg3OiMi2E9x5+Ijov5CQAAEQMBdCEABaOdJvPbaa8rdNWvWKKf67BSlCy64QDkp6O/vT9///vepoKDAXnHcAwEQAIFxEwgxSAnIO+bse1Atn5e10Lv7a9VZDl0PDH5NrASEgAAIgAAIgAAIeD6B0qZu4o1ErYUtFHhz8oODdSOavuqUGSM2M0cUMiCDlX+Zwo0KlH8GwEYXIAACEyIABaAdXBzwg816r7jiilFL8c6WWtLS0uiWW26hrq4ueuSRR9S3cA0CIAACThFgJSD7BNTzJKCv2KW+eUWGiFAnu4f9l/AHWCBMeJwVfhFo7tL+ZcDZcaE+CIAACIAACICAdgQqRACw2rZe7Rr8piWrCCbylw8LaGtBo9Q2u16/dmkarc6Jl/JdkYDyzxXU0ScIgMB4CUABaIdUc3OzcjclJeVIKT7hZ5Pu7m7b5ZHPFStWKNcffPDBkTxcgAAIgIAWBCyBfooSkBV1eklksD/dcHo6qXsYFJsdj4oFd6cGPnxKGjuJF/AQEAABEAABEAABzyPAir/Klh7NJ8ZuRB78IJ92lhx+P7N14Cu0fxzMbGlmjC3LZZ9Q/rkMPToGARAYJwEoAO2Asin7bJ9cNDT0aCRL9v03XAIDA5Ws0e4NL4s0CIAACEyUgKIETLCQnkrA45PC6fwTZP85fHrv8S2FxH53nBGOaFwmTgZAQAAEQAAEQAAEPIsAB/wqadTe3UfvwCD9vw2HaE9FqwTMTwRJ++nqTFqUGiXluyIB5Z8rqKNPEACBiRKAAtAOsenTpyt36+qO+piIi4sji+Wwj6ydO3eOqL1//34lj02HISAAAiCgB4FQPgmosxLw4vmJIviI7A9wd3krvbNvpMPtic6xvr2P2nRwCj7RcaA8CIAACIAACICANgRau/upqKFTm8ZUrXT1Wened3LpQHW7KpcowORDd56RTfOmR0j5rkhA+ecK6ugTBEDAEQJQANqhNm/ePOWuLRKwreiSJUuI/f+xn7++vj5bNrW2ttL999+v+A3Myck5ko8LEAABENCaACsBs4RPQL1OAnK7Ny7LoNBh/gBf/qyc8jXwB1gkTIHZkTcEBEAABEAABEBgchNo7x0Qa4NO8X6k7TzaewboD28fpAIRTVgtZn9f+tlZM2n2tDB1tkuuofxzCXZ0CgIg4CABKADtgGN/fqzoe/vtt6VS1157rZJmxeCcOXPojjvuoOuvv56OO+44ys/PV+6tXbtWqoMECIAACGhNICxIKAFF1F69XAKyP8AbhV8d9Xlm1tk9sqmAeLHvjPQNDBE7CYeAAAiAAAiAAAhMXgLd/VbKq+3QfFOPg4b97q2DxNGE1cJB0X5xdg5livWPqwXKP1c/AfQPAiAwUQJQANohdsEFFxCbAVdWVlJRUdGRkmeffTb94Ac/UJSDBQUF9OCDD9KTTz5JNr9/q1evpuuuu+5IeVyAAAiAgF4EwswcGCRUNyXgcWJ3/cJ5sj9AXpQ/uaVI+RvozLxq23upw0lFojP9oy4IgAAIgAAIgIDjBNg3X25Nuwjupe3RPz7597u3DlBVqxxMJFyseX59bg6lRAc7PmiNakL5pxFINAMCIGAoASgA7eAODw+n0tJSKisro7S0NKnkM888Q08//TSddNJJFBwcTAEBAcoJwAceeIDWr19PPj5AKwFDAgRAQDcCrARkc2C9TgJeNDeRZk09GgCJJ8L+AN87UOvUnNhUqLihi4ZgCuwUR1QGARAAARAAAaMJsBsPPvnHwb20FLa+evbjEqoT/oLVEh3iT785dxYlRpjV2S65hvLPJdjRKQiAgAYEpog/str+1dZgUGjC8wnwqcqkpCRlohUVFZSYmOj5k8YMQUBnAuyAmxfjeujTuO27Xt9HvCtvE/YT+LvzZlFqTIgty6HPxIggSop0/YLeocGjEgiAAAiAAAh4IYEC4Q+4sbNf85l/UtRIj35YKLUbHxoozH5nUlRIgJTvigSUf66gjj61IID3by0oTv42cExt8j9DzAAEQAAEFALhZn9Kj3VOGXcslNz2DafLJ6F59/8vYpHe0z94rGrjymcTH/YhBAEBEAABEAABEHB/AjVtPboo/3iz8e8fl0oA2OffL8/JgfJPooIECIAACDhGAApAx7ihFgiAAAi4JQHeHecTdXrInMRwOndOgtQ0+/FjUx1nDpPzOfSi+i6n2pAGhQQIgAAIgAAIgIAuBDgIWNmwwBxadMTriL+L9URnn7wh+INTUoiDkrlaYiwBhwOv6eVvxdUTRP8gAAJeQcDkFbPUYJKDg4O0bt062rhxI+3bt4+am5uVViMjI2n27Nm0cuVKOv/888lkAlINcKMJEAABJwiwOW2PcMzdpINpzncWJFGuMDMurO88MsKPCxuJg4UszYw5kjfRC17w17T10tRwfZSXEx0PyoMACIAACIAACMgE+q1DxKa/ejiQ2lHURLtKW6QOT0qJpJPToqQ8VySmhgdScpTrA4+4Yu7oEwRAwLMIQFs1juf55ptv0o033ngkyi9XsZ12mTJlCu3YsYOeeuopSkhIoMcee4w4ejAEBEAABFxJIE345esdaKOuPufMc4fPwSQCHN20LJ3uFv4AWclok+fErn2GMD92RoFX0dyt7PIH+vnamsUnCIAACIAACICAGxDgd598ofzTOugHT61FmP4+t6NEmmVooIn49J+rJTnK7NTaxtXjR/8gAAIgoCYAE2A1jVGuH3nkEbrwwgsV5Z9N6TdjxgxatGiR8sPXLHyvurqaLrroInr44YeVPPwDAiAAAq4iwAE6MuMs5Oc7RfMhxApn3NcsSZXa7ROnAh79sEC8GAxJ+RNJcPCSooajJwsnUhdlQQAEQAAEQAAE9CPAZr8dvbJ5rha98TvUs9tLRmxYsvIvNMhPiy4cakOc8VD8KjuzselQx6gEAiAAAjoSgALQDtydO3fSbbfdpij3LBYL3X///VRXV0dFRUXKqT8++cfXnMf3wsLClLJ33HEHcV0ICIAACLiSAJ+kyxBKQF7Eai2LUqNoRXas1Cy/HPzrs3Ipb6KJ9h4r1Qu/ghAQAAEQAAEQAAH3INDU2ae46dBjNNuFG5EvymTT35PFGuMk8eMq4U3U7HgLsd8/CAiAAAh4EgEoAO08zQcffJCGhoYUxR4r+1ixFx0dPaIG5/E9LsNKQK7DdSEgAAIg4GoCYWL3PCVaH781V56cPCLgyIYDtfR56WEfqY7OvUyYAjtzktDRflEPBEAABEAABEBAJtDTPyhO53fJmRqlmrv66YUdpVJrfOrv+6fMkPKMTLDlxMwEC4WbXR94xMh5oy8QAAHvIAAFoJ3nvG3bNnFyZgrdddddlJOTY6fk4VszZ85UyvJR9q1bt45ZHgVAAARAwAgCccJkNy5U+13sAJMv3bw8g/x95a+SJ7YWiQAkfQ5PzTr4NZU26fOy4fCgUBEEQAAEQAAEvIzAoPDNkSf8/vGn1nLY9LeYuoSCUS0/YtPfQNeY/gb4+dCsqWFkcVH/ag64BgEQAAE9CMhvbXr0MInbbGk5fBx92bJl456FrWxra+u466AgCIAACOhNgE8BhgZpH/eJIw6vXZwsDZ8Dj/zlw0KnXhg4gnGLOBkAAQEQAAEQAAEQcA0B9svLJwD1kK0FjbS7XH5fWiwi/i4QkX9dIWZ/X6H8C6Ug8QkBARAAAU8lAAWgnSfLUX0dFWfqOton6oEACIDAsQjwaWYOCsK721rL8qxYWpQqL9j5xMDruyud6qq4scspJaJTnaMyCIAACIAACHgxgerWHnGaX5+NODb9/ccnpRJddlny/cUzpDyjEhYRcZiVf2zZAAEBEAABTyag/ZugB9FauXKlMpstW7aMe1YfffSRUnb58uXjroOCIAACIGAEAT9hqstOrdm5tZbCysWrT0ulmBDZzPiNPVV0oLrN4a7YD2C58AcIAQEQAAEQAAEQMI5AW8+Abt+/bPr79LZi6h52svBHp6a4xPQ2MtifchJCyTTMnYlxtNETCIAACBhHAApAO6w5AnBQUBDdd999lJ+fb6fk4VtchqMBBwcHK0FBxqyAAiAAAiBgMAGzv4nSY0M075XbvXlFOvmqQg6zx6DHNhdSu3iRcFTqRETgjl7H6zvaL+qBAAiAAAiAgDcS6LMOUmF9Bwk9nS6yJb+BvqyQTX9PTY+mE2fIlgS6dD6sUfaPnBkXQj4ab4wO6wZJEAABEHAbAlAA2nkUWVlZ9OqrryolFi1aRA8//DA1N4+Mbsm+Ah955BFavHixUva///0vcV0ICIAACLgjAd7tTowI0nxo6bEWumRBktRua/cAPb6liIYcfJPgasUi+uCQDg7IpYEiAQIgAAIgAAJeToBP5xXUdVK/VR/tHwcI+8cnZRLlcGH6+72TZ0h5RiSmR5kpNSZECfhoRH/oAwRAAATcgcAU8Yden7/w7jA7J8dgM+OtqqqigoIC5QuCTd1SUlIoNjZWSdfV1VFJSYnYJTuMMT09naZNm3bMnrn+pk2bjnnfW25UVlZSUtJhRUFFRQUlJiZ6y9QxTxBwGwL5wk+f1v59WNF3/3uHaG+lbPrLfn3OmBXv8NxZYckBRyAgAAIgAAIgAAL6EChr6qLq1l5dGud3pftGWR/cvjqL5idH6NLnaI3yYb80YQkRPcxtyWhlkQcCnkQA79+e9DQdn4v2ISEdH4vb1WR/fqywswl/cfFPUVGR8mPLV38WFhYS/9gUgrZ73A7nqduz3cMnCIAACLiCQJrY+e4daCOO2quV+Ii/ddefnk53v7aXWlWmvy9/Vk7zpodTjCXQoa7YGTkv1hGdzyF8qAQCIAACIAACdgm0iRP7ein/uOPNeQ0jNgdPy4g2VPln8j0cEI0DjkBAAARAwBsJQAFo56kvWbIECjs7fHALBEBgchPgYCAcGXh/VRsNDGp3GJwX1tednkZ/fPfQEUB9IqDH09tK6J4zsx36u8oWwEUNnTR7WtiRNnEBAiAAAiAAAiDgPAHr4BAViu9YvaRCBPR66VPZ9DfC7EdrDTT9DfDzoZnxodhI1Osho10QAIFJQQAKQDuPyRbR104R3AIBEACBSU0g0M+XMoQSMLemXZxS1m4qcxLDaXl2LH14qP5Io/uEopGdf5+eFXskbyIXHb1W4qAgcaGOnSKcSF8oCwIgAAIgAALeQqBUmP72i406PYQDitz/Xh71DMjWBleflkohAca8inI/WfEW8jfB/b0ezxhtggAITB4C+Cs4eZ4VRgoCIAACuhDgE3vJwhm21vLdk6YTBxxRC58AaOnuV2dN6LpcnCLgCIUQEAABEAABEAAB5wlwYI6GDse/l+2NgC0M/vB2LnX2WaViSzNjaO50Y/z+8TokZ2oolH/SE0ACBEDAWwlAAeitTx7zBgEQAAEVgYSwIOGfL0CV4/yl2d9EPzwlRWqoq3+Q/r79aOAk6eY4ElZhqlza2D2OkigCAiAAAiAAAiBgjwBvqJU0dtkr4vC9XSXNSlAwdgGilgwRgMOoqL8JYYHC1UkIscsTCAiAAAiAABEUgPgtAAEQAAEQUAikRgeTJVBbc5x5IrLfKenREuHPy1ro0+ImKW8iieaufhG9uG8iVVAWBEAABEAABEBgGIHihi5NfQDbmt+SX08PbconKzvwVcmcxDD62VkzdffDxzEcZ0SbxU+wQ36HVUPGJQiAAAh4FAFt3/Q8Co08maGhITp48CAVFxdTR0cHDQ6ObYK2du1auRGkQAAEQMCNCfiIHfIMsVPOJjv9VnnR7syw156cTPsqW6ld+PCzyfM7SmmWCOgRGuhYJD72V8SmyyZf7GPZmOITBEAABEAABMZLoLatl1pF5F+t5Z19NfTisIAf3MdJKZF047J03b+3+bAf+zYe7oJE63miPRAAARCYjASgABzjqXV3d9Mf/vAHeuaZZ6ipafwnVqaIrScoAMeAi9sgAAJuRyDAdDgoyMFq7YKCsJLv+4tT6NEPC47Ml5WB/xBKwBuXZxzJm8gFKyjZBY49/gAAQABJREFUH2BqTMhEqqEsCIAACIAACHg9gR7hjoO/Q7WUr0UksVe/qKTX91SNaJaDgrFLEN5o1FP8fKcowT4sDm4u6jk2tA0CIAAC7kAACkA7T6Gzs5OWLVtGu3fvFtExtTsNY6dL3AIBEAABlxNghV2KMJth0yCtZFFqJO0oiiA2/7XJx0VNtDgtmthM2BGpa++jaOG30NFThI70iTogAAIgAAIgMJkJ8DtNUUMnDQ4zz3VmTkOizRfEpt77B+tGNHPe8VPp0gVJupviBgf4Cn9/Fgr08x0xBmSAAAiAAAgcJgAFoJ3fBD7598UXXyglFi1aRNdccw0df/zxFB4eLnawYHZmBx1ugQAITHICcaGB1CWi9rGSTQvhU9E/ODWFcmvaiQOB2OTZj0soO8FCHDDEESmo66RZIrofFvyO0EMdEAABEAABbyNQ2dJDHSqXHM7O3yrcJD2xpZg+Lmwc0dRlQvF33gnTRuRrnRFj8afU6BDdTxhqPW60BwIgAAJGE3DsjcvoUbqov1dffVXZrTrrrLNo3bp1UPq56DmgWxAAAdcQmBEVTN1CWafVi0KE2Z+uWJRMT24tPjIhDujxz53ldPVpqUfyJnLRL6ILHhRKRVYCsvkyBARAAARAAARAYHQCnWJjr6q1Z/SbDuTyd/DDG/NpT0WrVJsNfX94WgqtyI6T8rVOcLCP6ZFmmhoepHXTaA8EQAAEPJIAjrHZeaxVVYd9WNx8881Q/tnhhFsgAAKeSYB99bA5jb9Ju6+KpZkxNEcE/1DLh4fqlcAj6ryJXPcNCCWg8FnILyIQEAABEAABEACBkQSGhMlvYX2ncGs08p4jOd39Vvrju7kjlH++Yu1wk/Dvq7fyj/39zYwPhfLPkYeHOiAAAl5LQLu3Og9EGBsbq8wqOjraA2eHKYEACIDA2ARY+ZcpIgNr5bebTYF/JE4FBAxTKj69rZh6B46aBo89MrlEr1ACsnnxwCCUgDIZpEAABEAABECAqEwE/eDgH1oInyT8w9u5dKi2Q2rO39eHbl+dRSenRUn5WifY399ssZkYZvbTumm0BwIgAAIeTQAKQDuPd+HChcrdvLw8O6VwCwRAAAQ8mwBH00uJCdZskjGWQLps4XSpvfqOPvrP5xVS3kQTbK58qKaDrFACThQdyoMACIAACHgwgbbuAapt69Vkhnza/s/v51FJoxwozOzvSz87ayadkBSuST/HaiQ6xF+4/QiD799jAUI+CIAACNghAAWgHTi33nqrcvexxx5DFGA7nHALBEDA8wnECqVdfFigZhNdlRNHWcK8WC0b9tdSfp18mkB9fzzXfCqBTyRoGd1wPP2iDAiAAAiAAAi4IwHeFCsUUX+1EDYj/ttHhSNO/oUF+dGvzsmhrHj5e12LPm1tsL+/5CgzZYi1A5sZQ0AABEAABCZOAApAO8wWL15M999/P+3YsYMuvfRSam2VHdzaqYpbIAACIOBxBGaIhXdokDaxo3zESv7HS1KJffjYhN0SPbm1yGlffhy0JE8oAflFBQICIAACIAAC3kyAT+pp4SP3a+E88B+fltHOkmYJZ7gww/31uTlCOaedpYDUgUiYxFohWygXEexjOBmkQQAEQGBiBLR5k5tYn5Oq9O23305paWl09dVXU1JSEq1atYoyMzPJbDaPOY9f/epXY5ZBARAAARCYLATYfx+f2jsgAm6wua2zkiCi9l08P4le/qz8SFPVrb30xp5KumSBbCJ8pMA4L9p6Bii/vkMZL48bAgIgAAIgAALeRqCxs48aO/s1mfb6vTW04UCt1FaQny/dvSabEsL0i8LLpsV8sjBQ9AUBARAAARBwjgAUgGPwq6+vpzfeeIPa2trEaZIhWrdu3Rg1jt6GAvAoC1yBAAh4BgGTcPCdnXBYCcjRd52Vs49LoE+LmyRfQm9+VU0LU6IoJdq50wQtXQNUICIeZsSGEJSAzj4p1AcBEAABEJhMBNglRnGD7KfP0fFvK2iQNuu4HTbD/emqTF1P/kUE+4nvcJj8OvrcUA8EQAAEhhOACfBwIqp0U1MTLVmyhP75z3/S4OCg4geQj7+P90fVFC5BAARAwGMIBJh8aWZ8qGS+6+jk+AWCTYF9Vaf02HL3ceFjSIuIvk3i5EORRi9Ajs4R9UAABEAABEDASAK9A4PCFUa7Jv5w91a20pNbikcM/7qlaUok3hE3NMrwN/lQekwI/P1pxBPNgAAIgAATgALQzu/BvffeS/n5+YrC7+KLL6YPP/yQWCnIykA+DTjWj52mcQsEQAAEJjWBoG9McrRwxM1+g86fO1XiUdHSQ69+USnlOZpoEBGGizVygO7oGFAPBEAABEAABIwgwEE/2A/u/2fvPsDjKq/E/x+kUe+SJUuWJUu2ZRvbmGrTmwk1kJCQQjaFQBJS2IRf+mbZhGRTd5ckbEj4hwRI2VQIIbspVNMMAUwzxmDjIhfJVrN6l0bi/55xJN87mnJn5kqaGX3f5/HjW957597PxTPMO+c9Z8Qbex5czR/4/Yd3yJgJgLC2955cLacvnWfd5Oqy/ia41ETv66wDGgIIIICAewK8q4aw/L//+z/ftLH3v//9ctddd8k555wjRUVFTCULYcYuBBCYOwJ5mTo1R6fXxn7PbzuuUqqL7blV/7zlYMxVgSeurKVnWPa1uzMVauKc/I0AAggggEA8CegspR0tfa7k6W3tGZL/vH+7DPml+7h4dblo+o7pbBUFmaKVhWkIIIAAAu4KMAAYwvPAgQO+vddcc02IXuxCAAEE5q5AUU66LC6NLVef6umv/J84Z4ltqo8GHPx/j+2WYW/sBUf0NbTASGPngC7SEEAAAQQQSDoBTXmhRbBibT1Do/IdM/jX5XeuUxYXy/tOWTStwRA5GalSVWT/QTDW++F4BBBAAIHDAgwAhvgvYd68w6HteXl5IXqxCwEEEJjbAmV5mVJdEvv/rOtU4HecsNCG2WwiEH63qcG2LZaVho5B6RpwpyJiLNfBsQgggAACCLgp0NAxIJryItamP7rd9MDr0tQ9ZDvVyop880PdUklxI+zfduYjKyYtsG/qb4ou0BBAAAEEXBdgADAE6Zlnnunbu3Xr1hC92IUAAgggUFmYJTplJ9Z22bELZIlfROH9rzbLqwe7Yz315PG7TGVgt6IKJ0/KAgIIIIAAArMkoAN/jSZ3bqxtzFThuuWRXbLTfE5aW1VRlq/ib9o05+TTHxOz0z3Wl2YZAQQQQMBFAQYAQ2B+9rOflbS0NLnppptkaMj+K1iIw9iFAAIIzEmBmnk5Mi83PaZ716IiHzcRBmmp9l//f/z4bhkccWcq8OjYG7LT5EjSXEk0BBBAAAEEEllAp/y6UehKPxN/9tQeeWFfp42j2KT6+OJFKyQnY3oH5jTnX0VBlu21WUEAAQQQcFeAAcAQnieccILcfvvtvkrAF1xwge/vEN3ZhQACCMx5gSWluTEn7tZowivXVtssD/WNyK+e3WfbFstK75BX9pvpUjQEEEAAAQQSVUB/GNvR0ismcC/mdu9LB2TD9lbbeXLSU+VfzOBfSW6GbbvbKx7zo9+SstjzCbt9XZwPAQQQSDaB6f0pJ8G1Jop/rFy5Up588knRv9esWSPLli2T7OzQ+a6OMvkx7rjjjgQX4PIRQACByAQ0b8/y8jx57WCP9A17IzvY0vsiU2Xw+X0dsq2pd3LrI+aLydqaIjmuqmhyWywLWhREKxlrdAMNAQQQQACBRBIY8Y7LtuYe8Zqo9ljbM/XtcvcLjbbTaCT+5y5YLlXFob/z2A6KcqXWzCDI8KRGeTSHIYAAAgg4FTjKhHvH/qnh9NUSrF9KSoqtypVS6cBeuDbRb2zMnelq4V4vEfc3NjZKVVWV79IbGhpk4UJ74v9EvCeuGQEEjgjoFxPN2zc0On5kY4RLraYAyBfu2WLy9R05R2F2mvzXFcdKbqY7v19p1MExlQWSmcYXjwgfD90RQAABBGZJYNyE/L3W1CMazR5r08/af/njKzI4euR7i37buf68Ojl5cUmspw97fGleuin8QcHFsFB0QCBGAb5/xwiYJIe78w0qSTD8b6O6utrRgJ//cawjgAACc10g3ZMiR5uKgToIOOKN7nemsvxMef8pi+T2J/dMcnYNjMrPn94r/3zu0sltsSxo5ITmA1y1IF+oOhiLJMcigAACCMyEgAYa7Grrc2Xwzzs2Lj94ZKdt8E/v4QOnLpqRwb+MtBSpKWHq70z8d8NrIIAAAirAAGCI/w727t0bYi+7EEAAAQRCCWhUnf6qr9OBo23rV5TJpr0dsqXxSBXgp3Yd8k0FPrnWncgEnaq8z+QD1ClINAQQQAABBOJZQPPXtpu8uG603z/fILvb+m2nOmPpPLlwVblt23Ss6KQqzRvsmebKwtNx7ZwTAQQQSFQBioAk6pNz+br3798vN954o5x00klSWloqmZmZvim6Z555pnzlK1+RrVu3uvyKnA4BBOaCgFb1m58fffJwTbvw0bOWiCYit7Y7TFSgVj50qzV3D8mhvmG3Tsd5EEAAAQQQcF2gxUzX1fy1brTNDZ3yly1NtlOVm8j7a06vnZEZUBUFmTEXDbNdPCsIIIAAAmEFiAAMS5T8HW655Rb50pe+JP399l8ANU+A/tECKD09PXLzzTcnPwZ3iAACrgssMtN7usxg3XCU+QC1SMdVp9XIrY/tnrw2zXt0+8Z6+cz5y1z7olJvoiByMzzkA5xUZgEBBBBAIF4EeoZGZc8h+/+rR3ttHf0jts9UPY/HFPH6lMn7l+X3g1u0rxHquJyMVKkqmv7iIqGugX0IIIDAXBRgAHAuPnXLPX/jG9+QL3/5y74tWt34Ix/5iKxdu1YKCgqkvb1dXnrpJbn33ntNbiyCRS1sLCKAQAQCqeZLxWIzvdZa0TeCw31ddUrSc2Yq8HN7OycPfX5fpzxppgOfWVc6uS2WhTGTVH1HS6+sXlBAPsBYIDkWAQQQQMBVAS2stdN8PrlRulELiPzo0V1Tcgi+9+TqGUmFYf6XwKQHyeVz1tX/QjgZAggg4EyAAUBnTknZa8OGDZODfx/4wAfk9ttvl7S0NNu9nnfeefK5z31ORkbcyTViOzkrCCAwZwQKs9OlNC9D2nqjm2arU4E/dMZi2d78su1Ly8//vldWmmIjJbnRTzO2PoT+4THZ097vy0tk3c4yAggggAACsyGgRT/0x6loC2r5X/OfNh/wVRC2bj9xUdGM5P3T16wuyZbsdL6CWv1ZRgABBGZKgLAuI52amur74/HYP4wmtkfzt/+5ZuqBOn2d8fFx+fjHP+7rfuyxx8odd9wxZfDPeq709HTrKssIIIBAxAI15n/6tTpwtE3zCX7YDAJa28DImPzETAXWL0hutdae4agHKt26Bs6DAAIIIICACuxrH7D98BWLyvamHvnDi422U2iajY+etdi1dBq2k/utFGanSUVBlt9WVhFAAAEEZkog+m9iM3WFM/A6+sVx4o/15Sa2Rfu39Vzxtvzggw/Kzp07fZf1xS9+UeJ9wDLe/LgeBBCIXEAr/elU4FjautpiOd1MB7Y2rRD8m037XR0E1DxLAyNe68uwjAACCCCAwIwKaNR8kylS5UbrNTkEbzFTf62/l2kl3k+uXyp5mfYZQG68nv85yk3Rj+Xz8/w3s44AAgggMIMC9pC3GXzheHoprX4bqAXbHqhvom27++67fZes0+ouvfTSycvv6Ojw5f4rKSmR4uLiye0sIIAAAm4IFJlIg3m56abibvRpBT5oCoK8drBbOgeOVAHWSoZDpsjI1afXSIp+o4mxHc4H2CfHVBaI5jCkIYAAAgggMJMC+iOUW0U/NJjhtifqRYt/WNs7TlgoK8rzrZtcX9bP0Frz45+mAaEhgAACCMyuAAOAxj/YQF+w7bP7yNx59WeeecZ3opqaGsnLy5Pf/OY38u1vf1u2bt06+QITRUE++clPSkYGH9qTMCwggEBMAjXmi4BWM4w2n5FW6v3oWUvkP+7fLtaJvw9vazGVhsfko2cvcWXQbtBML65v65M6IhZiet4cjAACCCAQmYB3bFxeb+4V/THKjfbAq83ygimcZW2rFuTL5cdVWje5vpyZliLLy/PI+ee6LCdEAAEEohM4yvwi5M4nS3Svz1GzIKD5/7TYh/6tFX9PPfVU+cEPfhD0Sk477TT561//KoWFhUH7+O9obLTnF/Hf39TUJOvWrfNtbmhokIULF/p3YR0BBJJY4FDfsKlo2BfTHT6+o81ENOy2TWfSE66rKZZ/NlOa0syUYzfawqIsqSrOduNUnAMBBBBAAIGwAtube6Sz/0iUe9gDQnTQKMKv/O9W8VoGE/MzPfLtt68Rzf83XU3PvaQ0RzT9Bw0BBGZfQL+fV1VV+S6E79+z/zxm6wp4R54t+Vl83e7ubt/gn17CK6+84hv8q6iokF/96leiU4AHBgbk8ccfl1NOOcV3lX//+9/lmmuuieiK9c0l1J+Jwb+ITkpnBBBIGoF5pmpviZkKHEs7e1mpXL++bkq036a9HfLdB1+XYe9YLKefPLaxc1Bae9zJwTR5UhYQQAABBBAIINDYOeDa4J9Gsv9gw07b4J++5MfPWTptg3+ahWORKfqlkX8M/gV4wGxCAAEEZlGAAcBZxJ+tl+7v75986aGhIcnOzpZHH31U3vve90pRUZFkZWXJWWedJY888ohohWBt9957rzz77LOTx7GAAAIIxCpQU5JjovRiy6938uIS+dwFy6ac52VTGOQ79213rZBHvYmg6Bqw506K9f45HgEEEEAAAauAfs7oj05uNJ3kdcdTe6TZ7wesS9dUyHFVzmf1RHIt6Z6j5OiKfFlQSKXfSNzoiwACCMyUAAOAMyUdR6+TmZlpu5oPf/jDsnz5cts2XdGBwG9+85uT23//+99PLodb0LDiUH82bdoU7hTsRwCBJBdI96RItYkSiLUdV1Uk/3LRCtFcQ9a23eRP+tbftknfUOzVfDVZxg4zZbl/OPZzWa+RZQQQQAABBFRgyOSw3dXaNyWtRbQ6T+xsk6d2HbIdrlNy333S4SmAth0urOSZacWrTeGsgqzpryjswuVyCgQQQGBOClAEZA4+di36YW0XXHCBddW2fN5554nH4xGv1yvPPfecbV+oFXL6hdJhHwIITAiU5WX6qhLGmuto5YICueGSo31Rf/1mytNE293WL//+19fkXy9eIYXZsU051mTsOqi4ujJfMjypEy/B3wgggAACCMQkMG4+X3a09MromDup2fe198vPntpru6astFT5pEmbMR3TcisKMn3Tfo/S+b80BBBAAIG4FbCHS8TtZXJhbgpoRd/S0tLJU04kA53cYFnQaMF58+b5trS1tVn2sIgAAgi4I1BrqgJ7YpwKrFeytCxPvnzpStHk5tbW0DEgX/vza6KFR2JtI153KzPGej0cjwACCCCQ+AKaZqJ/+MiPV7Hc0YGuQfmWSYExbD6vrO3asxbL/Hz7LCDr/miWU1OOkrr5uVJjPscZ/ItGkGMQQACBmRVgAHBmvePm1VatWjV5LWNjof+HY2K/RgLSEEAAAbcFNJqu2qUqu4tMXsEbL1s1Jbm55kD62p9flebu2It56Jc0jdTQ/Eo0BBBAAAEEYhFoMZ9Pbb2x/0Cl16Dn+qaJeu8ZtFcQPm9FmZxicua62TSH76oF+aJFvWgIIIAAAokhwABgYjwn169Si3xMtPr6+onFKX/39PTIoUOH84dUVlZO2c8GBBBAwA0BjUpwK2+QJh//6mUrpSzP/qXkUN+IbxBQIwJjbV0Do6IRGzQEEEAAAQSiFegdGpW9Ln2WtJso92/+dZt0ms8na1thqvF+4NQa66aYl7XYxyqTeiMng+CAmDE5AQIIIDCDAgwAziB2PL3UFVdcMXk5WuE3WNN9E1EuZ555ZrBubEcAAQRiFlhskpPrdCI3WqnJLaiRgJV+lQi7TFTEv//lNXFjELC1Z9hUa4x9MNGN++UcCCCAAAKJJeAdG5edpuiHSf8Xc9Pqwd80Ra/a/FJdaNGPz1+4XLTollstwxTc0sG/rHRy4bplynkQQACBmRJw79Ngpq6Y13FFYM2aNXLxxRf7zvXb3/5WNmzYMOW8zc3N8m//9m++7enp6XL11VdP6cMGBBBAwC2BTJOg3K2pwHpNxTnp8hUTCVjjV2m4z1Ty/c8Htot+YYq1NXQMujZ1K9Zr4XgEEEAAgcQR0Cjy4VF7nr5orl6jCLXifZNfiotFJrXGv1x8tGSnuxell2kG/1ZW5It+XtMQQAABBBJPgAHAEM/sl7/8pegfnQbrtPX19fmO0ePivd18881SWFgo4+Pjcumll8qXvvQl2bhxozz//PNy6623ytq1a6WxsdF3G1//+teFKcDx/kS5PgQSX6DcVBIsyklz7UbyM9Pk3968UpaZJOXWptOBb3rwdZMkPXQOVOsxwZbr2/qk2y/fUrC+bEcAAQQQQEBz9bWbz6FY28CIV75tCn40dA7aTrWgMFO+dMnRkuviFF2N+Ftpcv4x+GejZgUBBBBIKIGjzPROFwLPE+qeHV9sSkqKr6LVK6+8IitXrnR03O7du6Wurk70WK/X6+iY2ez05JNPyjve8Q5paWkJeBla0euGG24QHQB0s+nA4kT14YaGBlm4cKGbp+dcCCCQwAKjZlrUlsZu0Yq7brWh0TFfhIROt7K2dTXFcv2bzHu2ea+LpWkV49VMiYqFkGMRQACBOSGgg3avmM+4WKf+6ufat+/bZopS2T/X5udnyFcunVoMKxbcnIxUOdpE/qWlEjsSiyPHIjCbAnz/nk39+Hlt3sWn6VkkyrjqGWecIa+++qrceOONcuyxx0p+vvllLzNTamtrfVN+X3jhBdcH/6aJnNMigECSCOgXjKVlueYHGPduSCMWPnvB8imFQTbt7ZDfbtof8wt5x96Qbc09rg5axnxRnAABBBBAIK4Exs2o3y4X8v7pD2Qaxe4/+FdiUl/ccMlKXwoMt248L9Pjm/bL4J9bopwHAQQQmD0BBgBdth8bOzydzONxL9+Gy5c45XQlJSXy1a9+VTZv3izd3d0yODgoWhn4zjvvlOOPP35KfzYggAAC0y2gFYH9C3jE+pp6zi9cuMLkQ7LnLvrLlibZsD1wFHQkr6m5nPSLHQ0BBBBAAIFAAvtMFfr+4dhST2jxkO8/vENePWhPUVRoPuNuePPRUpqXEeilo9qmg38a+ech8i8qPw5CAAEE4k2AAUCXn8jrr7/uO2NxcbHLZ+Z0CCCAwNwSWFiUJfrlw81Wac756Tctk1S/8MI7n9xjph13xfxSmguwxyRkpyGAAAIIIGAV6OgfkWa/Qh3W/U6Wx0wE4S2P7pLNDfbPK831968m519FQZaT0zjqoz+a6eBfaoqL4fiOXplOCCCAAALTJeDuN6vpusoZOu8TTzwR8JWee+45OXToUMB9ExuHh4dF8//ddNNNvryBxx133MQu/kYAAQQQiEJAc5DqVOBXDnSLTrF1q62uLJAPnVkrP3mifvKUmovp5od3ytfeskqqTOXEWNoBk4w9v8K9QiaxXAvHIoAAAgjMvoAWnNKCUbG0cZO2/ceP75ZNezpsp9Godh38i/Wzy3pSLca1rCzP5DRn8M/qwjICCCCQ6AIMAFqe4DnnnOMbvLNsEs3ld80111g3hVzW/vql9aMf/WjIfuxEAAEEEAgvoLn7auflyE6/JOfhjwzd49zlZb5IjP97+eBkx0GTUP0/H9guX3/rainMTp/cHulC18Co9A17Xa2+GOk10B8BBBBAID4E9LuBpocYjeGHLD3HHSZS/cld9oCEDE+KfPGiFb7PSbfutiQ3Xep8eXgZ/HPLlPMggAAC8SLAFGC/J6EfsBN/JnZNrDv5W6vZ/uhHP5LLL7984nD+RgABBBCIQWBeboaUmaqGbrd3r62Sk2vt6RoO9Y3Idx/aEXMxD40CpCGAAAIIIHCga1B6Br0xQfzhxUZ5ZHur7Rxppvr8Fy5cLsvm59m2x7Iyj8G/WPg4FgEEEIh7ASIALY/o0UcfnVzTwb7169f7ovnuuOMOX1XcyZ1+Cxrxp5VzKyoqpKqqym8vqwgggAACsQrUlORI75BXBkdiS55uvY4U8979iXOWSnv/a7biHRqpcetju+RT59WJ9ommaa6nfhMFmGPyMtEQQAABBOamQK/JCdsY4w9CT5movz++eMAGqHn5PnP+clm5oMC2PZYVzSO4pDR3ymyoWM7JsQgggAAC8SXANxPL8zj77LMta0cW161bJytXrjyygSUEEEAAgRkV0C87OiVpq8kHqPn63GrpZvrUZ89fJl/531elrW948rTPmhxLv3+uQd6zrnpyW6QLB03UR52LkRmRvj79EUAAAQRmT0Cr9e40PyiZmIKo246WXrntid224zUt3/Xr6+S4qkLb9lhWNJqwbn4uOf9iQeRYBBBAIAEEmAIc4iHt2bNH6uvrZdmyZSF6sQsBBBBAYCYENJquuiS2Ah2BrlPz/X3houWSZfINWpvmB/SfcmXdH2653UQBuhmxGO712I8AAgggED8Cew71y/DoeNQX1NY77EtJ4Z878KpTa2StX/qKqF/EHKiB7nWm4Ifm3KUhgAACCCS3AAOAIZ6vDv4tWrRIPJ7IAyU/8YlPhDgzuxBAAAEEohGoKMgSrU7odltYlC2fNpGAqX5Tfu80Sdc16jCaplEfB7oGojmUYxBAAAEEEligtWdINKdstE1/PPqvB183uQNHbae4YOV8uWBVuW1brCtaPbgg2/3P1Vivi+MRQAABBNwXYAAwhKkW8njhhRdC9Ai869prr5Xbbrst8E62IoAAAgjEJKA5inTqrtvtmMoCueaMWttpx8wo3vcf3iENHdEN5OkXwCFTXZiGAAIIIDA3BHTwbm97dJ8ZKjRu8lzc8sjOKZ87a8xn1AdM9J+brTgnXSoLs9w8JedCAAEEEIhjAfe/QcXxzUZ6ab29vXLJJZfI66+/7vjQD3/4w3L77bc77k9HBBBAAIHIBNJSU2SpL1F5ZMc56b1+RZlctqbC1nXAfJn7zv3bRadjRdoORwFSEThSN/ojgAACiSigg3c7W3tlLIZktb/etF9eauiy3b4O0mlhKs2H61bLSk81RT9y3Dod50EAAQQQSAABBgBDPKSlS5dKW1ubnH/++dLQ0BCi5+FdH/zgB+VnP/uZb+XKK68M258OCCCAAALRCeh0pQVmOvB0tCtN4Y91fvmVtKrvt+/bNmU6lpPX14FDogCdSNEHAQQQSGyB/SZavH84+qhvzTv7t1eabAhanffzFy53taq8DiQuM0U/POYHNRoCCCCAwNwR4F0/xLN+6KGHpLKyUhobG32DgK2trQF7v2FCPN7//vfLL3/5S1Pp6w153/veJ//zP/8TsC8bEUAAAQTcEagqzpK8zMhztIZ79RSTB/AT5yzxVR229m3qHpL/MJGAkRb20ChAPZaGAAIIIJC8AvpjTyzv9a8e7BbNO2ttOlCnlern52daN8e8rJF/2enuf37GfGGcAAEEEEBgWgUYAAzBqwVAHnzwQSkpKZGdO3fKRRddJD09PbYjxsfH5b3vfa/8+te/9m2/6qqr5Be/+IWkpEBrg2IFAQQQcFngKDNQt7w8b1oGATM8qfKFC1fIwiJ7lGG9qer43Ydel9GxyCo7akL4EW9kx7jMxekQQAABBKZJoH/YK/VtfVGfvalr0JdvVvPOWttHzqyVFRX51k0xLy8ozJSS3IyYz8MJEEAAAQQST4BRqjDP7Oijj5a//e1vkpOTIy+//LJceumlMjR0OJJjbGxM3vOe98jvfvc731muueYaufPOO0W/lNIQQAABBKZfQPMBrjRfjkrz0l1/sVwTXfili4+Webn2c796sEd++OguX6J2py+q6aAOmi94NAQQQACB5BLQH4Reb+mVaNP+9ZnBQ6346z91WPPRnr2szFWs/CyPVJuqvzQEEEAAgbkpwACgg+e+du1a+dOf/iTp6eny1FNPyRVXXCGDg4Pyrne9S+6++27fGT7ykY/4in8w+OcAlC4IIICAiwIpZorU0rK8KdF6bryEVkj8VzMImO831XjTng6586k9vrQPTl+n1UwPizRy0Om56YcAAgggMPMCmvpnV2ufDI9GF+HtNTOJbjaV5v2nDp+0qEg0H62bLd2TYlJb5BGo4CYq50IAAQQSTIABQIcPbP369fLb3/7WN7X3/vvvl9raWt+goB7+sY99TG677TaHZ6IbAggggMB0CFSZqIalZbniYpFE32VWmOqLX7xohWSlpdoue4NJ1n7X8422baFWtCpkUxe5AEMZsQ8BBBBIJIGGjkHpGhiN6pJ18PDnT+0VjSq3tkUl2XLduUvNZ5l7M4r0c1GLfuggIA0BBBBAYO4K8CkQwbO//PLL5ac//anvCC0Ioh/c1113ndx6660RnIWuCCCAAALTJVCalyFHL8iXtFT3vjjptS4uzZXPXrBMPH6ji3/afEDu22qv2Bjq3ppNLkCiAEMJsQ8BBBBIDIH2vmE5EENqh/u2Nov+kGRthVlp8vkLlkum3w9O1j7RLNfMyzH5ctOiOZRjEEAAAQSSSIDyT+Zh7t+/3/Ej1UjAT33qU/Lf//3f8o53vEM+//nPBz2+utrd0H3HF0lHBBBAYA4L5JsvOasrC2R7c2/EFXtDsa1aUCCfXF8nN2/YYX4AOtLzl0/vk9wMj5xZV3pkY5AljQJsNhWBNVqRhgACCCCQmAIDI17Z3dYf9cVvbuiSXz27z3a8/nD1uQuXu16gQ38Yc7uKsO3CWUEAAQQQSBgBBgDNo9LpvJE2zfV3zz33+P4EOlb3e73eQLvYhgACCCAwzQIaPbHaRALuaOmT7sHopmcFusR1tcXyoTNq5faNe2y7b3u83jcIeHx1kW17oBWNAqwoyBSPKWBCQwABBBBILAGvFv0wPzDpDzrRtBbzGfDDR3fafkjS83z87KWyxESbu9lyMlJlsYn+oyGAAAIIIKACfPswCDqVdzr+8J8YAggggMDsCegA29EVeVKWn+HqRZy3Yr68e22V7Zxj5nPk5od3+r4U2nYEWPGOmShA8wWQhgACCCCQeAK72vpkKMqiH8PeMfm+KfrhX/H3nSculFOXlLiKoYN/K8rzTf5yd1NiuHqRnAwBBBBAYEYFiAA03D/72c9mFJ0XQwABBBCYGQGNxtaICo0I3N8+4NqLvvXYBdJrIgv/ZnI4TbQRExXyXw9sl69ctkqqw0zx1WnAFQVZksoXswk+/kYAAQTiXqChY0A6+6OLKtdggzue3CP7/D6LNLL8bcdXunrveaZy/YryPCLNXVXlZAgggEDiCzAAaJ7hVVddlfhPkjtAAAEEEAgqUGkq+Waa6oe7WvskyllbtnPrwOJ7T1kkvUNe2bjr0OS+/pEx+fZ92+Qbb10dMo/TqIkC1GlgC8x10RBAAAEE4l+gs39EGjsHo77Qh7a1yMadRz4v9EQLCjPlY2ctEf1McasVZqeZir95/MDkFijnQQABBJJIgCnASfQwuRUEEEAAgeACJbmHKwS7FXWXYr6wXXv2Yjm+qtD2ol0Do/KLp/fatgVaaeoelHE3RiMDnZxtCCCAAAKuCQyaH3d06m+0bUdLr2jBKGvLMpHpnzl/uWSlp1o3x7Q8LzfdF/nn1udcTBfDwQgggAACcSfAAGDcPRIuCAEEEEBgugS0QvAKkxfQrS9HnpQUuf5NdbLcRFtY23N7O2XPodAVIke8b0hr77D1MJYRQAABBOJMQIt96ACe5m+NpnUNjJgcsTumFA352NlLRKPT3WrzTb7bpWW5rkYTunVtnAcBBBBAID4EGACMj+fAVSCAAAIIzJCADgIuN7mR3BoEzPCkyucuWO6rAmy9hT+80GBdDbjc0DkgGllCQwABBBCIT4HdJvJvIMr3ae/4uPz3hp3SaSLDre0tJo+s5v5zq+lA4mKT79bNqcRuXRvnQQABBBCIHwFyADp4Fl6vV/7617/Kxo0bpb6+Xnp7e2VsLPQXNv0A3rBhg4Oz0wUBBBBAYKYFCrIODwJub+pxJSdgrkm4/uY1FfL7544M+r24v8vkHOw1ERn26EDrvWpEyesmsuSYygLXBiSt52cZAQQQQCB6gQNdg9LeNxL1CX7z7H7Z3txrO371gnx510n2SvK2DhGuVJdkuxpJGOHL0x0BBBBAIIEEGAAM87Aef/xx+eAHPyj79++f7KlVvII1HfjT/fwCF0yI7QgggEB8COgg4IryfPPlzJ1BwItWlct9rzRJjykMMtHufqFRvnTx0ROrAf/25ZYyxUk0KpGGAAIIIBAfAlr0Q6v+RtueMgWi7rNUitfzlOSkyyfX17nyg4/WDVk8L0fK8jOjvUSOQwABBBCYYwIMAIZ44Js3b5aLLrpIRkZGfIN6mZmZUldXJ4WFhZJi8j7REEAAAQQSW6DAVEvUgbfXTYRGrPU4Mk1C98vMtK5fm4iPibalsdt37nCDex3/+KJZVZw9cSh/I4AAAgjMksBE0Y8Qv/mHvLL9ZuDwpxvrbX3SUo+ST5+/TPLNj0+xthQz+LfE5PubZ4pb0RBAAAEEEHAqwABgCKmvfvWrMjw8LBkZGfK9731Prr76atFBQBoCCCCAQPIIFGanyzJTxEOTvMc6CHj+yvny1y1N0jV4JN/T3SYX4L+9eWVYsMbOQV8ewSITIUJDAAEEEJgdAe/YuC8yPNqiH/3DXvn+Qztk2Dtuu4GrT6uVJSZPX6xN89cum58r+tlFQwABBBBAIBIBwthCaD355JO+qbw33HCDfPzjH2fwL4QVuxBAAIFEFtBBNx0E1ClVsTQtCPLW4xbYTvHqwR557WC3bVuwlV0m2TxFQYLpsB0BBBCYXgFN47PTpGQYGrUP3jl91XFz/K2P7ZbmniHbIetXlMm55k+szWOiCLWSPYN/sUpyPAIIIDA3BRgADPHch4YOf3jrNGAaAggggEByC7g1CLh+xXwp9oviu+v5Rl8qiXCCE0VBNAKFhgACCCAwswI6dbfLr2JvJFfwp5cOyIv7O22HLCnNkQ+eVmPbFs2KRv4dXZEvWsmehgACCCCAQDQCDACGUKupqfHtHR09MpUrRHd2IYAAAggkuIAO3NWZvEqxRAKme1Lkcr8oQK30+8oBZ1GAE7mnEpySy0cAAQQSSqCtd1gOdtkj9yK5gc0NXfIHU/jJ2vJMhfhPv2mZpKXG9pVLc/4tN1HquRlkb7L6sowAAgggEJlAbJ9Gkb1WwvW+/PLLfdf8xBNPJNy1c8EIIIAAAtEJlJik6ktjHAQ8d3mZSc5uz8+kFYFDVZG3Xm1n/2hM1Set52IZAQQQQCC0QJ/J21dvUjBE21rNlN8fPrpT3rCcQH9I+pSp+KufKbE2LfihRatoCCCAAAIIxCLAAGAIveuvv14qKirkpptukr1794boyS4EEEAAgWQS0MqKmqw92khAj4n2eNvxC20ku0xeqZdMhIjTpkVBtDowDQEEEEBg+gRGTLGOWCrBD3vH5Hum6Ef/8JjtIq9cWy2rKwts26JZqZmXTbXfaOA4BgEEEEBgigADgFNIjmwoLS2Vv/3tb5KVlSUnn3yy/PSnP5XubmdTuI6chSUEEEAAgUQUKM3LkMUmd1O07axl86TMnMPadHqY0yhAPU4HDQdGvNZTsIwAAggg4JLAuCn9rhXgdRAwmqbv5z99ol72mdyB1rautlguW1Nh3RTVcmVhllQUZEV1LAchgAACCCDgL8AAoL+I3/qaNWtkYgrwxz72MSkpKZHy8nJZvHhxyD9LlizxOxOrCCCAAAKJJlCWl2kiAaMbBPSkpMgVJ9ijAPcc6pfn99kTxIcyGTNfTjUyhaIgoZTYhwACCEQnUG/ek3uHov+R5b6tzfLU7nbbi+ug3cfOWmIiyGMrK1+WnyHVJdm2c7OCAAIIIIBALAJkkg2jd88998iHPvQh6e3t9UVt6C99ra2tYY6SmD/0w74AHRBAAAEEZkSgLD9TzDic6OBdpO30pfPkT5sPSFP3kcTyGgV44qIiSXH45XBodFx2mkjAFeV5fLZE+gDojwACCAQRaOoeFC38EW3bago7/frZfbbDs9JS5bPnL5Os9FTb9khXinLSZPG86H58ivS16I8AAgggMHcEGAAM8ayffvppufLKK2Vs7HBOj0WLFolGBBYWFkqKieygIYAAAgjMDYHyAh0EfEP2tduneYW7+1RTulGjAH/46K7JrvvNVLFNezrklMUlk9vCLXQNaFGQQaJBwkGxHwEEEHAg0G3eUyN9P7eeVgcOf/DITt+PQ9bt1527VCpMBGAsTSsHLyvjB59YDDkWAQQQQCCwAAOAgV18W7/xjW/4Bv8KCgrk17/+tVxyySUherMLAQQQQCCZBRaYL3U6CKgDcZG0U81A370vHZADXUeO0yjAdTXF5sck51PE9PicjFRXKkpGcv30RQABBJJJYGh0zERV68ye6O5K8wV+/+EdU6YO6489Gt0dS8s2kYMa7R3JZ0Msr8exCCCAAAJzS4AwthDP+/nnn/dNt/ra177G4F8IJ3YhgAACc0VgYVG2LCyKLLpDv8i980R7LkAdzHu63p43yomh5qsaHYsuWb2T89MHAQQQSGaBibyqo2PRjf5pKqDbn6yfkhLihOoiefsJlTHRpXtSZEVFnmgVeRoCCCCAAALTIcAnTAjVgYHDU73OOOOMEL3YhQACCCAwlwSqirNlQWFmRLe81lSEXGSOszaNAtQvo5E0r/nS2uBXbTKS4+mLAAIIzGWB+jatrH44tU80Dg++1iIbdx6yHVphUkRcd+4Sx3ldbQf/YyUt9ShZWZEvGZ7YcgcGOjfbEEAAAQQQmBBgAHBCIsDftbW1vq0TA4EBurAJAQQQQGAOCiwqyRH90ue0acGPd/hFATb3DMmTu+xfJJ2cr9Xknuobjr5qpZPXoA8CCCCQbAI9Q6NyqG8k6tva1tQj//O0vehHZlqKKfqxXLLTo8+qpLlil5tpv7EWDon6xjgQAQQQQGDOCDAAGOJRv/3tb/dV/n3ggQdC9GIXAggggMBcFKgxFRrn52c4vnXNDeVf1fGPLzaKdzyyKb2at2pvFBWJHV8oHRFAAIEkFNgfYREnK0F737DcvGGnjPklDvzE2UulMsK0ENbzajH4urJcyctMs25mGQEEEEAAgWkRYAAwBOtnP/tZqaurk5tvvlk0HyANAQQQQAABq8Di0lwpzXM2CHiU+ab3zpPsuQA1mu/xHW3WUzpa7h3ySmvvkKO+dEIAAQTmusAhM4Cn75vRtImiHz2Do7bDLz+uUjS9QyxNfxQqykmP5RQciwACCCCAgGMBBgBDUOXl5cmGDRtk9erVctZZZ8kNN9wgW7ZskaEhvnSFYGMXAgggMKcElpTmyLxcZ1/gjl1Y6Iv2sAL9yVQIjqawh+YC9FIQxErJMgIIIDBFYNzkWt0fZe5ULfrxs6f2yO62ftt5j6sqnFLcydbBwYoWlCrLd55KwsEp6YIAAggggEBIAQYAQ/CkpqbKokWLZNOmTb5Bv+985zty/PHHS05Ojui+UH88nuhzgYS4JHYhgAACCMSZgEb2LTVTuEocDAIejgKsst2B5qR64NVm2zYnKyPeN0SrCdMQQAABBIILNJl8q8OjkaVamDjbw9ta5TG/KG1N/XDduUtFK7xH2/RHIy0oRUMAAQQQQGAmBRgADKGtv/pN/NFuE8tO/w5xanYhgAACCCSRgA7saR6nopzweZxWL8iXFSbhu7X9blODvN7ca93kaLmpe0gGY6ho6ehF6IQAAggkqIBGVx+M8oeSHS298oun99ruPMNzuOhHbkb0P/TnZXpkiUkfQUMAAQQQQGCmBaL/9JrpK52F17vxxhtn4VV5SQQQQACBRBTQQUD9UvfSYJeMmSlnwZr2e/faKvnan1+b7KKJ5W9+eId86+3HSFG2s+nEerDmo99jCoKsNIOKNAQQQAABu0Bj56BJlRD8/dje+8haR/+IfP+hHVPeyz929pKYIve0arBW/I0levDIVbKEAAIIIIBAZAIMAIbwYgAwBA67EEAAAQSmCKSlpkhFQabol85QbUV5vrz1uAXyv5sPTnbrMgnmdRDwy29eKR5zHqet2xynFSpLcp0VI3F6XvohgAACiSyg0dEtZvpvpK1v2CvfuW+b6Huytb3l2AVyyuIS66aIlj2pR5no73zRzwkaAggggAACsyHAJ9BsqPOaCCCAAAJJK7CgMEvSPeFzQ73rxCpZU1lgc9jR0ie/fGafbZuTlX0mwb0muqchgAACCBwW0MIfGiUdSRsaHZP/emC7NPj9iKPv1e8+yZ6/NZLzmsBvWTY/T7LSUyM5jL4IIIAAAgi4KsAAoKucnAwBBBBAYK4LpJrE8JWF4ZO76xSwf16/VEr9Ivceeq1FHt/RGhGjJrinIEhEZHRGAIEkFtDIaJ3GG0nTquoaha0/xFhbuanUq+/VsUzbXWyqxRdkhc8Ra31dlhFAAAEEEHBbgAFAt0U5HwIIIIDAnBfQKpGa6ylcy8tMk0+fv8xMCbNHDN7x5B6pb7N/CQ13Lk10r9ErNAQQQGCuC+xvH4iIYNyECt76+G55ubHbdlxRdpr86yUrRN+ro22VJiq8LC8z2sM5DgEEEEAAAdcEwn87ce2lOBECCCCAAAJzQ0ALfSwsCh8FqBq183LkI2cutsGMmqT13zeRKD1D9hxUtk5+KzoDeF+EX3r9TsEqAgggkPACbb3Donn8nLY3zODfL/6+V57e3W47RCv9funio6U0hsG7ebnpUl3i7LPA9uKsIIAAAgggMA0CDABOAyqnRAABBBBAoDQvQ3IynOV7OrOuVC5cVW5DO9Q3Irds2DmlCqWtk9+KTnnrGohs2pvfKVhFAAEEElZAc6E2dEYW/XfPi43yoEm9YG0ZnhT54kXLY6r4m5fp8VWGt56XZQQQQAABBGZTgAHA2dTntRFAAAEEklqguth55Mf7Tqk2FSLzbB5bD/bI75/bb9sWbmWviQLUiBYaAgggMNcEmkzVX82J6rTdv7VJ7nnxgK275nH9jEnNsLTM/n5s6xRmJcOkgFhu3s9jyRsY5iXYjQACCCCAQMQCDABGTMYBCCCAAAIIOBMozE6X/CyPo86elBS5/rw60ZxT1vbnLU3yTL19app1v//y4MiYNHUP+W9mHQEEEEhqgVFTxENzoTptT+46JL942l51XbOxXnfOUlmzsNDpaab085icrkeX55vcrnzNmoLDBgQQQACBWRXgk2lW+XlxBBBAAIFkF4gkClAHDP/fm5aJRqBY249NcvqGDufT2ho7B2XE6zwKxvpaLCOAAAKJKKDve16TP9VJe3F/p/z4sd1Tul59eq2cuqRkynanG0z6V1lmIgez0p2lf3B6XvohgAACCCDghgADgG4ocg4EEEAAAQSCCGj1yBKTCN5pWzY/T646tcbWfdgM5n3voR3S7zCx/ZjJg7W/o992DlYQQACBZBXQyOcWM/3XSdve3CM3myJLY36pEt51UpWcv3K+k1ME7bPYFHUq8IviDtqZHQgggAACCMywAAOAMwzOyyGAAAIIzD2BKlMRWCNDnLY3HV0m5ywrtXVvNl9ub31sl4z7fWm1dbKstPWORFRF2HIoiwgggEBCCewzP3g4eWvc194v//XA66KV1q3tktXlcvlxC6ybIl6en58hZfmZER/HAQgggAACCMyUAAOAMyXN6yCAAAIIzFkBnQ5WZqoCO21HmdFCnYqm0STW9uL+Lrn3JXvCeut+/+X9piAIDQEEEEhmge7BUensHw17ixoh+O37tsuAiRa0tjPr5sl7T1lkfqSJ4Fca6wnMslb8rfV7v/brwioCCCCAAAKzLsAA4Kw/Ai4AAQQQQGAuCFQWZYlfar+Qt53uSZFPm0qU+sXS2u55oVE2N3RaNwVd7h3ySu9Q+C/GQU/ADgQQQCDOBZz80NE1MCLf+ts20cFCazuhukiuPWuxeW+OfvBP36s1dUMsA4jWa2IZAQQQQACB6RJgAHC6ZDkvAggggAACFoEMT6pUFGRZtoRfnJebIZ9aX2ebPqwT1257ol76zOCek9ZMRWAnTPRBAIEEFGjtHZK+MLlR3zBzg3+6cY+09g7b7nBFeZ6v8rpWYI+26Y86y+bnig4C0hBAAAEEEIh3AT6t4v0JcX0IIIAAAkkjsKAwU9JSI4s0WV1ZIP+0rtpm0DUwKr94eq9tW7CV9v4RGfbap7wF68t2BBBAIFEEtNhRQ8dg2Mt9fl+naNVfa1tUki2fv3B5zAN3Ou1XCz3REEAAAQQQSAQBBgAT4SlxjQgggAACSSHgSU2RBYWRRQHqjb/5mAo5rqrQZvDkrkPy/N4O27ZAK5oYv6XbHvkSqB/bEEAAgUQSONg1KCOmQnqoNjQ6Jj//+15bl/ysNPmXi1ZIdro9vYKtk4MVin44QKILAggggEBcCTAAGFePg4tBAAEEEEh2gXJTJTLS6WKaW+rDZ9SaL6ypNp7bn9zjKMdfi5kmp9EyNAQQQCAZBDSquclBeoM/mJypHSYK2to+YAp+FGanWzdFvKy5WWtK7EWaIj4JByCAAAIIIDDDAgwAzjA4L4cAAgggMLcFUkzSqCpTECTSVmLyAX7g1EW2wzSh/S/8oltsHf6x4h17Qw71EQUYyIZtCCCQeAI69Tfcjxr72vvlvq1NtptbvSBfTltSYtsW6Uq65yhf0Q99L6chgAACCCCQSAIMACbS0+JaEUAAAQSSQqA0L0Oy/KL5nNzYWXWlcrzfVOCndrfLc3vCTwV2Ei3j5BrogwACCMymQL8p+hHuB41xk/vgDhMhbQ189pgBu2tOr42pWq+O+dWZir+RRnHPphevjQACCCCAwIQAA4ATEvyNAAIIIIDADAnolN7q4uyIX803FfjMxZLjN3h4+1N7pGdoNOT5BkfGpGvAPhUu5AHsRAABBOJQYK+J7NPcpqHao9tbZWdrn63LW49bIBVR5GC1nqTGFP3Ip+iHlYRlBBBAAIEEEmAAMIEeFpeKAAIIIJA8AsU56aZ6ZORJ6PW4q06rsUH0mKnAP39qr21boBWiAAOpsA0BBBJFQPP59Qx6Q16upkb47ab9tj6ae/Utx1batkW6UpafIfPNeWgIIIAAAggkqgADgIn65LhuBBBAAIGEF9BokmjSSJ2xdJ6cUF1ku/+n69vl2T3ttm3+K10DozIwEvrLs/8xrCOAAALxIPCGCfvTvH7h2q+f2Sf9JuLZ2q4xRZRimbarP9bUUvTDSsoyAggggEACCjAAmIAPjUtGAAEEEEgOgdwMU0nSDAJG2g5PBa6VnAx7VeA7Tc4rjQYM1ZodVM4MdTz7EEAAgdkQaO4ZkqHR8ZAv/erBbtm465Ctz+mm6McxlQW2bZGsaNGPuvm5QtGPSNToiwACCCAQjwIMAMbjU+GaEEAAAQTmjIBOKdOiIJG2oux0+eBptbbDeoa8cqfJBxiqtfUOy+hY6C/RoY5nHwIIIDDTAvqe1dg5GPJltY/+CGJt2SZf6vtOsVdPt+4Pt2zStfqKfmR47D+2hDuO/QgggAACCMSjAAOA8fhUuCYEEEAAgTklsNhEAfpH8zkB0MiWkxbZpwI/ayoCP20qAwdrWhWz1QwC0hBAAIFEEdDBP++YefMK0f788kE56BfhfOXaKik0P5ZE2/S9maIf0epxHAIIIIBAvAkwABhvT4TrQQABBBCYcwI6tWzZ/DzxpJpwkwiaTgX+kMltpVOJre1nf98jmgg/WNNpwJpPi4YAAgjEu4BWMG8x039DNX1P+9PmA7YuS0pz5LwV823bnK5o5N+Sshwpo+iHUzL6IYAAAggkgAADgAnwkLhEBBBAAIHkF8hMS5WlpbkR36hGt1x9eo3tuM6rBWEAAEAASURBVN5/TAUONsg34h2XdlNNk4YAAgjEu8C+jn7zg0Xwq9T3uZ+Z1AejlghBHcD70BmLo8rbp4WZ6spypSyPir/B1dmDAAIIIJCIAgwAJuJT45oRQAABBJJSoCgnXRYWZUV8b6cuLpF1NcW24zbpVGBTGThYoxhIMBm2I4BAvAh0m8rlnf3Bo5n1Op+p75AtB7ptl3zRqnKpjaLAUqoZ/VtenicluZHnZbVdACsIIIAAAgjEoQADgHH4ULgkBBBAAIG5K6ADgIXZaREB6FTga8xU4LxMv6nAT+2VroHAkX4aJdg7FPqLdUQXQWcEEEDARQGN7Nvb3h/yjAMjXvnlM3ttfYrNDynvPLHKts3JiqZgWFGRF1POQCevQx8EEEAAAQRmS4ABwNmS53URQAABBBAIIKCDeUvN9LOMtMg+oguy0uRqv6rAfcNeucNUxQw2FZgowAAPgE0IIBAXAlqxfMDk/wvV7nq+0fzIYf8h4wOnLpIsU/03kpZmBv9WVuRT8CMSNPoigAACCCScQGTfLhLu9rhgBBBAAAEEEk8gLTXFVxREc1FF0k41VYFPrrVPBX5+X2fQqcCaB3DYG/oLdiSvT18EEEDADYExU668oXMg5Kl2t/XJg6812/ocV1U4JR2CrUOAlXRPiqxaUGAqsdsjqAN0ZRMCCCCAAAIJLcAAYEI/Pi4eAQQQQCBZBbSybzQ5rK45vdZEsdi/yN7zQqOMB8iir5tauoeTlZD7QgCBBBU40DkoI97glT/GzQDh4ejmIzeoUXxXn1YjGkXttGWaSOtVC/Ijjhh0en76IYAAAgggEE8CDADG09PgWhBAAAEEELAIlOVnSll+ZMno881UYB0EtLaD3UPySqM9Sf7E/tbeIdFoGxoCCCAQDwJDo2PS1D0Y8lIe3tYiew7Z8wO+/YSF5v3SeeXebDNNWCP/tAI7DQEEEEAAgbkgwADgXHjK3CMCCCCAQMIK1JbkiEYDRtLWmWnA/tWE73/VPlVu4nyjY2/IoT6iACc8+BsBBGZXoKFjwEQsB78GHSC858VGW4fKwiy59JgK27ZQK1owSSP/dPovDQEEEEAAgbkiwKfeXHnS3CcCCCCAQEIKpJhEgHXzc0WntzltOgXuolXltu6bG7qCRtU0mQhBGgIIIDDbAlqZ/FBf4MrlE9d239Zm6TFVzK3tmtNrxGNypzpp+VkeOdoU/HDa38k56YMAAggggEAiCDj7pEyEO+EaEUAAAQQQSFIBnaKmlYEjSG0lpy+dZ5La26e2PfhaS0ChQVNps2sg9JfugAeyEQEEEHBJQPP67T0UuvBHnxn4+8uWg7ZXPKG6UFaaqbxOWlFOmhxdni+pkVZYcnJy+iCAAAIIIBDnAgwAxvkD4vIQQAABBBBQgcLsdNFpbk6bDhqeu7zM1v3x19tEB/sCNaIAA6mwDQEEZkpgb3u/9A3bI/v8X/vPZvBvwO897F0nVfl3C7iuBT/qyvJEo6ppCCCAAAIIzEUBBgDn4lPnnhFAAAEEElJA8/oVZqc5vvYLVs63RQ0OmtxZT+xsC3h818Bo0MHBgAewEQEEEHBJoK13WFp6Quci1Sjl+830X2s7dUmJLDJ5UsM1jZ7WKGoi/8JJsR8BBBBAIJkFGABM5qfLvSGAAAIIJJWA5vbTL7EZJpLFSSvNy5QTq4tsXR8wxUDG3wicYf9gmMqbthOxggACCLggMDDinVLRN9Bp733pgIyMjU/u0kC+d5rKv06aRk/nZTr/8cTJOemDAAIIIIBAogk4+waRaHfF9SKAAAIIIJCkAmkm0f2y+WYam8NZbBetthcD0am+Wxq7A+poFA65AAPSsBEBBKZBwGsG9F5v7pWxUGV/zeu29Q7Jhu2ttis4e1mZVDhIi6BV1P2rottOxAoCCCCAAAJzRIABwDnyoLlNBBBAAIHkEdAvtDXzwk970zteaapdVpmpw9amUYCBmgYG7mztkyEzVZiGAAIITLfA7rZ+835zJKov2Ovd8+IB2yChx/wCcsUJlcG6T27XH0qWlOWYVAgOfzGZPJIFBBBAAAEEkk+AAcDke6bcEQIIIIDAHBCYn58ppXkZYe9Uv/he6BcFuLmhS5q6BgMe6x17Q3a09IpW5KQhgAAC0yVw0LwHdfSHrz5+oHNwSu7S801+05Lc8O9/1SXZkp3uma5b4LwIIIAAAggklAADgAn1uLhYBBBAAAEEjggsNlGAORmpRzYEWTpj6bwp/R58rSVIb5H+4TGpP9QfdD87EEAAgVgEugdHZX/HgKNT3P1Cg1jTlmZ4UuStx4WP/ivISpOKAnv0s6MXpBMCCCCAAAJJKsAAYJI+WG4LAQQQQCD5BVLM/DbNB+hJDT29LcOTKuuXl9lAHt/RJpp8P1jTfIDNJl8gDQEEEHBTYMQ7Lrtae22DesHOX9/WJ8/u6bDtvuSYCtHBvVBN3xN16i8NAQQQQAABBI4IMAB4xIIlBBBAAAEEEk4gMy1VlpTmhr3u81eWmzxYR7oNmjx/T+w4dGRDgKW97f3SOzQaYA+bEEAAgcgF3jChfJpiYMTrLMXAXc832F5EI57fbAYAw7WakhzRHz5oCCCAAAIIIHBEgAHAIxYsIYAAAgggkJACxTnpsqAwM+S1a77AkxYV2fpoMZBx69w6217xRejsaOkzX9bDJ+n3O5RVBBBAYIqATvvtHQoeeWw9YFtTj7zsV7H8LWsWmHQGoXP6leSmO8qPan0tlhFAAAEEEJgLAgwAzoWnzD0igAACCCS9QHVxtuRnhf5ifNGqcptDc8+QbGnssm3zX9HBv52+6XrOInb8j2cdAQQQUIH2vmE52OUsrYBGCv7+OXv0X6GZ9nuB33uYv2y65yipdVgh3f9Y1hFAAAEEEEh2AQYAk/0JR3h/X/ziF80UsaMm/zz22GMRnoHuCCCAAAKzIaDv3XVleZJuEuQHa0dX5EuVGSi0tvu3NltXAy73DHodJ+wPeAI2IoDAnBYYHBmT3W3OCwu9bH6YeN1MFba2tx1fKZryIFTTdAhpqcHfA0Mdyz4EEEAAAQSSXYBPyGR/whHc3+bNm+V73/teBEfQFQEEEEAgngR08K9ufq4t15/1+nSQ0D8KUKfYHewatHYLuKyRO4dMBA8NAQQQiERgbPxw3j/920nTtAT+0X+luRmyfoW9kJH/uebnZ0hhdrr/ZtYRQAABBBBA4B8CDADyn4JPYHx8XK699lrxer1SVhb6f7AgQwABBBCIX4H8zDRZVGKP8rNe7elLSyTXL4fWg6+1WLsEXa43ETyhKgcHPZAdCCAwZwX2HOoz7xtjju9/k6n6u7d9wNb/ihMXmmrnwb+2ZKalmPc9qv7a0FhBAAEEEEDATyD4J6lfR1aTW+AHP/iBPPfcc7JixQr50Ic+lNw3y90hgAACSS5QUZAlmgg/UNPKmP6RNI/vaHU0sHc4kqdPvGMUBQlkyzYEELALNHcPSVvviH1jiDV9j7nbr/JvZWGWnLl0XtCjtLr50rJcSU2xlDkP2psdCCCAAAIIzF0BBgDn7rOfvPP9+/fLl7/8Zd/6j3/8Y0lPD/ylcfIAFhBAAAEE4l5Ac2FlpQfOl3X+yvm2acJDo+PyxI42R/cUaS4vRyelEwIIJJ1A/7BX9rU7z/unABt3tslBM2hobe88aaGkhBjc0wHCPBP5TEMAAQQQQACB0AIMAIb2mRN7r7vuOunr65OrrrpKzj777Dlxz9wkAgggkOwCGg2zpDTwlLh5Jp/W2ppiG8EDr7aI5t5y0jr6R+SAg7yBTs5FHwQQSD6BcRPJt7O1z7ynOL+3URNZfM+LjbYDtKLvOr/3KmsHTWewsCjLuollBBBAAAEEEAgiwABgEJi5svmuu+6Sv/zlL1JcXCw33XTTXLlt7hMBBBCYEwIaFZOX6Ql4r/7FQJp7huTlhq6AfQNtbOgYkO6B0UC72IYAAnNcYK+J/NNo4Ujahm2tptCQfbrwu0+qMtHKwaf21pofOULtj+T16YsAAggggECyCzAAmOxPOMT9dXV1yfXXX+/r8R//8R8yb17w/CohTsMuBBBAAIE4Fpifnxnw6laU50l1sb1YyP2vNgfsG2ijBgvubO2VYW9kX/IDnYttCCCQPAIaIdzSE1nF8KHRMbl38wEbgr5HrVlYYNtmXSnKSZtS0Mi6n2UEEEAAAQQQsAsEDguw92EtSQW+8IUvSHNzs5x++umuF/5obLRP4fAnbGpq8t/EOgIIIIDANAiU5KTLfs9RMuK1z8XTqBmNAvzJxvrJV93S2C0HzdTeBSanlpM2Omam+bX0yaoF+UThOAGjDwJJLjDiHZf6tr6I7vIN82vCXabwR8+gPaL43WtDR/9p7j8aAggggAACCDgXYADQuVVS9dy4caPcfvvt4vF4RAt/uD19oqqqKqm8uBkEEEAgUQU0eX5ZXqY0dg5OuYXTTWXN32zaL30mWf9Ee8BEAV59eu3Eati/e4e80tAxKNUl9mjCsAfSAQEEkk5gl8n7pz8MRNLufemA3LfVHn18XFWhrCjPD3qawmxNb0Dhj6BA7EAAAQQQQCCAAFOAA6Ak+6aRkRG59tprRX9x/fSnPy2rV69O9lvm/hBAAIE5LVCWn2F+6JlKkO5JkfUrymw7njBVOAdGjgwI2nYGWdGCIF0D9txdQbqyGQEEklRAo4e7/aL4wt3qX7c0yd0v2GeNaMFfjf4L1Sop/BGKh30IIIAAAggEFCACMCBLcm/81re+Jdu3b5fq6mq58cYbp+VmGxoaQp5XpwCvW7cuZB92IoAAAgi4I5DhSZViMxW43S/Bvp79/JXz5S9bDk5W6xwaHZdHt7fJm9dURPTiGvlzjMnXpa9FQwCBuSXQb6KItTBQJO2h15rlV8/um3LIR85cLDUlgSuYa+f8LI/kE/03xY0NCCCAAAIIhBNgADCcUJLt14G/b3/72767uuWWWyQnJ/j/YMVy6wsXLozlcI5FAAEEEHBZQIuBBBoAnJebIWtriuXZPR2Tr/iHFxvkxEVFUl4QuIDIZEfLAvkALRgsIjCHBMbGTS5Q8wOA+ctxe3xHm9z51N4p/a8+vUbOWW6PSvbvtLCQdAP+JqwjgAACCCDgRIABQCdKSdTn+9//vugU4MWLF8vAwID87ne/m3J3W7dundz2yCOP+AqF6IbLLrts2gYMJ1+QBQQQQACBaREoyEqT7PRUM713atXei1dX2AYANQrwh4/ulK9etko8qc6zhZAPcFoeHSdFIK4F9rb3y2CA95VgF/307na57YndU3a/9+RquWBl+ZTt1g15mR4pMPn/aAgggAACCCAQuQADgJGbJfQRw8PDvuuvr6+X97znPWHv5etf//pknz179jAAOKnBAgIIIJB4AhrRV9/WP+XCl5fnyTnLSuUxE5Uz0Xabfpqb6z3rqic2Ofpb8wHqFL3C7HRH/emEAAKJK9DeNyytPYf/39LJXbywr1N+9Oguk4fa3vuKExbKpWsW2DcGWFtI7r8AKmxCAAEEEEDAmYDzn/WdnY9eCCCAAAIIIBCnAjrd15MaoBqIud6rTquRCr8pv39++aBsPdAd8d1oPsBh79RIw4hPxAEIIBC3AvpvfM+hqT8oBLvgLY1dcvPDO2TMb/TvMpNv9IoTKoMdNrk9N4MfFiYxWEAAAQQQQCAKAQYAo0BL5EN+/vOf+6r/agXgYH+shUEeffTRyX41NTWJfOtcOwIIIDDnBVJNec1SMwgYqGWmpcon19eJ9ploGqTzo8d2Sc/Q6MQmR39P5APUzxkaAggkn4D+297d2i/6b91J29bUI999cId4/RIFXmCKEGmU8VGBypT7nZjKv34grCKAAAIIIBChAAOAEYLRHQEEEEAAgUQW0GIgwVrtvBx5z1r7lN+ugVG57fF6349BwY4LtF3zATZ2DgbaxTYEEEhwgYPdQ9I96OyHgV2tvfKfD2yXkbFx211r2gGNPHYy+JeTcbiSue0ErCCAAAIIIIBARAIMAEbERWcEEEAAAQQSWyDLFAIpDJFE/+JjymVNZYHtJl/c3ykPvdZi2+ZkRQcAuwZGnHSlDwIIJIhA37BXGjoGHF2tFgj5zn3bRQsLWdtpS0rkI2culhQHkX96XGVhlvVwlhFAAAEEEEAgCgEGAKNA4xAEEEAAAQQSWaA8RBSgfiH/+DlLTCEPe6XNXz27T/Y7/NJvtSEfoFWDZQQSW2DMTOHVf9NOZvc3dg7It/62Tfr9KgSftKjI9x6TYkk3EEpFf7QozqGoUCgj9iGAAAIIIOBEgAFAJ0r0QQABBBBAIIkENAIwIy34/wJoBd+Pn73Ydsea6+uWR3bKiNceyWPrFGBFjzs8YOAsV1iAU7AJAQTiREAj+gb9BvQCXVpLz5B80wz+aSoAazt2YYF86rw68aQEf/+x9tdljf5zMk3Y/zjWEUAAAQQQQMAu4PzT134ca0ks8NWvfnWy8Mc555yTxHfKrSGAAAJzU0C/TIeKAlSV46qK5OLV5TYgndL7P8/ss21zstIzSD5AJ070QSCeBTr7R6S1ZzjsJeoA4X898LqZ/m/PEbiyIl8+c/5ySUt1/vUj0/xQMS+X6L+w6HRAAAEEEEDAgYDzT2AHJ6MLAggggAACCCSGQGlehsm/FfpatTrnopJsW6eHt7XIc3s7bNucrBzoIh+gEyf6IBCPAl5TwKP+UH/YSxs3c4NvNZXD9d+7tdWV5crnLlgu6Z7IvnoQ/WdVZBkBBBBAAIHYBCL7FI7ttTgaAQQQQAABBOJEQKNw5plBwFBN+3xyfZ1k+H1p/8kT9dLeFz4SyHpuzRm2u61fxk0OMRoCCCSWwN72AUfT///00gF5fl+n7eaqirPlixetEM3lF0nTNAX6QwUNAQQQQAABBNwRYADQHUfOggACCCCAQMIJhJsGrDekETgfOLXGdm9aBfTWx3ZHPJin+QMP9Uc2cGh7YVYQQGDGBbSSd1tv+H+3L5qBvz+80Gi7vpyMVPns+cskJ8Nj2+5kheg/J0r0QQABBBBAwLkAA4DOreiJAAIIIIBAUgnol/K8zPBfzM9dXion1xbb7v21ph75v5cP2rY5WWnqGnLSjT4IIBAHAjr1VyN3w7WDZsrvDx/dJdb4XpNqVD5lIojnh6g6Huy8OlW4NJfov2A+bEcAAQQQQCAaAQYAo1HjGAQQQAABBJJEwMmXcy0a8uEzF09Jxn/3Cw2ys6U3IokBUyBAiwnQEEAg/gWcTP0dGPHKdx96XQZHx2w39E8mh+iahYW2bU5XFhRmSkq4JKVOT0Y/BBBAAAEEEPAJMADIfwgIIIAAAgjMYQGtsJnuCVMNxPjkmmjB685dKhrVM9E0nZ9G/egAQCTtYLe9QEAkx9IXAQRmRsDJ1N/DRT92y0G/yN7TlpTIm4+piOpC9f1ofl5mVMdyEAIIIIAAAggEF2AAMLgNexBAAAEEEEh6AY3uK3P4ZXtFeb68/fhKm0mryQ3204318oZW+XDYega90js06rA33RBAYKYFnE79/eOLB+QFv6IfWjn82rMWmx8LLL8WRHADFQVZRP9F4EVXBBBAAAEEnAowAOhUin4IIIAAAggkqUBZfoYtsi/Ubb7t+IWyfH6ercsz9R3ywKsttm3hVpq6yQUYzoj9CMyWwL6O8FV/n9/bIfe8aC/6oZHCWvQjwxNZxd+J+0xLNdF/UeQMnDievxFAAAEEEEAguAADgMFt2IMAAggggMCcENAv68U56Y7uNdXk5dKpwDnp9i/4v3p2X0T5ADtMHsAhv5xhji6ATgggMK0COvW3tSd01d8DpuiHVgK3Ng34u/68Oil1GFFsPXZiucJUHdf3GBoCCCCAAAIIuC/AAKD7ppwRAQQQQACBhBOIJOqmNC9DPn7OUts9jpmEgP+9Yaf0OJzaqzOGtXIoDQEE4kfAydRfX9GPB6cW/XjfyYtkdWVB1DezsChLKs0AIA0BBBBAAAEEpkeAAcDpceWsCCCAAAIIJJRAQVaa5GTYo/pC3cCJi4rkLccusHVpN1F9P3pkl4xrdRAHrc3kDxzxjjvoSRcEEJgJgXBTf7Xox49M4R//KfynL50nF68uj+oSPWba74ryPKkqzo7qeA5CAAEEEEAAAWcCDAA6c6IXAggggAACSS9QHmHurXedVCUrK/JtLlsOdMsfXzpg2xZsRccJW3rIBRjMh+0IzKSAk6m/97zQKC/u77JdVo0p+vGRM2ujKvqRbVIJHGOiBoscpiCwvTArCCCAAAIIIBCRAAOAEXHRGQEEEEAAgeQV0Km9eZkexzeoubo+uX6pFJroQWv7oykMsKXRPkhg3W9d1gFAnT5MQwCB2RNwMvX3uT0dUwb39f3iM+cvj6roR0luum/KcGaa88jj2RPilRFAAAEEEEh8AQYAE/8ZcgcIIIAAAgi4InCUyeK/pDRXIsnBX5idLp8yif+tx+hw3i1mKnB7X+hCAnrRo2NviE4FpiGAwOwJhJv629g5ILc+vst2gfpv/v/5in5k2LaHW9FiIYtM1OAyU02cgh/htNiPAAIIIICAewIMALpnyZkQQAABBBBIeIEsMyUv0lxcR5tpwFeurbbde9+w11cURCOLwrWD3YPyhlYFoSGAwIwLhJv6O+wd8/1bHhq1/1t+3ymLZOWCyIp+pJl8f0eX58sCin3M+HPmBRFAAAEEEGAAkP8GEEAAAQQQQMAmUFGQGdFUYD340jUVcpIpDGJtO1v75NfP7rduCrg8bAYWtIAIDQEEZlbAydTfXz2zTxo77RW7z6ybJxetiqzoR26GxzfltyDbnjJgZu+YV0MAAQQQQGDuCjAAOHefPXeOAAIIIIBAQIFopgLrMR87e4mUmTyC1nb/q83y9O5266aAy01dFAMJCMNGBKZRYH/HQMhK3JtM3r+Ht7XarmCRqdb74TMWR1T0Q/OLrlqQL+T7s1GyggACCCCAwIwKMAA4o9y8GAIIIIAAAokhEM1U4BwT4fP/3rRMdJqftf1k42450GWPILLu12WdMtw9MOq/mXUEEJgmgX7zb641RP7NQyaH50+e2G179QxPinzS5P1LN387aZrvr3ZejiwtM7lFrYlCnRxMHwQQQAABBBBwVcDZp7erL8nJEEAAAQQQQCARBKKZCqxf9q8+rdZ2e5o77OaHd8jQ6Jhtu/+K5gKkIYDAzAho9F+w1JtamftHj+6S/hH7v9mrTquRSof5+7TAh+YHLTcpBWgIIIAAAgggMPsCDADO/jPgChBAAAEEEIhLgWimAuuNnLO8VM5eVmq7J80hdseTe0IW++gyEYAalURDAIHpFdBoW/33Fqzd+9IB2d7ca9t96uISOcfv37Wtg2VFI/+Wzc+Vgizy/VlYWEQAAQQQQGBWBRgAnFV+XhwBBBBAAIH4FohmKrAOHF59eo1Um1xh1vbkrkNT8olZ9+tyE1GA/iSsI+C6gEb/BWvbm3rkjy812naX5mbIh86odZz3b0lprhRmp9vOwQoCCCCAAAIIzK4AA4Cz68+rI4AAAgggEPcC0UwFzvCkmnyAdZKVlmq7v18+vVd2t/XZtllXDvWNhJ0qbO3PMgIIRCbQZvL+ac7NQK1vyCs/NFN/rVODNXXfP69fKprj00mrmZctWvSDhgACCCCAAALxJcAAYHw9D64GAQQQQACBuBPQiD5fEn8zEBBJqyjI8lUGth7jNbnFQk0F1oGH5m4qAlvNWEbALYFx8++voTNw9N8b5h/fTzfWS3v/iO3l3nlilZnOm2fbFmxF8wPqv3saAggggAACCMSfAAOA8fdMuCIEEEAAAQTiTiDTRPJVl9in9Dq5yHW1xXLJMRW2rnsO9csL+ztt26wrWpnUOzZu3cQyAgi4INDSOyTDpihPoLZhe6ts2tth27XSFPF4y7ELbNuCrZTlZ0T1HhHsfGxHAAEEEEAAAXcFGAB015OzIYAAAgggkLQCGtmTl+lsGqAV4T3rqkxUkL0S6B9eaAxaEEQrkDb3EAVoNWQZgVgFdFD9gCnGE6g1mJyAOj3f2nLNlN/rzl0qKToHOEwrzkmXxaYCOA0BBBBAAAEE4leAAcD4fTZcGQIIIIAAAnEnoFOBUx0MCFgv3JOSIm8/YaF1k+xrH5Dn9waPAmwxA4A6XZGGAALuCBzsGpLRsan/pka843LLIzun7PvY2UtEB/bCtfwsj9SZ9wVNFUBDAAEEEEAAgfgVYAAwfp8NV4YAAggggEDcCehU4KriyHN8nba4RBYU+kUBvtgo49ZqA5a7HfG+IYf6hi1bWEQAgWgFhr1jQSts/+rZfSYvoD0y8MJV5XLioqKwL5eTkSrLTX5AJ1GCYU9GBwQQQAABBBCYVgEGAKeVl5MjgAACCCCQfAI6FVijfiJpOkDw9uPtUYD7zbTD5/xyjlnPqYMSXQP2ggTW/SwjgIAzgYaOQTPYPrWv/vt76LUW247q4mz5p3XVtm2BVjLSUmRFeb54Uvk6EciHbQgggAACCMSbAJ/Y8fZEuB4EEEAAAQQSQGBJaeRTgU81UYBaJdTa7jG5AINHAY7LtqZe2Xqgm4FAKxrLCEQgMDDiDRhN224ibG97YrftTOlmMO9T6+sk3RP6K0K65yjRAiHh+tlOzgoCCCCAAAIIzKpA6E/3Wb00XhwBBBBAAAEE4lXAVxXYRApF0jQK8IoTKm2HaJTfpj32yqO2Dmald8g7ORDYPTDqv5t1BBAIIaCRtv4z7TW/5g8f3SX9w2O2I686rUYqi+yD9LYOZkVzgC43kX/6HkBDAAEEEEAAgcQRYAAwcZ4VV4oAAggggEBcCZSbyr7zcsMXCbBe9MkmCnCh3wDDPSFyAVqP1YHA15p6fBGBDARaZVhGILBA9+CodPZPHTT/vy0HZXtzr+2gk2uL5dzlpbZt/ita/0dz/mmFYBoCCCCAAAIIJJYAA4CJ9by4WgQQQAABBOJKYLGZCpyd7jwSKMVUCr3CryJwo4kCfLa+3fF9MRDomIqOc1xgv6m27d80r+afXjpg26wD+R85c3HYSr5LTLXfguw027GsIIAAAggggEBiCDAAmBjPiatEAAEEEEAgLgV0OuAyExHkSTWhQQ7bOhNpVOU3ffieFw+ITkuMpE0MBL56sFs00omGAAJHBLSKdt+w98iGfyzdawb/hr3jk9vNmLz887l1khMmqk8jd+flZkwexwICCCCAAAIIJJYAA4CJ9by4WgQQQAABBOJOIMtEAGpREKftcBRgpa37ga5BeTqCKEDrwT2DZmrwwR4zpbHHupllBOaswBsm6V+Dyf3n35q7h2TDtlbb5rPqSk1OvzzbNv+Vopy0KVP3/fuwjgACCCCAAALxLcAAYHw/H64OAQQQQACBhBAozkmPaIBgbU2xVPtFAf7xJVMROMIoQCuO5joLFPFk7cMyAnNBoLlnSIZGj0T5TdzzXc83yJilIkiaidx954kLJ3YH/DszLUWWmgH+ozRUkIYAAggggAACCSvAAGDCPjouHAEEEEAAgfgS0CmChQ7zg2kU4Dv8cgEe7BqSv0cZBTgh0W6mPdIQmMsC3rFxOWDyavq3+ra+KVG2F64ql5IQ03oPV/zVKf58ZfD3ZB0BBBBAAIFEE+DTPNGeGNeLAAIIIIBAnApohFCdKRKQYSKGnLSTaoqkpiTb1vWPpiLwWAxRgIf6RkSnP9IQmKsCTWaa7+jY1H8Dv32uwUaixXveeqx9Kr6tg1lZav49Z6dT8dffhXUEEEAAAQQSUcDZ/6En4p1xzQgggAACCCAw4wIaKbTcFAUxtUHCNh0wvMJv+qEOXvx996GwxwbrMGKKG/QMTS18EKw/2xFIJoFh75jovyH/tqWxS7Ye6LZtfuuxCyQ3M/jgnkb06tR+GgIIIIAAAggkhwADgMnxHLkLBBBAAAEE4kZAq4kudlgU5MTqIqmdl2O79j+aisCxRQEyDdgGysqcEWg0U3/9/+2Mm4jY327abzPQgb2LVlfYtllXKPph1WAZAQQQQACB5BBgADA5niN3gQACCCCAQFwJlOZlSHlBZthr0ihA/1yAWsDgyV3RRwF29o/EVEwk7EXTAYE4E9Bp73sO9Utrz9TB76d3t8vedntFYP03l+4J/DWAoh9x9nC5HAQQQAABBFwSCPzJ79LJOQ0CCCCAAAIIzF0Bze+XF2KK4YTM8dWFsqTUHgV4r6kI7B/JNNE/3N+a/6x7cDRcN/YjkBQCo6box2tNPdIcYOqvFgTRyr/WVlmYJWctK7Vumlym6MckBQsIIIAAAggknQADgEn3SLkhBBBAAAEE4kNAo/uWmXyA6Z7QCQF9uQD9KgK3mEimjTvbor6RQ1QDjtqOAxNHoH/YK6+Y3H49g4HzXm7Y3iqtvfaowHevrRId6AvUdCCeoh+BZNiGAAIIIIBA4gswAJj4z5A7QAABBBBAIG4FdJphnRkENGOBIdtxVYW+iqPWTve+dEC84+PWTY6XOwdGo44gdPwidERgFgV0kFsLewyPBv43MjgyJlpV29q0SvdJi4qsmyaXtehHSW7G5DoLCCCAAAIIIJBcAgwAJtfz5G4QQAABBBCIO4H8zDSpKbFP8fW/yEBRgBq59MSO6HIB6vThDpMLkIZAsglovr/9JqffzpY+Mf+ZB21/feXglIrY/7Su2gzGTx2Np+hHUEZ2IIAAAgggkDQCDAAmzaPkRhBAAAEEEIhfAS0IUpqXHvICj11YIBqhZG2aC1DzmEXT2vvtUx+jOQfHIBBPAvpvYXtzrxzoGgx5WV0DI/LXV5psfU4wFbdXVOTbtukKRT+mkLABAQQQQACBpBRgADApHys3hQACCCCAQPwJ1M7LDZkPUCOT3nHiQtuFH+obkZ88UR+wwIGtY4CVLjMNWAsk0BBIBgGd0qv5/vS/63BNp88PWaYGa9DflSb3n3+j6Ie/COsIIIAAAggkrwADgMn7bLkzBBBAAAEE4kpABxsWFmWHvKZjKgtM4RB7FODGXYfkM3dtlu8++LpsM9VOdQqkk6bdmAbsRIo+8S6g/x3r4J91UC/YNbf0DMmGba223WfVlUpV8dR/e4sp+mFzYgUBBBBAAIFkFmAAMJmfLveGAAIIIIBAnAmU5WWYKqOpQa9KowDfeeLUSCUd8nt+X6f8+19ekxv+tFWeNIOCTqYGt/lVQA36wuxAIE4FGjsH5HUz7VfzWjppv3++QcYsg+RpqVMja/U8+VkemUfRDyek9EEAAQQQQCApBBgATIrHyE0ggAACCCCQGAI6wLeoZGokkvXqV5sowLcdXykmYDBg23OoX3706C751O9ekv/dfED6hrwB++nGXrNvaHQs6H52IBDPAjtbeqWhI3S+P+v117f1ydO7262b5MJV5QEH+qoDRATaDmQFAQQQQAABBJJKgAHApHqc3AwCCCCAAALxL1CYnS4FWWkhL/RdJ1XJf195vFy6pkKy0gJHDHaaXGi/e65B/vm3L8qdT+2RpiCFEdqpBhzSmp3xKdA7NCqaAzOSpv8erE2jbd96bKV1k2+5OCdd8kx1bhoCCCCAAAIIzB0BBgDnzrPmThFAAAEEEIgbAY0C1MIEoZpOT3zvyYvkR/90glx16iLR6cOB2rB3XB56rUU+e/fL8r2HXpdOUwHV2tr7qAZs9WA5MQQ0l18kbUtjly9PoPWYtx67QHIzPdZNvn93VcVZtm2sIIAAAggggEDyCzAAmPzPmDtEAAEEEEAg7gRyMjxSGmRAz/9is0wU00WrK+T77zpOPvOmZbJ8fp5/F9+6Zkh7bm+nr2qwtUP/8JgMjASfJmztyzIC8SAwYga1I4n+Gzc5//yj/4qy0+TC1eVTbkcH1rPT7YOCUzqxAQEEEEAAAQSSToBP/6R7pNwQAggggAACiSFQZSoCt5spjk6LG6SYpIBra4t9f3abXGd/e6VJnqlvF//aCJsbusx5h6XEUuBAXye7mP/tSYz/MrhKjf6z1PEIC/KUKYqjuTGt7R2mmE6Gxz59XvNqLiwi+s/qxDICCCCAAAJzRYAIwLnypLlPBBBAAAEE4kwg3ZMiFQWZUV3VktJc+eT6OvmByRN4mckTqJVOre3vfoUQDjEN2MrDchwLjJsR7dZe59N/Nzd0yk831tvuaEFhppy9rNS2TVfm52dKZpCcmlM6swEBBBBAAAEEkkqAAcCkepzcDAIIIIAAAoklsKAwS3QgMNqmUX7/ZPIEnr5knu0UGhFlbUOj46Yi8Kh1E8sIxKWAFq0Z8eqE9vDt+b0d8t0Hd8jomL3/lWurJdWvjLauVxL9Fx6VHggggAACCCSpQPT/x52kINwWAggggAACCMycgA5KuFGQ4PSl9gHAfR0D0mD+WFskOdWsx7GMwEwKNHc7i/7T6e83P7xTvH5z4PXfwkmLiqZcskbbpqXyv/5TYNiAAAIIIIDAHBHg/wLmyIPmNhFAAAEEEIhXgVITxZeTYc9VFum1rqzIFy16YG1/322PAuzoHzZ51eyRUtb+LCPw/7d3H3ByVfX//w/Zze4mm957hYSEEEF6l95UmiBYAGkK+hX5y1ewwc+CggIqqICIFL9SFGkCUgSkhBKqEkogvSebnmySTXYz//O+cCb33pk7ZXd2997Z13k8NjO3nXvu80y5+cwp7S2gVqrrG/JPWPP8h3Xmuqc/NE2h1/MBNvh3/kFj7Uy/wS7x6iKv1rYkBBBAAAEEEOi4AgQAO27dc+UIIIAAAgjEQkDBipF9altUFk0Qsk9GN+AVgYCfulWu2Ug34BZBc3CrChTS+u+Z6cvMDf+emTFJyMHjB5iv2eBfuOuvCqyuv9nWt+rFkDkCCCCAAAIIxEqAAGCsqoPCIIAAAggg0DEFetrWe71rgy34ipXYP9QNuM5O/PHhsvWBbOgGHOBgIUYCDY1NRuP/5UpPvrvE/OG5WSbcjvWIiQPNOQeMNgqEh1N1505mYPfmTbYTzotlBBBAAAEEEEiuAAHA5NYdJUcAAQQQQKCsBNQKMNRzsajrG9W3qxkSmlX4hdBkIKs2bDaaZZWEQNwElq1VF/XoUj369mLzpylzMnY4ZufB5sx9R5lOEW+eYbb1X7bAYEZGrEAAAQQQQACBshYgAFjW1cvFIYAAAgggkByBLlUVZmCP5rdUUlfi8GQgmiihcevWNEKjnS1VQUASAnESUFB62broyT8eemuh+fPLczOKfPwuQ8yX9hqRMeaf27GrfU9pjE0SAggggAACCCBAAJDXAAIIIIAAAgjERkCtlSrthAXNTeEA4LpNjebtBWsC2eXrZhnYmQUE2kBguZ2gRmNUhpMmrbn39QXmrlfnhzeZz+02zJyy+/DI4J8OGN6na87tGZmyAgEEEEAAAQTKVoAAYNlWLReGAAIIIIBA8gQ6V3Rq0WylakG4/YBugQufMnNFYHmVHWetsWlbq8DARhYQaAeBpWsaMs6q4N89r803f39jQca20/YYbk765LCcwb3uNZWmT21VxrGsQAABBBBAAIGOKUAAsGPWO1eNAAIIIIBAbAUG2yCeJi5obtovNBvwa3NWmk1bmtLZaQjAlXQDTnvwpH0F1m7aYtY3NAYKoeDf/9kuvw++tSiwXgun7zPSfHaXoRnrwytG2DExSQgggAACCCCAgBNo/t21y4FHBBBAAAEEEECghAKasGCE7brY3LT3mD52QoRtRzc0bjWvz121bYV9tnwd4wAGQFhoN4ElazLH/nvwP4vMo9OWZJTprP1Gm6MnDc5YH16hGbV71LRsVu1wniwjgAACCCCAQLIFCAAmu/4oPQIIIIAAAmUp0M9OXKAujM1JvbpWmUlDewYOnRKaDVitrjbbwCAJgfYUaGhsMittl3R/qretAR94c6F/lVE8+6sHjjGHTxwYWJ9tQZMBD+/d/AB6tjxZhwACCCCAAALJFyAAmPw65AoQQAABBBAoS4FR/WpNbXVFs65t/+37BY77r50IREE/l2wPS7PCTrxAQqA9BTT2n16L/vTv6XVGrVZdUkDvgoO3N58aP8CtyvnYr1uVfd80L3ieM2M2IoAAAggggECiBQgAJrr6KDwCCCCAAALlK9DNBjEmD+tldh7W0wzqWVPU7MC7j+xjquyEIi412SjLK7OCk4HQDdjp8NgeAlvtYJTL1gW7/2rd4+8Eu/7uOaqPCQe0o8qrYOEwWv9F8bAeAQQQQACBDi2w7c64QzNw8QgggAACCCAQVwEFAkfb1oC7jehtdhjYzfTs0tnOfpq7tF2qKsxuI3sHdpoyIxgA1MQLS9cGAzCBA1hAoBUFlq9vMFuags3/Xp+3ytTZ9f501KRB/sWczzULdk3n5rWazZkxGxFAAAEEEEAg8QIEABNfhVwAAggggAACHUNAk4NobMCJQ3qYXYb3si2duuScLXjf7fsGYKYvXWfqQi2u5q7YEJghOHAACwi0osCSLMHnx0ITfyjwPX5g94JKUWNnztZ7goQAAggggAACCGQTIACYTYV1CCCAAAIIIBBrAbVyGm5nCv6kbRU4cXAP0797VWDmXxV+F9t9WK0H/WnKzGArwCbb5XLGsvV2HLZgSyz/MTxHoNQCazZuMfUNTYFs566oN+8uXhtYd9ROg2xr1zzNXe0RnSu2MxPs+6Czr9t7ICMWEEAAAQQQQKDDCxAA7PAvAQAQQAABBBBItkDPrp3N9gO6m11G9Ap0Da60wZC9RvcJXJxmAw4H+9ZtajSL19AVOADFQqsKZOt6Hh77T13d9xkbbMWarVAVtmXs+EHd6fqbDYd1CCCAAAIIIJAWIACYpuAJAggggAACCCRZoLqywvSywUB/2i80G/CCVRvNvJUb/Lt4z+fbdRs3B1tkZezECgRKINDQ2GRW1m8O5KQZql+wwWl/OmzCgLwt+tQ4cIcB3Uz3muDr3p8PzxFAAAEEEEAAAQkQAOR1gAACCCCAAAJlI6AxAv1JLaP61lb5Vxm1Agwn2xOYrsBhFJZbRWDpmgbbCjWY9dPvLQtMCKJWfYdNGBjcKcuSxgjsHXp9Z9mNVQgggAACCCCAAAFAXgMIIIAAAgggUD4CvbtWGQVPXOpkm0jtG+pG+aIdB3BrOAJjD9CswAtXb3SH8ohAyQW22kjzstBENI1bt5on31saONe+Y/ra1qzBwHVgB7ugCT806y8JAQQQQAABBBAoRIAWgIUosQ8CCCCAAAIIJEJAwb8+tcHukOFuwCts98vpS9ZlvR51Ea63gUASAq0hsHx9Q6Cln84xdfbKjC7BR00alPP0/btXe5Pg5NyJjQgggAACCCCAgE+AAKAPg6cIIIAAAgggkHyBcDfgkX1rzXDbWsqfsnUD1nY1DJxZt96opRYJgVILLFmbOdnMY9OWBE4zfmB3M6Z/t8A6/4ImBxnbv9a/iucIIIAAAggggEBeAQKAeYnYAQEEEEAAAQSSJKAASVXltm7AKvu+oclAXp69wjQ2bc16WfUNTXQFzirDypYIaKIPvbb8acay9eZD++dPuVr/1VZXmHEDu9nZroOvb//xPEcAAQQQQAABBLIJEADMpsI6BBBAAAEEEEisgIIjfWuDk4HsFxoHUIGYtxasjrxGjQW4zgZsSAiUSmBZ1tZ/iwPZ97ETeuwxqk9gnVuo7tzJaFKbygpu350JjwgggAACCCBQuAB3EIVbsScCCCCAAAIIJESgnx0jzZ/6d68x6lrpTy/OWOFfDDz/qCtwPV2BAyosNFdgi21tumL95sDhK+1YlC/PWhlYd8TEgYFJbNzGyortzI42+FddWeFW8YgAAggggAACCBQlQACwKC52RgABBBBAAIEkCHSrrjRdqoLBkv227xso+utzV5mNm4NdMv07aNu8lRv8q3iOQLME6tY12Jmng4f+y8782+SbjbrKtuw7ZMcBwZ3skia1HmeD112rKjO2sQIBBBBAAAEEEChUgABgoVLshwACCCCAAAKJEuhru1P6015j+poK39hpm22rrFfnBFtg+ffX88VrNpk1G+kKHHZhuTiBpaHuv5sbt5qnbADQn/bfoZ/pXhOcwVrbxw7oZjSuJQkBBBBAAAEEEGiJAAHAluhxLAIIIIAAAgjEVqB/qBtwDxtcmTysZ6C8U2YuDyxnW9CswE3h5lvZdmQdAlkE1mzYYjZtCU4486J93a3d1BjY+6idBgWWtTCib1cTntU6YydWIIAAAggggAACBQgQACwAiV0QQAABBBBAIHkCNZ0rbIuqYLfJ/UKzAb+9cI1ZvSE4Nlv4Shts8GbuivrwapYRKEhg6bpNgf1SttvvY9OWBNZNGtLDDO/TNbBOk34M6VkTWMcCAggggAACCCDQXAECgM2V4zgEEEAAAQQQiL1AuPXUbiN724kUtt3+aAi28EQM2S5q6dqGvIHCbMexrmMLqKuvJvvwp/eWrDNzQ2NLHjVpsH8X7/lgG/zTjNYkBBBAAAEEEECgFALb7oBLkRt5IIAAAggggAACMRLo263KBlG2FUitAncf1WfbCvvsqfeXmsatwS6agR0+Xpi1vN6o9RYJgUIFltnWf+GXzGPTFgcOH9ij2uw6oldgXYWd+aN/t+BM1oEdWEAAAQQQQAABBIoUIABYJBi7I4AAAggggEByBDrbmVV7dQ1OoLDf2OBswAtWbTSP/jcYlMl2heoKzIQg2WRYl01AwWK1HPWnOhsQfM3OPu1PR9qx/zr5o9R24wA7fmWlfe2SEEAAAQQQQACBUglwZ1EqSfJBAAEEEEAAgVgK9K0NtqT6xLBeGeOt3fvGAjvj78a85V++PhjQyXsAO3RYgVV28g91Afanx99ZGmgR2MW2SD1oXH//Ll6L1UGM/RcwYQEBBBBAAAEEWi5AALDlhuSAAAIIIIAAAjEW6FNbZdSl0qVO9vl5B4w229YYs6UpZf74/Oy8XXxX1m9hRmAHyWNOgaVrg5N/bNrSZJ6ZvixwjIJ/XauCE9X07lpl1FWdhAACCCCAAAIIlFKAAGApNckLAQQQQAABBGInoOBfn9pgN+DtB3Q3R04aFCjru4vXmn9PrwusCy80bU1lTOoQ3odlBBTsC3cXf+7DOrNhc1MaRwFodf8NJ1r/hUVYRgABBBBAAIFSCBAALIUieSCAAAIIIIBArAXCswGrsJ/ffbjpZycJ8ae/vDLXrNoQnLXVv13P6QYcFmE5LLDMjv3nn/xjq114fNqSwG6a+CMc7KutrjA9uwSD1YGDWEAAAQQQQAABBJopQACwmXAchgACCCCAAALJEVBQparS3+nXeN0sz95/dOAi6m0LrdtfnBNYF15Qy67w2G7hfVjuuAJbbSvRuvXB7r//XbDGLFoTXHfUpMEZSOGAYMYOrEAAAQQQQAABBJopQACwmXAchgACCCCAAALJEdjOzrIangxEpd9leG+z3/b9AhfyyuyV5tU5KwPr/Atq2bWinslA/CY83yaw0rYg3dxoXyS+9Ni04CzTw3p3MZOG9PDtYbwAdb/QhDWBHVhAAAEEEEAAAQRaIEAAsAV4HIoAAggggAACyRHoG+ru60p++t4jTbfq4EQMt06Zbcdra3S7ZDwuX5e7m3DGAazoMALhyT8WrNpg/mNbAPrTUXbsPwWl/WlA9xqjCWpICCCAAAIIIIBAawgQAGwNVfJEAAEEEEAAgdgJdK/pbLv9Zt769LDdg0/fZ2SgvKs2bDF3TZ0XWOdfWN/QaDb6JnTwb+N5xxXQa2LtxmDg+NG3g2P/Kdi8/w7BVqeK+w3sUdNx4bhyBBBAAAEEEGh1gcy74FY/JSeIg8Brr71mfvzjH5sjjjjCDBs2zFRXV5tu3bqZcePGma985SvmhRdeiEMxKQMCCCCAAAIlFcg2GYhOsL/tBjx5WM/Auf713jLzvp0ZOCoxGUiUTMddH279p/EiX5gRnFn68IkDTXVlRQCpb7dq2wWY2/IACgsIIIAAAgggUFIB7jRKypmMzA488ECzxx57mMsvv9w8+eSTZuHChWbz5s2mvr7efPjhh+a2224zBxxwgDnjjDO89cm4KkqJAAIIIIBAfoH+3auz7qTumOfYCUGqQ0GYm1+YFTnhR916xgHMitlBVzZ5k38EXxNPvrvEbGnaNh5gpW3qd4QNAIbT4J60/gubsIwAAggggAACpRUgAFhaz0TktmjRIq+cQ4YMMRdeeKG59957zdSpU81LL71krr32WjN06FBv+x133GHOPPPMRFwThUQAAQQQQKAQgZrOFaZ7TXC8P3dcfzsG2ym7D3eL3uOi1ZvMg28tDKxzCw1btpq1m7a4RR47uMAKGxBu9AX7NFP0k+8uDahowpleXasC63p0qTS1oTEoAzuwgAACCCCAAAIIlECAAGAJEJOWxY477mjuueceM2/ePPPrX//anHTSSV6LwL333ttcdNFF5q233vK6Auu67rrrLvPcc88l7RIpLwIIIIAAApECUZOB6ABNzjC2f23g2AffWmTmrdwQWOcWlq8Ltvhy63nseAJL1wZfC8/brr9rNwXHAzxm58EZMIN7dslYxwoEEEAAAQQQQKDUAgQASy2agPwefvhhc8opp5iKiuD4M67o/fr1M9dcc41b9FoIphd4ggACCCCAQMIF+tZW2xlYs1+EZmE994AxpsK3Q1MqZW5+fpbZart4htOK+s1Z14f3Y7m8BTQpjP5c2mpfM/8MTf4xeWhPM6JPV7eL91htJ6Xp3bVzYB0LCCCAAAIIIIBAawgQAGwN1TLI8+CDD05fxcyZM9PPeYIAAggggEDSBTTZQk87829UGtm31nzmE0MCm2csW2+esOO5hZO6fK62Ez2QOrZAePKP/8xfbRau3hhAydb6b5Cd+VfjT5IQQAABBBBAAIHWFiAA2NrCCc2/oWFbN5aoloIJvTSKjQACCCCAgImaDdjRnLDrUBOemOHuV+ebuixdfpkN2Kl1zMfGpq1mxfrNgYt/9O3FgeVhvbtkzDJdYVubDoiYlCZwMAsIIIAAAggggEAJBAgAlgCxHLN49tln05c1YcKE9HOeIIAAAgggUA4CfWqrjAIwUUmtBNUV2J8a7KQOf5oy26Rs905/WmW7ASsIROqYAstt8E8zALs0Z0W9mbZorVv0HtX6L9zSTzNSV1ZwKx6AYgEBBBBAAAEEWk0g+zR4rXY6Mk6CwNatW82VV16ZLqrGCyw2LViwIOchixcHfxnPuTMbEUAAAQQQKLGAgn99ajvbFn3Bllv+00wY3MMcuuMA89T7y9Kr37JdO6fMXGH2t7O5uqTYj8YCHGi7c5I6nkC4+2+49Z+6m/tfLxJSr99wC9OOJ8cVI4AAAggggEBbChAAbEvthJzrV7/6lZk6dapX2hNPPNHstttuRZd8+PDhRR/DAQgggAACCLSlgLoB5woAqiyn7TnCvD5vlVm9Yds4f3dPnWf2GdM30IJQXYMJALZl7cXjXGs3bTEbNjelC7PSBoJftAFifzpi4kDTOdTSr5ed+KOmc/bJ2PzH8hwBBBBAAAEEECiVAP0OSiVZJvmo6++ll17qXc2AAQPMDTfcUCZXxmUggAACCCAQFOjVtcp0r8n9W2htdaU5a9/RgQPV2u+1OSsD69ZtajSbtmwLBAU2slC2AsvWbgpcmyaK8XcH7lyxnTnMBgDDaXCPLuFVLCOAAAIIIIAAAq0qkPuut1VPTeZxE3jnnXfMCSecYBobG01NTY3529/+ZhQEbE6aP39+zsPUBXjPPffMuQ8bEUAAAQQQaG2B0f1qzdsL19hx/aLPtMfoPmaHAd3Mh3YmYJcee2eJ2cu2AvQnTQYyrHdX/yqel7FAfUOj0fh/LikA/K/3lrpF7/HAHfqbHjXBGadrqytMT9sCkIQAAggggAACCLSlAAHAttSO8blmz55tjjjiCLNq1SqjWX/vvvtuc+CBBza7xMOGDWv2sRyIAAIIIIBAWwmohd8gO3bf4jXBllzh8x+50yAbAJyRXv3+knVmrp3sYWTf2vS7WGwWAABAAElEQVQ6BYMIAKY5yvqJJoKZWbc+EDh+7oM6U98QbAWqyT/CSa83EgIIIIAAAggg0NYCdAFua/EYnm/RokXmsMMOM3rUDHV/+tOfzHHHHRfDklIkBBBAAAEESi8wrHcXU1UZPSOwzriXbQXYy07m4E+P21aA/rTRjgW33rYKI5W/wIJVGwPBvq12JphHpwUnOPvkiF5mSK9gV191CdbYkyQEEEAAAQQQQKCtBQgAtrV4zM63fPlyc/jhh5tZs2Z5Jbv++uvN6aefHrNSUhwEEEAAAQRaT6DSTtDgb8mX7Uza59AJwbHcXpix3Kyzk0D403I7GQipvAUU5F24emPgIjVRzNK1wbo/NkvrP00U08nOQE1CAAEEEEAAAQTaWoAAYFuLx+h8a9asMUceeaR59913vVJdeeWV5utf/3qMSkhREEAAAQQQaBsBtcrqGWrhFz7zYRMGBGb+3dKUMs+8vyyw24r6BtstNMeAgoG9WUiagFr6zbRjQYar+JH/Blv/jerb1UwY3CNweYr7MVN0gIQFBBBAAAEEEGhDAQKAbYgdp1Nt2LDBHHvsseaNN97wivX973/fXHLJJXEqImVBAAEEEECgTQU0IYgdCSMyadbgvUMTfzzx7tLArK+bG1NmzcZgq8DIDNmQOAF1/d1gu3r70wwbEJy+dJ1/lTl28hBvWBX/SgX/qiq59fab8BwBBBBAAAEE2k6Au5C2s47NmTZv3uzN9jtlyhSvTBdeeKH56U9/GpvyURAEEEAAAQTaQ6BLVYUZ0jM4Zlu4HEfZyUD8aUX9ZvP63FX+VXZm2GBX0MBGFhIroK6/i9YEu/7qYh59O9j6r0+tAsV9Atep1n/h8QADO7CAAAIIIIAAAgi0sgCzALcycByzP+2008wTTzzhFe2QQw4xZ599tpk2bVpkUauqqsy4ceMit7MBAQQQQACBchEYaicEqbMBvM2NW7Ne0vYDuhn9qdWXS4+9s9jsaScJcWll/RavVWAFY705ksQ/quuv6jzc9bfOjvn4yuwVgevTjNGVnYK/sQ+g9V/AiAUEEEAAAQQQaHsBAoBtb97uZ7zvvvvSZXj66afN5MmT08vZnowcOdLMmTMn2ybWIYAAAgggUFYCCtpp/LYPlm4L8IUvUAGeGctmpFe/t3idmbuiPj2RSJMNFq20LQP7d2e21zRSwp/MX7XBaJbncHrMzgRtqzudqm0X30N2HJBe1pOPWv/VBNaxgAACCCCAAAIItLVA8OfJtj4750MAAQQQQAABBGIm0NdOCNKra+fIUu1tW/v1Ck0Y8vg7SwP70w04wJHoBc30vHjNpoxr2LC5MWMSmIPHDzDdqoO/r6v1X3VlRcbxrEAAAQQQQAABBNpSgABgW2rH5FyanbCYP1r/xaTiKAYCCCCAQJsJaEKQqB68lRWdzKF2RmB/mjJjuVGgyCVNBBLVjdjtw2P8BbxZf+vqM7r+quTPvF9nNm7Z1ipQE8gcNSk4RiSt/+Jfx5QQAQQQQACBjiJAALCj1DTXiQACCCCAAAIFC9R0thOC9IqeEOTQCQONf4y/zU1bzTPT69L5a6y4FfVMBpIGSeiTqK6/6uatsR/9aY9RfYxm+vUndQOn9Z9fhOcIIIAAAggg0F4CBADbS57zIoAAAggggECsBYbaAGBN5+y3Sr272plefRN/6EKefHeJN/mHuyhNEKFWgRo7Tq0B1ZqMlByBtRFdf3UFU+3EH8vXbw5czLE7Dw4sf9T6LzqIHNiZBQQQQAABBBBAoJUFgoOUtPLJyB4BBBBAAAEEEEiKQCcbwRnVt9a8v2Rd1iKru+eUmdtmgFVA6I25q8weHwcG6xuazLSFawPHKiikLsSV9olaEFZW2D/v+Ufr1OrQ37IwcDALbSagFn4zs8z6qwI0NDaZv7+xMFCWHezM0OMGdg+sU+s/tSQlIYAAAggggAACcRDI/rN2HEpGGRBAAAEEEEAAgXYW6F1bZfrYv2xp+wHdzdj+tYFNmhU2V1IjQLUG3GBbBa7b1GhW1W8xdes2myV2kokFqzaa2cujZx/OlS/bSiswf+UGs2nL1qyZ3v7iHLNw9cbAtmNCrf80HmCuLuSBg1lAAAEEEEAAAQTaQIAAYBsgcwoEEEAAAQQQSK7AyL5dI1vlHblTcNKHdxevNfNs8Ki5ScHAZWszZ5xtbn4cV7yAJnDJNuuvcnr+w7rAWI9ap9eHxv/zJ1r/+TV4jgACCCCAAAJxECAAGIdaoAwIIIAAAgggEFsBdeMc2jv7WG57j+lrenTpHCj743laAQZ2zrIwe3m9bSHYmGULq1pbQF1/Z9Vlb4WpVn+3vDA7UASNEfnNQ3YIBIjV+k/jR5IQQAABBBBAAIE4CRAAjFNtUBYEEEAAAQQQiKXAYDu7a5eqzPHcOtvx/A6bMCBQ5hc+XG7W2+69zU3qJvzB0vWBCUWamxfHFSeg1pvZuv5q3L/f/OsDO/5fsFvwOfuPyejq268bY/8Vp87eCCCAAAIIINAWAgQA20KZcyCAAAIIIIBAogU0IchoOyFItnTojgNNhZp9fZw2N2213USXucVmPWrmYLUEJLWdgFpdLo3ofn37i3PNfDtGoz8dsuMAs9/2/fyrjF4GwyJaiwZ2ZAEBBBBAAAEEEGhjAQKAbQzO6RBAAAEEEEAgmQI9u3Y2vexfOGmSkL3GBMeAe+LdJWarmvK1INWtazDL1jEeYAsIizp0oQ3wpbJU2QszlmcEdIf36WrO2GdURv60/ssgYQUCCCCAAAIIxESAAGBMKoJiIIAAAggggED8BRT4yZbCk4EsX7/ZvD5vVbZdi1o3Z/kGo9aApNYVkPGK+s0ZJ1lkx/374/OzAuurKzuZbx26g6myj/5E6z+/Bs8RQAABBBBAIG4CwTuXuJWO8iCAAAIIIIAAAjES6FZdafp2q8oo0Q4Dupkx/YJdhFs6GYhOokkpPli6rsWtCTMKzIqAwMLVGzJa/2224/39+qkPM8f9OyBz3D9l1s++LjRhDAkBBBBAAAEEEIijAAHAONYKZUIAAQQQQACB2AoM793VG+vNX8DtbPOvcCvAdxatNfPtpBItTRs0HuAKxgNsqWPU8Zu2NBm12Ayn21+ak1F/B48fYPYPjfun49T6b2iv7K1Dw/myjAACCCCAAAIItIcAAcD2UOecCCCAAAIIIJBYAc0G3L97dUb59xnb1/ToEhwjsBStAHWiZWsbjMYEJJVeYEGWsf+m2HH/nn4/OJGLun+fue+orAVQ679ss0Rn3ZmVCCCAAAIIIIBAOwgQAGwHdE6JAAIIIIAAAskW0EyvdmLgQOpc0ckcameG9SdNILG+odG/qtnPNSsw4wE2my/rgR+1/gsGVr1x/17IHPfvwizj/ilTWv9lpWUlAggggAACCMRMoDJm5aE4CCCAAAIIIIBA7AWqKyvMwB41ZvGa4Cy9h00YaB56a5Fp+ng62QY7jtz//u0/ptaOHVjTuZM3cYSO1UQS3p8dMy793K4f3KvGfHJEbxtcDEUXrYjGA/xw2TozaUhP0ykcfYy9WDwLuNBO8uGf+Vfj/v3Gjvu3acvWQIHP3n+07eLbJbDOLfS1s0DT+s9p8IgAAggggAACcRUgABjXmqFcCCCAAAIIIBBrgaG2FeAy2y1XgTmX+thg0J6j+5iXZq1wq8zqjVu8v/SKPE92H9nbfNO2NlOLwnCqb2gyc+x4gGP6dwtvYrlIAbX+C3ervuOlOWZeaNzGg8f3Nwfs0D9r7l7rP/s6ICGAAAIIIIAAAnEXyLyzjHuJKR8CCCCAAAIIIBADAQXoBvesySjJUZMGZawrZsVrc1eZa5/8wKg1Wra01I4HuHx9sNtqtv1Yl1tAXX39rf807t9T4XH/bHDvjIhx/5S7Wv91reL39NzSbEUAAQQQQACBOAgQAIxDLVAGBBBAAAEEEEikgAKAnSuC3XXHDexujpg4sEXX89b81eaaJ6dHBgFn1dXbbqpNLTpHRz64oTHY+m+xDQb+Mdu4f4eNs120K7JSqRe2WoGSEEAAAQQQQACBJAjwk2USaokyIoAAAggggEAsBSptK8Ahdmy4uSs2BMr3lf1Gm2N3Hux1EdY4gAo4eY92bDk9V+u+TVpvg3har+X/LlwdGHvuvwvWmF8+Md1cfERmEErdjj9Yus7sZMcDrGA8wIB9IQuLVm8yrud249at5vpnZgTslUeucf+0feyAbrT+EwQJAQQQQAABBBIhQAAwEdVEIRFAAAEEEEAgrgKDPp4MJNxld4Bdr79CkwJ6V/7zfbPR17Jv2sI15hePTTf/e+R4O4lIsCWaxgPUMeNti0MmBSlU2XgB2GVrt03e8sCbi4xmWPanT42LHvdP+2kW6H7dqv2H8BwBBBBAAAEEEIi1AF2AY109FA4BBBBAAAEE4i6g4JsCQi1N6jr8vWMm2FZlwUDfu4vXmqseez9rl9/VG7bYmYHX27Hstk1E0tJylPvxi32t/2bVrTcPvLkwcMma7ffM/UYF1vkX+nevMsP7dPWv4jkCCCCAAAIIIBB7AQKAsa8iCogAAggggAACcRcY0L3attBr+W3V9rZb6fdtELC2OhgEfH/JR60DN2xuzKBYWb/ZzLSBLFJ+AbXSXPpx6z89v+HZmabJFzxVb+oLPjU2cty/7jWVZkw/ZmDOL80eCCCAAAIIIBA3gZbfqcbtiigPAggggAACCCDQxgLbbadWgKVpFTamfzfzg2Mnmm7VwZFapn/cRThbELBu3eaMbqxtTJCI0y1eszE99t+9r883C1ZtDJT7hF2HGvlnS9U2wKtWmnS3zqbDOgQQQAABBBCIuwABwLjXEOVDAAEEEEAAgUQI9LetAMMt95pb8FF9a20QcIJRizN/Unffnz36nqlvyGwJuGTNJjMvNBmJ/9iO/nxLk1r/NXgMGjvx4f8uDpCM6tvVHG8DgNlSpZ3pecdB3U1VJbfO2XxYhwACCCCAAALxF+AuJv51RAkRQAABBBBAICECw0vUClCXO9IGAX9oWwL26NI5cPUz6+rNFTYIuH5TZhBw4eqNtlVbcEbiwMEdeEFj/2n25E12kpUb/j3T+EdNrLR9f8//1PamslPmrbFt3Gl2YMbfDvzK4dIRQAABBBAoD4HMu5zyuC6uAgEEEEAAAQQQaHOB3rVVGa32WlIITTZxmQ0C9goFATVr7U8ffdes3bQlI/v5KzcatQYkbRNQ678lH4/9d/er89PP3R4n7zbMjIiY2EOtMXt1rXK78ogAAggggAACCCRSgABgIquNQiOAAAIIIIBAXAVKPUPsUDvD8A8/PdH07hpsCTjXdvf96SPvmTUbM4OAChAuW0cQ0L1GFBBV679pC9eYx99Z4lZ7j2rd9+nJQwLr3MKgnjVGfyQEEEAAAQQQQCDpAgQAk16DlB8BBBBAAAEEYiXQ07bW6xUK1rW0gEN6dTGXfXon08e2MPSn+Ss3mO/e918vsOVfr+ezbFfhFes/GvMuvK0jLTd+3PpPk6fc9NzMwKVXVXQy5x80NuvEHr1rOxuNC0hCAAEEEEAAAQTKQYAAYDnUIteAAAIIIIAAArESKHUrQF2cWqJdZlsC9usWDAKu2rDFmxjkL6/MNQp2uZSyg9zNsJOGrN6w2a3qkI+Lbeu/xqaU+b+X55rl64MWp+053Ay2wdVw6lpVYcf96240uzMJAQQQQAABBBAoBwECgOVQi1wDAggggAACCMRKoFt1pekbCtSVooADe3wUBOzfrTqQnSa00Ky2lz30jllkJwJxyfZ6NR8sXZ91rEC3Tzk/utZ/b8xbZZ6ZXhe41ImDe5gjdhoUWKeFqsrtzHg742+FnRiEhAACCCCAAAIIlIsAAcByqUmuAwEEEEAAAQRiJaBJJQbbVnsDe1SbAfavf3f9VXkt+BQcVHdedRVWl+EeXSq9yUMKiTn1715jfnzcTmby0J4Z16ux/753/9vm6feXmZSaANqkse+mL1ln1jdkzhqckUGZrdDEH6vrt5ibn58VuLKazp3M1w4aYzqFWvjJf9zA7qamc0VgfxYQQAABBBBAAIGkC1Qm/QIoPwIIIIAAAgggEEcBBZFG9astqmgap05j963blDtYp1lpLzl6R/PYtCXmrqnzTKOa+n2cGhq3egGv/8xfbc49YIzpVlPpdYF9f/Hako9N6M4Z10d1j77txdm2G3RwopQv7z3KBmODk3soFjjWTgjSvSY42Upcr41yIYAAAggggAACxQgQACxGi30RQAABBBBAAIFWFOhaVWkm2ZZ9y2zLtbl2gg+NXReV1HrtmJ0Hm4lDepjrn/7Qdv0Nzvo7dc5KM6NuvbngU2PNTkN6mi02r7p1wTHwovIul/WvzFphpsxcEbicXYb3MgeP7x9Yp5Z/Cv71C3WtDuzEAgIIIIAAAgggkGABugAnuPIoOgIIIIAAAgiUp8AAO9bfJ4b1ypjwI9vVjupba352ws7msAkDMjavrN9srnjkvY9aCfomCMnYsQxXaPKTW6bMDlxZrZ3cQ60i/ZN7aKy/HQf1IPgXkGIBAQQQQAABBMpNgABgudUo14MAAggggAACZSFQVdnJ7GDHo5swuLuptmPW5UrVlRXm7P3HmG8fPs5oAhJ/UhvCh/6zyFxuJwhZvGbbBCH+fcrtucY/vOWF2Rldqc/cb7Q39qK73s4V23m+Pe1YjCQEEEAAAQQQQKCcBYJ3iOV8pVwbAggggAACCCCQQAGN9/eJYZ3NwlUbzSIbwPt4bo+sV7L7qD5mTP9u5oZ/zzDTFq0N7DPLThDy//31P4F1HWlhT2uz39i+6UtWgFUzAXexrQJJCCCAAAIIIIBAuQvk/jm53K+e60MAAQQQQAABBBIgoG6qI/p2NTvb8QG720k9ciXNLvzdYyaYL+41wug4kjE9rNlZ+49Od/1V0G/SUIJ/vDYQQAABBBBAoOMIEADsOHXNlSKAAAIIIIBAwgVqbffeneykH6Pt7MKVtvtqVNIEIZ+ePMT85LhJZkjP4Gy3UceU83p1j+7Z5aNuvgqgylDdpkkIIIAAAggggEBHESAA2FFqmutEAAEEEEAAgbIQ0AQWg2xQb/KwnmZory5GXVmjkgKFV3w8QYjGu+uI6fhdhpg9R/fxLr2XHetvgu3227ki2qwjGnHNCCCAAAIIIFD+Arn7kJT/9XOFCCCAAAIIIIBAIgXUgk3dgof36WLWbNxilq9vMCvWbzZbNeuHL9V0/miCkC/sOdLbx7ep7J8q4Ne95qOWf/26VZmxdnzETnSLLvt65wIRQAABBBBAIFOAAGCmCWsQQAABBBBAAIHECKhFoCYK0d+ovlvNyvrNZtm6howZcDXu3fA+XRNzXaUsqFpMjrLBUlmREEAAAQQQQACBjihAALAj1jrXjAACCCCAAAJlKVBpu7YO6FHj/W3a0mTqbCCwzrYMbNiytSyvt5CLGta7S4cNfBbiwz4IIIAAAggg0DEECAB2jHrmKhFAAAEEEECggwmo669a/OlPXYQVDNy4ualDKfTvXu2Nl9ihLpqLRQABBBBAAAEEsggQAMyCwioEEEAAAQQQQKCcBDQDrpsFt5yui2tBAAEEEEAAAQQQKEyAKdAKc2IvBBBAAAEEEEAAAQQQQAABBBBAAAEEEilAADCR1UahEUAAAQQQQAABBBBAAAEEEEAAAQQQKEyAAGBhTuyFAAIIIIAAAggggAACCCCAAAIIIIBAIgUIACay2ig0AggggAACCCCAAAIIIIAAAggggAAChQkQACzMib0QQAABBBBAAAEEEEAAAQQQQAABBBBIpAABwERWG4VGAAEEEEAAAQQQQAABBBBAAAEEEECgMAECgIU5sRcCCCCAAAIIIIAAAggggAACCCCAAAKJFCAAmMhqo9AIIIAAAggggAACCCCAAAIIIIAAAggUJkAAsDAn9kIAAQQQQAABBBBAAAEEEEAAAQQQQCCRAgQAE1ltFBoBBBBAAAEEEEAAAQQQQAABBBBAAIHCBAgAFubEXggggAACCCCAAAIIIIAAAggggAACCCRSgABgIquNQiOAAAIIIIAAAggggAACCCCAAAIIIFCYAAHAwpzYCwEEEEAAAQQQQAABBBBAAAEEEEAAgUQKEABMZLVRaAQQQAABBBBAAAEEEEAAAQQQQAABBAoTIABYmBN7IYAAAggggAACCCCAAAIIIIAAAgggkEgBAoCJrDYKjQACCCCAAAIIIIAAAggggAACCCCAQGECBAALc2IvBBBAAAEEEEAAAQQQQAABBBBAAAEEEilAADCR1UahEUAAAQQQQAABBBBAAAEEEEAAAQQQKEyAAGBhTuyFAAIIIIAAAggggAACCCCAAAIIIIBAIgUIACay2ig0AggggAACCCCAAAIIIIAAAggggAAChQkQACzMib0QQAABBBBAAAEEEEAAAQQQQAABBBBIpAABwERWG4VGAAEEEEAAAQQQQAABBBBAAAEEEECgMAECgIU5sRcCCCCAAAIIIIAAAggggAACCCCAAAKJFCAAmMhqo9AIIIAAAggggAACCCCAAAIIIIAAAggUJkAAsDAn9kIAAQQQQAABBBBAAAEEEEAAAQQQQCCRAgQAE1ltFBoBBBBAAAEEEEAAAQQQQAABBBBAAIHCBAgAFubEXggggAACCCCAAAIIIIAAAggggAACCCRSgABgIquNQiOAAAIIIIAAAggggAACCCCAAAIIIFCYAAHAwpzYCwEEEEAAAQQQQAABBBBAAAEEEEAAgUQKEABMZLVRaAQQQAABBBBAAAEEEEAAAQQQQAABBAoTqCxsN/ZCoLQCjY2N6QwXL16cfs4TBBBAAAEEEEAAAQQQQAABBBAonYD//9z+/4uX7gzklAQBAoBJqKUyLGNdXV36qvbcc8/0c54ggAACCCCAAAIIIIAAAggggEDrCOj/4qNGjWqdzMk11gJ0AY519VA4BBBAAAEEEEAAAQQQQAABBBBAAAEEWiawXcqmlmXB0QgUL7Bp0ybz9ttvewf279/fVFYW3xhVzZhd68GpU6eawYMHF18QjoitAPUb26opScGo35IwxjIT6jaW1VKyQlG/JaOMZUbUbyyrpSSFom5LwhjbTKjf2FZNSQpWivpVt1/XC2/nnXc2NTU1JSkbmSRLoPioS7Kuj9LGVEAfOHvssUfJSqfg37Bhw0qWHxnFS4D6jVd9lLo01G+pReOTH3Ubn7pojZJQv62hGp88qd/41EWpS0Ldllo0XvlRv/Gqj1KXpiX1S7ffUtdG8vKjC3Dy6owSI4AAAggggAACCCCAAAIIIIAAAgggULAAAcCCqdgRAQQQQAABBBBAAAEEEEAAAQQQQACB5AkQAExenVFiBBBAAAEEEEAAAQQQQAABBBBAAAEEChYgAFgwFTsigAACCCCAAAIIIIAAAggggAACCCCQPAECgMmrM0qMAAIIIIAAAggggAACCCCAAAIIIIBAwQIEAAumYkcEEEAAAQQQQAABBBBAAAEEEEAAAQSSJ0AAMHl1RokRQAABBBBAAAEEEEAAAQQQQAABBBAoWGC7lE0F782OCCCAAAIIIIAAAggggAACCCCAAAIIIJAoAVoAJqq6KCwCCCCAAAIIIIAAAggggAACCCCAAALFCRAALM6LvRFAAAEEEEAAAQQQQAABBBBAAAEEEEiUAAHARFUXhUUAAQQQQAABBBBAAAEEEEAAAQQQQKA4AQKAxXmxNwIIIIAAAggggAACCCCAAAIIIIAAAokSIACYqOqisAgggAACCCCAAAIIIIAAAggggAACCBQnQACwOC/2RgABBBBAAAEEEEAAAQQQQAABBBBAIFECBAATVV0UFgEEEEAAAQQQQAABBBBAAAEEEEAAgeIECAAW58XeCCCAAAIIIIAAAggggAACCCCAAAIIJEqAAGCiqovCIoAAAggggAACCCCAAAIIIIAAAgggUJwAAcDivNgbAQQQQAABBBBAAAEEEEAAAQQQQACBRAkQAExUdSWzsP/85z/Ndtttl/77f//v/xV0IdOmTTNf/epXzdixY02XLl1M//79zQEHHGBuvPFG09jYWFAe2knnP+GEE8ywYcNMdXW196hlrS806Xw6r86vcqg8KpfK98477xSajVm+fLm57LLLzOTJk02PHj28Pz3XuhUrVhScT3vs+MgjjxjV3bHHHmsmTJhg+vXrZzp37mx69+5tdtttN/Ptb3/bTJ8+veCizZ071ztmxx13NLW1taZPnz5mjz32ML/85S/Nhg0bCs7nxRdfNF/60pfMyJEjTU1NjRk0aJA58sgjzV133VVwHtpR+x9xxBHe8cpH+Snfl156qeB8VO5f/OIX3nXoenRduj7Z6HrjnObMmWOuv/56c9JJJ5kddtjBdO3a1fPU++b44483d999d1HvO96/8ant9evXm+eee85cffXV5pRTTjGjR49Ofx6PGjWq6IJSt0WTlc0BpfrcLhuQZlzIsmXLzMMPP+x97x999NHed6m7RzrzzDOLzrEc73FK9RlTNGYJDnjttdfMj3/8Y+9+wt13duvWzYwbN8585StfMS+88EJRZ6F+i+Jq1Z3Xrl3r3Qvpnu6ggw4y22+/venZs6epqqoyAwYMMJ/61Ke8e8BC7+fL8f61XL8jLrnkkvR9kz6v//3vf+d9rfHezUvEDu0lkCIh0IoC9j+eKRtISdnXd/rv8ssvz3vGP/zhDyn7hZo+xn+8nu+5556purq6nPk0NTWlzj777Mg8lM8555yT0n65ks5jA1OR+digYurmm2/OlYW37eWXX07Z4FRkPoMHD0698sorefNpjx22bNkSWW5/3diAYOrnP/953iI+9NBDKRsAjczT3iinPvzww7z56LXUqVOnyHxssDK1cePGnPnYoF3qmGOOicxD+dvAZ848tFHltYGzyHx0vf/4xz/y5tMeO/zgBz9I2RuayLK7Otb7wN7c5S0i79+8RG26g/1PSWTd6vO5mETdFqNVXvuW6nO7vFSKvxr3eZrt8Ywzzig4w3K9xynFZ0zBiCXe0f5IHPlZ66/v008/PdXQ0JDz7NRvTp522fjkk08WVL/2B/LUY489lrOM5Xj/Wq7fEW+++WaqsrIyUPfPPPNMZP3y3o2kYUNMBExMykExylTgoosu8j4w7S9j6Q/OfAFA29IsHdQZOHBg6rrrrvMCY/aXlNSJJ56Yzmf//fdP2ZZ5kXKXXnppet9dd901ZVt4paZOneo9atndjH33u9+NzEP56zxuX51f5VCgTuVy16Ug0aOPPhqZz7x581K25aCXj75EvvOd76RsixzvT8/dF4vymz9/fmQ+7bVBAUD7K2fquOOOS/3sZz9L2dZgqWeffTb16quvph588MGU6lnbndMNN9wQWdQ33ngjZVtQevvaX8VTV1xxRcr+Cpp66qmnUueee246DwUB7a+tkfnYFpnpfW1rzNQtt9zi1e8DDzyQOvjgg9PbTjvttMg8tOHUU09N76vjdLxeJ8pP+bpruummmyLzUTlVXrevrkPXo+vS9ek6tc22qkvpRiJuyQXKbYvFlG31mLr11ltTtpVCyrZkSP35z38OBMAV5Fy3bl3kJfD+jd/717ZUSL82bcvUlG3pmn5NFhMApG7jV7eRb8QSbyjV53aJi5XI7Nz3hB5HjBjhvR/dumICgOV4j1Oqz5j2emG4e4YhQ4akLrzwwtS9997r3U/YngSpa6+9NjV06ND0Z3G+exPqN/r+vr3qVwHA4cOHpxTA/c1vfpO67777UqrbKVOmpO65557UySefnKqoqPDqWI0Y3nrrraxFLcf713L9jlAwzzUCcf/n0+d1rgAg7934vXezvhE78EoCgB248lv70hU80BehayHnbnBzBQA3b96cGjNmjPflqRZTM2bMyCjmBRdckL6BUqAiW7JdUdNBtd133z2lVl7+VF9fn9J6lUnBt6jWZgoCuXLrvOGk41xLNtsVIKVAWbb05S9/OZ3PX//614xddOPgzlPMfwAyMmrFFbmCrTrtrFmzUrY7sHcdCnZG7e9+IZe7AmThZLvQpi2iXiu2e0U64Kj/QIVbg+rcn/nMZ9L5RH1RK0jn3LV/uMzKV/lrn169eqVWrlwZLq63/MMf/jCdj8ofTro5dEFeBWPilhSEvuqqqyIDrnKxXUfT1/ijH/0o6yXw/jWpOL5/Fby+8847A59zrmV2oQFA6jaedZv1jdgKK0vxud0KxUpklnbID681+JIlS7zyz549O/3ZWujnRzne45TqM6Y9XxTqdaD7ufC9hCuT7in8Pxbqh9Rsifo1qaj7+2xebbUuql7957///vvT72c73JB/k/e8XO9fy/U74le/+pVXn3Y4n5QajLj/M0T9v4L3bjzfuxlvxA6+ggBgB38BtNbl60vStbJTsEAflO5DMyqoo7L4A2FRXUkVvHOBpokTJ2a9hPPPPz99Pv06ly1pvStTtuCejrFj3Xn7qNWMzpstqZwun2zBvcWLF6dbNNqx6bJl4a3TNuWj1oQ6JonJjomYtrBj+GRcglpOOivtmy3p1zbnrqCb/lMQTgpWuXzUsjNbUktK90usuvhmS3b8JS8fBeeiWl4qf3eubME9lc+1flS5Vf5syW+jFoZJS3b8ynS3/J133jlr8Xn/Juf9W2wAkLpNTt1mfXO2YGWpPrdbUISyPrQ5AcByvMcp1WdM3F8sGgrE3VP8z//8T9biUr8mFXV/nxUsZivHjx/v1bG6AodTOd6/lut3hIa8cT147Jh/Kf3/1b139f/abIn3brLfu9nqtBzXEQAsx1qNwTXZiRy8D0n90rlp06aUPijdh2auAKC6RLj9cgXB/MEU/driT1u3bk2p+4Xy0S82uZL7kla3DB3nT8rXleVrX/uaf1Pgucrp9svWpUOtb9x2dZ2NSv5AU67uplHHx2H9xRdfnL5WtQANJ/+vZxoTMSr5g6qPP/54xm777LOPdx61vsw1jo4LqqoVarg7sZbdOJNHHXVUxjncCuXvWnnqvOGk8rn6vfLKK8Ob08v+gHOubufpA2L4xLWaVVfmbIn370djnSbh/VtsAJC6TU7dZntvtmRdqT63W1KGcj622ABgud7jlOIzJgmvE42N7e4Zsv04Sf1u+yE5fH+fhPpVGd29koJH4VSO96/l+h3x6U9/2nuvupbZ+QKAvHeT/94Nv1/LdZlZgO23MKm0AppN1H5IepnaseC8mXcLPYObHc0G5rzZWKOOs90o05ts98r0cz2xN9Nm0aJF3jr/foGdPl5w2xcuXGhUbn9yZdE6t59/u3uuWWdtoNNbDJdFKwvNx3+ObPm488X10U62Yex4gF7xbCvGtIm/vM5Cs+Nq5uColMvCtrgztgWdd6i9kfJmX8uXjw3iGc3M5092/EKjvJT85/Pvo+ea3W3vvff2VusY2807sIu7Jq3MlY+9IfRm1tV+SaxflVuOSrZlpfcY/sdZ8P4NfiaFnZK4TN1+VGtJfe+25DXn6r4ln9stOT/HBgXK9R7Hvc5a8v0RlIrnkvseVemyfZdSv9H39/Gs0WCpbNDS2LH/vJW2EUJgY7nev7r3bjl9R9geXd5s7bYHmLn66qsD9Ri1wHs32e/dqHotx/UEAMuxVtv5mmzzZ2PH3DNf/OIXzSGHHFJwaeyvosZ2w/T2D39phjPxb3/vvfcCm9999930sn+/9ErfE//2UuSj8tuuwr4zGOPKY7uJ5gxq2lmAjW1p5h0bLksgwxgtKBhmJzgxtmWj2XfffY0dE9Er3VlnnWW6d++eUVJ3XXa8RGO73WZsdyty1csHH3xgbDdbb1f/fu5Y/6N/uzu32+7qRcv+/dx2/6Pbbru2p6/RbS80H12vrlspXBaXV5wfly1bli637eqcUVTev8l7/2ZUYsQK6rZ86zaiygOr3edVSz63Axmy0CKBQr9zdBL33aXnrh71XKk5+bTWPU6pPmM+urJ4/2vH/UsXMNt3aXPqRRlSv2nWNn+i//Po/tdO9OL9EKx7RaVvfetbgbKU6/2re+2Vy3fE6tWrjZ3Ex6s722Xb2K7cgXqMWuC9uy3g7V4TUVasb18BAoDt6192Z7cDzZvHHnvM2LHbvC/CYi5wwYIF6d2HDRuWfp7tiZ2FK73aBQ3divbMxzYVNv7zq0xuOd81aV93XeFr0ra4JLWU3G677bw/tY6zXQmN7bqT/sXTdrs111xzTUZxbVdwY8eR89bns7BjPBr9kqgUtnCe2pYvH+fZFvmovHrd50quPHYg8HRrulz7x2mb7dZv3E2tnRAko2hxrRcVtNDXSUd4/2ZUXAErqNtkfDYXUJVF71Kqz+2iT8wBkQLt+X5src/IUl1TJFpMNtgugsYOFZIuTdy+S6nfdNXkfXLbbbel74V1/6eeQN/+9rfN0qVLvWPtTLDmC1/4QiCfUr3OS51PS+5fy/E7wk6MZ+wkTWa//fYzZ599dqAOcy2Uul50rva+fy3VNeVyY1vbCxAAbHvzsj2jnSHVXHTRRd712THcjJ0uvahrXbduXXp/O25G+nm2Jy44pG365dif4ppPvmvSNbjrCl+T//ri+ly/kNlBvM0jjzySbsnoL2sx9aLjoiyKycflofzCpqXOp5j6zVYerYtrsgM8m1//+tde8XQzola+4VRqT+WfzzSu9Rt+rYWtkrZM3UZ/HiWtLostbzF1r7zde7Lc3gPFurXm/sXUiasPlSdcJ6XOJ9/ntcrgytNaZdE54pzsjKLpIUxOPPHErEOhlLpe5JGvbly9aN+ousmXh451+UTloX3y5ePy0L7hfLQu7mmXXXbx6lj/D9KP5f5UDnWr6/HXSzHXpGNd/frz0Pq4pOeff9788Y9/9Hop3XjjjRl1mKucxVg4B+UXtih1PvnecyqDK09rlUXnIMVHILoPXnzKSEkSImAngDDqJrjXXnuZ8847r+hS61ckl9SyLFeykzqkN2vsOX+Kaz75rknX4K4rfE3+62vv53bCFPP22297xVCLMI2fqFaft9xyi7GTpZiZM2caOyBwRjGLqRcdHGVRTD4uD+UXNi11PsXUb7byaF0ck37N/tznPue1/tPN7O23354ey9Bf3lJ7Ku98pnGt3/Brze+UxOfUbfTnURLrs5gyF1P3yte9J8vtPVCMWWvvW0yduPpQmcJ1Uup88n1eqwyuPK1VFp0jrkldf9UqTEk/kGuM7Gyp1PWic+SrG1cv2jeqbvLloWNdPlF5aJ98+bg8tG84H62LSzr++OONxnZWUjl176tx4+6//36vV4x+NLWTSASKWw51667XXVgx16RjXP3GsW41RqP+/6qWsGrQMmnSJHeZBT0WY+EclHHYotT55HvPqQyuPK1VFp2DFB8BWgDGpy7apCSu62ZLHtXsPZzs9Ojm1ltv9QY01i8mmgSi2FRTU5M+xE3OkF4ReuIfRLlLly6BrXHNJ9816SLcdYWvKXCBORZaUq/u2Gz16z9l586dvS9FfTHql85jjz3WXH/99cbO6uv9Uva9733PaAzAcCqmXnRslEUx+bg8lF/YtNT5FFO/2cqjdfmSq6OWPOarX38Z9Cuk6td1AVDXpahxPUvtqXLkM41r/YZfa37TqOctqVN3bDF1G1WObOup2+jPo2xe5bSumLrXdbv3ZHPeA+Xk1prXUkyduPpQecJ1Uup88n1eqwyuPK1VFp0jjumdd94xJ5xwgvdDmtz/9re/RfaSKXW9yCNf3bh60b5RdZMvDx3r8onKQ/vky8floX3D+WhdXJKGfNF9sP722GMPc+qpp5r77rvP3HHHHWbWrFnmuOOOM+Hv5HKoW/n766WYa9Kxrn79eWh9HNLPfvYz8/7775sRI0akJ7MsplzFWDgH5R+2KHU++d5zKoMrT2uVRecgxUeg+ChNfMpOSWIioA+Nr371q15pvvnNb3pBoeYUzT9pRLgJcjg//0Qb4abNcc0n3zXpGt11ha8pfP1xXJ48ebL56U9/6hVNweAnnngiUMxi6kUHRlkUk4/LQ/mFTUudTzH1m608WhenpF8gdQP7+uuve8VSC1+NixKVSu2p8+QzjWv9hl9rUWZJWU/dRn8eJaUOm1vOYupe53DvyXJ7DzTXrzWOK6ZOXH2oHOE6KXU++T6vVQZXntYqi84Rt6SZQY844gizatUq70dyTZp24IEHRhaz1PWiE+WrG1cv2jeqbvLloWNdPlF5aJ98+bg8tG84H62Le/ryl79sTj75ZKPxHr/xjW8YDY/kUjnUra7FXy/FXJOOdfXrz0Pr2zsp8Kdu20pq1OC6xBZTrmIsnIPyD1uUOp987zmVwZWntcqic5DiI0AX4PjURZuUpBSz8mi2Wn/SL16a2UotwyZOnOjNCOvfruf+mZGmTZuW3kfdhUePHu3trq6lLrkWR245/OifGMJNrOD28Q+YWsp8cs0C5cqjljj+86tMWlY3ynxl0b4un/A1aVshqTXqt5Dzun0UMLrgggu8xXvvvde76XXb9ItW3759zYoVK/Ja6EbZfRmFLfy++Uydp8qQLx/XlcOV1/+YLx+NkafyauawXBOBuHz69++fbm7vP0++521Vv+rarcHJn3nmGa9I55xzjtEkILkS79+WvX/bqm5z1WHUNuq2ZXUb5ZqE9aX63E7CtSaljK31Hdie9zil+oyJWx0uWrTIHHbYYUaPuj/805/+5P2wlquc1O/8NE/4vi29IeZPdC+s7sC6L9QQOW4ykNaq2/a8fy2X7wiNz6mWcmPGjDGa1VmB+nDS/19devrpp72JQrT8mc98xgsYtlb98tns1HkslQABwFJJJiSfHXfcNkV3qYrsmg1v2bLFnHvuuXmz/fvf/270p6SWYi4AqF889GWvIIl+icmV/NsnTJgQ2FVBSJf8+7l1/kf/9nz5qLtrVHL5qPzhX41UHrWiWrNmjfdlMWjQoKzZLF682Kxdu9bbFi5L1gOyrGyN+s1ymshVCmy5NHfuXPc0/SgLDbA7Y8YMrytMZWX2jyDnqQPDFpppraKiwjQ1NbX560Tl3WGHHdLXoye6Jvd6Vrn33nvvwHa3oKCaxohRCl+T2yffY1vUr3611i/Y//jHP7zifP7znzc33XRTvqIZ3r8te/+2Rd3mrcSIHajbltVtBGtiVpficzsxF5uAgqo+XPJ/V7p1/kf/9vD3Tjif9rzHKdVnjP/a2/v58uXLzeGHH+51B1VZ1Kro9NNPz1uscL3kOoD6zaXTftui7oXL9f61HL4j3P9l1X37tNNOy/vi+clPfpLeR6189X8/3rvb/u8e/r5JY/EkFgJ0AY5FNVAIJ7D//vt7T6dPn57+ZcVt8z9qMGWXNE27PymgOGTIEG+Vfz//Pu75c8895z3Vr8+jRo1yq71HVxYt5MpHU8WrBaRSuCxaV2g+/nNky0d5xT1pQhCXws3Itd5Z6FdR17XU7e9/zGWhwWz33HNPb/eXXnop53gyLh8Nbhv+hVRjtriBcd1+/jK45/pFUOMbKukYtXT1J3dNWpcrn9deey3dqjHO9avu/O6XT/2q+X//938Fj+npLHj/Bj+T/K+XpD6nbj+quTi/d1vrteXqviWf261Vto6Yb7ne47jXWUu+P+LyetAPvkceeWS694vGz/36179eUPGo3+j7+4IAY7BT1L1wud6/uvduR/+O4L2b/PduDD4+2qYIdqYbEgKtLmC7EqbsK9r7u/zyyyPPd88996T3s2MxZN3PfsGkevfu7e1nf23Jus/555+fzscGibLuo/WuTLbbatZ97C8Y3j59+vRJ6bzZksrp8rFN/jN2sS37UnZSFG8fe0OYsd2t0Dblo311TBLTL37xi7RFtnq2XWXT222gKesl2pZ9Keduu9OmbAAuY7+rrroqnc9dd92VsV0rbEvSlG0p6O13zDHHZN3n6KOP9rbbln3e/tl2Uv6ufnV94WR/NUz17NnT20flti3owrt4y7pel8/UqVOz7tPeK+2sZ+kyHnrooSk7DmBRReL9m5z378iRI7261mMhibpNTt0WUp/F7FOqz+1iztmR9rWtR9Kfu2eccUZBl16O9zil+owpCLAVd9K9ov2hIF2n3//+94s+G/VrUlH390VjtsMBuud093v6/48/leP9a0f5jtD/a6Lq1dUx791kv3ddPZb7o6a6JiHQ6gKFBgAV7LHjL3gfsD169EjZrqIZZVOwzn0A2y7EGdu1wv6CnA7+2JZfKTueQ2A/LWu98lHwx7bgC2x3C7fcckv6XPbXW7c6/ajyqZzKZ/vtt0/ZbtDpbf4ntktlOh87+5t/k/dcgUN3TYX+ByAjk1Zccf/996fsGDY5z2Bbv6Vsqz/vOmRqu6Zk3f+AAw5I7/Piiy9m7JMviKgD7DiC6aCbAhi2q00gH9vdNmVbr6VNwzdgbuennnoqvc9nP/vZlI7zp7q6upSdDczbR8FIO5izf3P6+Q9/+MN0PtmChLpOmaiODzrooPRxcXriv7HZd999U3bQ4KKLx/vXpOL4/s1WkcUGAKnb5NRttvpu6bpSfG63tAzlenxzAoDleI9Tqs+Y9nyd6AdBO+FH+n7gwgsvbFZxqF+Tirq/bxZoiQ5SmTZu3Jgzt2uvvTZd/7ZFWMZ9Zbnev3aE7wj/fXLU/yt478bzvZvzTdsBNxIA7ICV3h6XXGgAUGV75JFH0i3mBg4cmLLjpqT065IdSDd10kknpb9YbZPzjC9W/7Vdeuml6X133XXXlO3WmHr11Ve9Ry27gNt3v/td/2GB5woI+X/J1flVDpVH5RowYICXj1rtPfroo4Fj/Qvz5s1L2TFBvH0VCLrkkktSdiw870/PXXBI+6jlWtySghq260LqhBNOSP32t79NqT7ffPPNlO0am/rLX/6SOvXUU9N1Jtcf//jHkZfwxhtvpOw0856FAoY/+9nPUmqNaQfUTZ133nnperFjpaTsmIiR+dx4443pfceOHZuyg2t79fvggw+mDj744PQ2O5ZHZB7aoLK714KO0/F6nSg/5eu22XHwIvNROVVet6+uQ9ej69L1ucCorltucUvXXXdduuy2O3zqhRdeSL399ts5//SftWyJ92/83r8ffvih958p/efF/dkJebw616Nb5x6jWiBTt/Gr22zvwdZYV6rP7dYoW9Ly1He/e6/p0U6wlP781f2Gf5ueR6VyvMcp1WdMlFlrrz/xxBPTdXnIIYek/vvf/+b8HlWwICpRv41RNO22Xj+cqUeQHe88dfvtt3v3Sm+99ZZ3L//73/8+8P8F3TM/+eSTWctajvevHeE7opAAoCqc92783rtZ34gdeCUBwA5c+W156cUEAFWuP/zhD17AyQVUwo92DLiUWmflSupKetZZZ6VvxsJ5aPnss89Oab9cSeexY79F5mPHl0vdfPPNubLwtilYZicAicxH27RPHJMCgNn8wusU4LrmmmvyXsJDDz2UbjkZzkPLCqYpaJEvXXbZZSk7s15k2dQNI9+vtWoN6u+uES6Pgrv60s+XVF47QUhkWdRS1E6skS+bdtmuVonh6863rFYrUYn3b5RM+6xXECFfffq36/M6KlG3UTLlv75Un9vlL5X7Cgv9PnXvyajcyvUepxSfMVFmrb3e1Vmhj7mGYKB+W7u2is/ftZzPV792NtjUE088kfME5Xj/Wu7fEYUGAHnv5nzpszEGAgQAY1AJHaEIxQYAZaIWSPqVTV2C7TTzKbVUUau/G264IbKrbTZL/aJ83HHHpezEIF5QUY9aztViL5yPuvbq1z2dX+VQeVQulc9OCx/ePXJZwcQf/OAHqUmTJnmtwtQybOedd/bWhbuxRmbSDhuWLl3qtYg788wzva7TdrbjlAKfCvipxZi6vNhBrvN2E/YXfc6cOSmNOadgX9euXVPqYqtu2RofJWq8Rf/x7vmUKVNSX/jCF1Iqk35xVatMO/Ne6s4773S7FPSolow6TscrH+WnfLN1U47KUN1mVX5dh65H1zV+/HjvOnW9cU2lDgDqOnn/xqe2SxkApG7jU6/tUZJSfW63R9njcs5SBQDd9ZTjPU6pvj+cUVs95gsMhbfnCgC6MlO/TqL9HzW0jX7kVkvPyZMnp9RLST147CzWXo8R9RLS922h97DleP9azt8RhQYA3SuV966T4DFuAtupQPYLiYQAAggggAACCCCAAAIIIIAAAggggAACZSjQqQyviUtCAAEEEEAAAQQQQAABBBBAAAEEEEAAgY8FCADyUkAAAQQQQAABBBBAAAEEEEAAAQQQQKCMBQgAlnHlcmkIIIAAAggggAACCCCAAAIIIIAAAggQAOQ1gAACCCCAAAIIIIAAAggggAACCCCAQBkLEAAs48rl0hBAAAEEEEAAAQQQQAABBBBAAAEEECAAyGsAAQQQQAABBBBAAAEEEEAAAQQQQACBMhYgAFjGlculIYAAAggggAACCCCAAAIIIIAAAgggQACQ1wACCCCAAAIIIIAAAggggAACCCCAAAJlLEAAsIwrl0tDAAEEEEAAAQQQQAABBBBAAAEEEECAACCvAQQQQAABBBBAAAEEEEAAAQQQQAABBMpYgABgGVcul4YAAggggAACCCCAAAIIIIAAAggggAABQF4DCCCAAAIIIIAAAggggAACCCCAAAIIlLEAAcAyrlwuDQEEEEAAAQQQQAABBBBAAAEEEEAAAQKAvAYQQAABBBBAAAEEEEAAAQQQQAABBBAoYwECgGVcuVwaAggggAACCCCAAAIIIIAAAggggAACBAB5DSCAAAIIIIAAAggggAACCCCAAAIIIFDGAgQAy7hyuTQEEEAAAQQQaL7AqFGjzHbbbWfOPPPM5mcSkyNXrFhh+vTp413Pq6++WrJS3XbbbV6ecpozZ07J8i3njFKplNl55509t1tvvbWcL5VrQwABBBBAAIEYCRAAjFFlUBQEEEAAAQQQQKA1BC677DKzatUqc8wxx5g99tijNU5BngUKKFj6/e9/39tbj/X19QUeyW4IIIAAAggggEDzBQgANt+OIxFAAAEEEEAAgdgLzJ0719x8881eORUIJLW/wCmnnGLGjx9vFi9ebH73u9+1f4EoAQIIIIAAAgiUvQABwLKvYi4QAQQQQAABBJojoC6t6q6pbq5JTldddZXZsmWL2W+//cxee+2V5Espm7J36tTJXHTRRd71XH311WbTpk1lc21cCAIIIIAAAgjEU4AAYDzrhVIhgAACCCCAAAItFli9erW54447vHy+9KUvtTg/MiidwMknn2w6d+5s6urqzN133126jMkJAQQQQAABBBDIIkAAMAsKqxBAAAEEEEAAgXIQUGBJY8wp0KSAEyk+ApqU5aijjvIKdMstt8SnYJQEAQQQQAABBMpSgABgWVYrF4UAAggggAACTmDRokXm0ksvNZ/85CdNz549vWDYwIEDvZlYTzvtNK+L79q1a93u6ceoWYDVNVgTORT696lPfSqdZ/jJM888Y8444wwzZswY07VrV9OjRw+vXP/7v/9rVO6Wpr/+9a9eFipD3759c2b39NNPG3mMHj3adOnSxSvPyJEjzd57720uvvhio+350tatW80f/vAHs++++5revXub2tpaM3nyZHPFFVeYDRs2RB6u45S/zqOuyv369fPqqVevXmaXXXbx1s+bNy/yeG3QNapO9Kj04Ycfmm984xtmhx128K5F27LNVDxjxgyvO65m5tXrQ9eu+tDsz6+99pqXV9Q/6rp73XXXeefs37+/V2YF9jS+39FHH22uvfbarOd0+Z100kne0ylTppj58+e71TwigAACCCCAAAKlF7Bj25AQQAABBBBAAIGyFHjuuedSNqiWsndQOf/+8Y9/ZFy/DX55x9gAXWDb7Nmzc+YVPtdBBx0UOF4LGzduTJ166qk587HBs9RDDz2UcWyhK2xwKlVdXe2d44c//GHOw771rW/lLIuuyQYQM/K49dZbkPWGgwAAEKtJREFU08e98847qUMPPTS9HHbYc889U+vXr8/IQysuv/zyyONcPjZAmrrvvvuyHq+Vcta+enzggQdS8nPHukfVnT/98pe/TNnWkRn7uf1t0DAVZWcDtKmJEydGHuvy+Pa3v+0/ZeD5+++/nz7eBk4D21hAAAEEEEAAAQRKKVBpb05ICCCAAAIIIIBA2Qk0NDQYG2Qzat3XvXt3c/7555uDDz7YDBgwwGzevNnYYJB58cUXzf3331/UtQ8dOtS8/fbbOY9Ry7uf/OQn3j5qRedP9kbOfO5znzOPPPKIt/ozn/mM0aywanWmySGmTp1qrrnmGqMWb9pPrcN23313fxYFPX/11VeNDJT22GOPyGMefvhh8+tf/9rbrtZ6cpowYYLXGk5jCNrAnvnXv/7llSsyE7vh3HPPNS+//LLXolHXM2jQIO8afvGLX5iXXnrJO/6nP/2p+fnPf56RTWNjoxk8eLA54YQTzD777ONZ1NTUeK3iVEe///3vjQ0emi984QvmjTfe8MqXkcnHK+Sm8Q7VotIG78wBBxxgKioqjDy6deuWPswG/8x3vvMdb9ldt1oLqtXh9OnTzW9/+1uv3KpHtUj85je/mT5WT/7nf/7HvPvuu946ne/EE080Q4YM8c6l2X3VevDBBx8MHBNeGDdunHc+OT/77LOeYXgflhFAAAEEEEAAgZIIlDKaSF4IIIAAAggggEBcBJ566ql066psLfxcOe0Muak1a9a4xfRjVAvA9A4RT2ygKWW7kXrntoG0jLzV0svexHktz/75z39mzWXlypWpnXbaydvPdonNuk++lXb23/T12+6lkbt/+ctf9vbT9a5bty5yvxUrVmRs87cA1DX9+c9/zthHLREnTZrknUOtCOUdTmqZZ4Oy4dXpZZXfBl69PGywLb3e/8S1AFQ5bCAuNXfuXP/mwHO1VnQt/9T60HZBDmzXQlNTU0rnUn42cJhSnbikFpzu+Fwt/LR/NjeXjx5tUNo7x4477uhfzXMEEEAAAQQQQKCkAp3sTQ0JAQQQQAABBBAoO4ElS5akr+nAAw9MPw8/qays9MbeC69vzrLG7TvuuOOMDRAZjQVnA4+BvO1dnLGBOS9rtShzk0CEz6Xx89RCTUktADWeXbFpwYIF6UPU6jEqOSeNkehvIRfeX9eTK6kFnFrChZPthuyNxaf1NhiWbjXn30/jLWqikqg0bNgwo3ERlWy3aCPHXOnKK680I0aMiNxFLSxtINJrWWkDgN7YgeGd1Rrz+uuvNyq/Wh/ee++96V1sMNA7Xityvba0PZ+bqxu1SM13XcqPhAACCCCAAAIINEeAAGBz1DgGAQQQQAABBGIvoC6lLtmWau5pqz0q6Hf88cd7k3coqKiA0dixYwPnU5fRmTNneuvUvTdX8geW1IW22FRXV+cdoq6wVVVVkYc7JzteYrpskTvn2PDFL34xcutuu+2W3jZr1qz086gn6ratgJi6H0+bNs3703UouW1Rx+pa8814rMCskibh0OQgUUndgTU5iJK/DjShijO1rR6NujA3N7kAobprqyswCQEEEEAAAQQQaA0BAoCtoUqeCCCAAAIIINDuAvvvv783lpwKYie5MHYSCm/8ObWo0xiApU5nnXWWN86c8tXMsBpvMJz8s8pqrDsFn6L+/K3xXCu9cH65ltVKTUmtCXOl008/3dus1nm2q643bqICppodt5hku7BG7u6CXNrBdjPOup/tsuuNq6fWgJqNV2MiqjwKwOnvvPPOSx+3fPny9PPwE43jp/EDo5LO44Kj3/3udyP9Xb24OvPXgVoFfv7zn/dOoUDv9ttv740n+OijjxYdxPPXT319fVSxWY8AAggggAACCLRIgABgi/g4GAEEEEAAAQTiKqAupWrppQktlDQJxPe+9z2jwKBadqn77Z133mnsWG8tvgRNFHH33Xd7+VxwwQXeRBrZMl22bFm21XnXbdiwIe8+4R1cEEwtE3MlO3OvN+GFHbfQ2PH6zD333GMUzFQgTV1vv/a1r5n//Oc/ubLwtrkWetl2VHdal7J527EQjZ1R1yuHAnT5Uq5r8gfUsuVTqjrQJCGawEVJZVaX7WOPPdaodaAmXdGyHVsyWxEC6/zXkqsbdOAgFhBAAAEEEEAAgSIFmAW4SDB2RwABBBBAAIHkCCiopBl7FQjUn7q5qmWbgi6PP/6493fttdcatdxyY7EVe3V///vfjcaRU1Iw7Te/+U1kFv7gl8qj1m6FpOaUrX///l7W6laqseVydXX9+te/7nWbVUD0ySef9MYdVPBq4cKF5qabbjJ24hIveKpZfEud1JpPs/sqyKlWjxdffLE58sgjve7Tagnouto+/fTTnq/On2usPM34myv56+Cyyy7L213Y5VVbW+ueeo89evTwxiPUrM2a9fnf//63eeutt7yAsloN6u/qq682DzzwgDezceBg34JrqalVul4SAggggAACCCDQGgIEAFtDlTwRQAABBBBAIDYCCghpbD79KS1evNg89thj5ne/+515/fXXvb+vfvWr5v777y+6zG+++aZRF1oFpNQNVIEgjf8XldQ6zCW1QlQX19ZKLgBoZ7j1WqLpfLmSgozqKq0/HaNglkzU0k1BxCuuuMJr2aZJTkqZ1IXWjX2n8x122GFZs/cHyrLuUOBKfx2oxV1L60Bdy/WnpO7NCgTedttt5r777jNqbahxBjXuo1pYZkurVq3yVsvftdrMth/rEEAAAQQQQACBlghs64/Rklw4FgEEEEAAAQQQSIiAJr34yle+4k3qoJlvlR5++GGvVWAxl6Ax4RQMU8s1tdxSiz7/WHfZ8tp1113TqzUWYWsmN3mFzvHBBx8UdSp12ZWNujY/9dRT6WMV4Cx10kQfSrKLCv5puxuLT89bkjS2oGtpV+o66N69u9ctWK1CNcuzkgLOL7zwQmSRXd3stNNOkfuwAQEEEEAAAQQQaKkAAcCWCnI8AggggAACCCRSQK2/DjroIK/smsXVtUIr5GI0Vp5aFM6fP9+ohaHG/8s1CYbLU0E1jaunpG61yqe10gEHHJDOWuMfNjepzG5cvVyTbzQ3fzeDrizU8jBbUpBVs+2WIqm+jjnmGC+rJ554wrz33nulyDYjD3UHdynKTTMaT58+3dttr732crvziAACCCCAAAIIlFyAAGDJSckQAQQQQAABBOIg8Pzzz+ecyVYzAT/77LNeUTX2nOsyW0jZzznnHPPKK694u2qyB00oUkhSyzpNRKI0a9Ysr/twQ0ND5KEKEKkLbnPS8OHDzciRI71DNU5dVNKkH/6JKML7qeWd66Y6evTo8OYWL2uyESUF+bK1MNSYffJetGhRi8/lMtDsvwoEKuD4uc99zixYsMBtynjU+f/yl78E9lHduddOxgEfr1Bw0aUoN9m68QyPOOIItzuPCCCAAAIIIIBAyQWiB6kp+anIEAEEEEAAAQQQaDsBdV1VF1a1hNPsrJMnT/aCfAp2qdvljTfeaN544w2vQGeffXbOsfv8pf7Tn/7kBYS07pBDDjGHH364mTZtmn+XwHNNHuEPAGlWXU20ofHu/va3v3ll0BiEGkdOXVMV9Hv//fe9seQeeughb1y4b3zjG4E8C11QF+XrrrvOPPPMM5ETgVxyySXeTL/a98ADDzTjxo0zKvOKFSu8rqvXX3+9dzoFzBSIK3U65ZRTvKCoAqHqmq2xB2UqC3UP1vk1VuN+++3nTU5SivOre7Qm6LjooovMu+++640DeN5553n1OXDgQK9l5pw5c7xu4hqjUN14NZmMa705b948c/DBB3szF59wwglm9913N0OHDvWKplahCqq6YOYuu+xiolr3ue7V/fr182anLsW1kQcCCCCAAAIIIJBNgABgNhXWIYAAAggggEBZCKiFl1pq5WqtpcDXz3/+84KvV8EflzQzrX+sPbfe/6huxpoYwiXNxqsA0YUXXugFITVBxHe+8x23OeOxOTMAu0zOPfdcLwCooJRaRCrAly2p+/Ptt9/u/WXbXl1d7ZVVga5SJwXVbrjhBi+4qG7AV111lffnP8/nP/95o2vJNUagf/9CnmuyEwU69agZj9WSU3/ZkmYizjZBh4KH+otK6hauyUCiZmC+6667vEN1feqSTkIAAQQQQAABBFpLgABga8mSLwIIIIAAAgi0q8DFF1/stfr717/+ZTRbr7qQalZWpUGDBnkt7jSDr1oHtnVSsOf3v/+9Of/8883NN9/sBQgVWFy/fr1Rd2S1GNxtt93M0UcfbT796U83u3ia4XafffbxWrLdeeedWQOAah2oCUyee+45r2WkJjdRl9+uXbuasWPHGo1lp3Jq8ozWSmr5N378eC8Ap4k5FJBUq7hPfOITXqtAtRL0B1FLVQ4FFT/72c+am266yajLrsbj07kV8FSLPgV31RpRM/mqPC6pVanK8/jjj5uXX37ZGwty6dKlXstBTWaicp944onmzDPP9PJyx/kfX3rpJTN79mxvlXxJCCCAAAIIIIBAawpsZ8cdSbXmCcgbAQQQQAABBBBAoP0E1BVVLcw0kYeCjAowktpfQN2pb7nlFnPkkUeaxx57rP0LRAkQQAABBBBAoKwFmASkrKuXi0MAAQQQQACBji5w8skne60J1aqvuROKdHTDUl+/ArF33HGHl+2PfvSjUmdPfggggAACCCCAQIYAAcAMElYggAACCCCAAALlI6Dx5zSuntK1115r6uvry+fiEnolGnNyy5YtRsHZqAlCEnppFBsBBBBAAAEEYipAF+CYVgzFQgABBBBAAAEESimg2XQ1s6/G05s4cWIpsyavIgQ0+o4Csprw5KyzzjIjRowo4mh2RQABBBBAAAEEmidAALB5bhyFAAIIIIAAAggggAACCCCAAAIIIIBAIgToApyIaqKQCCCAAAIIIIAAAggggAACCCCAAAIINE+AAGDz3DgKAQQQQAABBBBAAAEEEEAAAQQQQACBRAgQAExENVFIBBBAAAEEEEAAAQQQQAABBBBAAAEEmidAALB5bhyFAAIIIIAAAggggAACCCCAAAIIIIBAIgQIACaimigkAggggAACCCCAAAIIIIAAAggggAACzRMgANg8N45CAAEEEEAAAQQQQAABBBBAAAEEEEAgEQIEABNRTRQSAQQQQAABBBBAAAEEEEAAAQQQQACB5gkQAGyeG0chgAACCCCAAAIIIIAAAggggAACCCCQCAECgImoJgqJAAIIIIAAAggggAACCCCAAAIIIIBA8wQIADbPjaMQQAABBBBAAAEEEEAAAQQQQAABBBBIhAABwERUE4VEAAEEEEAAAQQQQAABBBBAAAEEEECgeQIEAJvnxlEIIIAAAggggAACCCCAAAIIIIAAAggkQoAAYCKqiUIigAACCCCAAAIIIIAAAggggAACCCDQPAECgM1z4ygEEEAAAQQQQAABBBBAAAEEEEAAAQQSIUAAMBHVRCERQAABBBBAAAEEEEAAAQQQQAABBBBongABwOa5cRQCCCCAAAIIIIAAAggggAACCCCAAAKJECAAmIhqopAIIIAAAggggAACCCCAAAIIIIAAAgg0T4AAYPPcOAoBBBBAAAEEEEAAAQQQQAABBBBAAIFECBAATEQ1UUgEEEAAAQQQQAABBBBAAAEEEEAAAQSaJ0AAsHluHIUAAggggAACCCCAAAIIIIAAAggggEAiBP5/SzbRhpmFXEcAAAAASUVORK5CYII=\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAQABJREFUeAHs3QmcJVV5KPAzgMMqu8gygAuiuCMu8FCQ1ecWBdQoUZ5EFhUVXxLDS1BfQojBuKDBp6xqNFGekPiMiAEXZHNBxAVFBYGBGWaA2Zfu6f2++q5Wpbqn+/bt7tvd99b9n99vcuvWcuqc/6nu2B/n1LeglpWkECBAgAABAgQIECBAgAABAgQIECBQSYEtKtkrnSJAgAABAgQIECBAgAABAgQIECBAoC4gAOhBIECAAAECBAgQIECAAAECBAgQIFBhAQHACg+urhEgQIAAAQIECBAgQIAAAQIECBAQAPQMECBAgAABAgQIECBAgAABAgQIEKiwgABghQdX1wgQIECAAAECBAgQIECAAAECBAgIAHoGCBAgQIAAAQIECBAgQIAAAQIECFRYQACwwoOrawQIECBAgAABAgQIECBAgAABAgQEAD0DBAgQIECAAAECBAgQIECAAAECBCosIABY4cHVNQIECBAgQIAAAQIECBAgQIAAAQICgJ4BAgQIECBAgAABAgQIECBAgAABAhUWEACs8ODqGgECBAgQIECAAAECBAgQIECAAAEBQM8AAQIECBAgQIAAAQIECBAgQIAAgQoLCABWeHB1jQABAgQIECBAgAABAgQIECBAgIAAoGeAAAECBAgQIECAAAECBAgQIECAQIUFBAArPLi6RoAAAQIECBAgQIAAAQIECBAgQEAA0DNAgAABAgQIECBAgAABAgQIECBAoMICAoAVHlxdI0CAAAECBAgQIECAAAECBAgQICAA6BkgQIAAAQIECBAgQIAAAQIECBAgUGEBAcAKD66uESBAgAABAgQIECBAgAABAgQIEBAA9AwQIECAAAECBAgQIECAAAECBAgQqLCAAGCFB1fXCBAgQIAAAQIECBAgQIAAAQIECAgAegYIECBAgAABAgQIECBAgAABAgQIVFhAALDCg6trBAgQIECAAAECBAgQIECAAAECBAQAPQMECBAgQIAAAQIECBAgQIAAAQIEKiwgAFjhwdU1AgQIECBAgAABAgQIECBAgAABAgKAngECBAgQIECAAAECBAgQIECAAAECFRYQAKzw4OoaAQIECBAgQIAAAQIECBAgQIAAAQFAzwABAgQIECBAgAABAgQIECBAgACBCgsIAFZ4cHWNAAECBAgQIECAAAECBAgQIECAgACgZ4AAAQIECBAgQIAAAQIECBAgQIBAhQUEACs8uLpGgAABAgQIECBAgAABAgQIECBAQADQM0CAAAECBAgQIECAAAECBAgQIECgwgICgBUeXF0jQIAAAQIECBAgQIAAAQIECBAgIADoGSBAgAABAgQIECBAgAABAgQIECBQYQEBwAoPrq4RIECAAAECBAgQIECAAAECBAgQEAD0DBAgQIAAAQIECBAgQIAAAQIECBCosIAAYIUHV9cIECBAgAABAgQIECBAgAABAgQICAB6BggQIECAAAECBAgQIECAAAECBAhUWEAAsMKDq2sECBAgQIAAAQIECBAgQIAAAQIEBAA9AwQIECBAgAABAgQIECBAgAABAgQqLCAAWOHB1TUCBAgQIECAAAECBAgQIECAAAECAoCeAQIECBAgQIAAAQIECBAgQIAAAQIVFhAArPDg6hoBAgQIECBAgAABAgQIECBAgAABAUDPAAECBAgQIECAAAECBAgQIECAAIEKCwgAVnhwdY0AAQIECBAgQIAAAQIECBAgQICAAKBngAABAgQIECBAgAABAgQIECBAgECFBQQAKzy4ukaAAAECBAgQIECAAAECBAgQIEBAANAzQIAAAQIECBAgQIAAAQIECBAgQKDCAgKAFR5cXSNAgAABAgQIECBAgAABAgQIECAgAOgZIECAAAECBAgQIECAAAECBAgQIFBhAQHACg+urhEgQIAAAQIECBAgQIAAAQIECBAQAPQMECBAgAABAgQIECBAgAABAgQIEKiwgABghQdX1wgQIECAAAECBAgQIECAAAECBAgIAHoGCBAgQIAAAQIECBAgQIAAAQIECFRYQACwwoOrawQIECBAgAABAgQIECBAgAABAgQEAD0DBAgQIECAAAECBAgQIECAAAECBCosIABY4cHVNQIECBAgQIAAAQIECBAgQIAAAQICgJ4BAgQIECBAgAABAgQIECBAgAABAhUWEACs8ODqGgECBAgQIECAAAECBAgQIECAAAEBQM8AAQIECBAgQIAAAQIECBAgQIAAgQoLCABWeHB1jQABAgQIECBAgAABAgQIECBAgIAAoGeAAAECBAgQIECAAAECBAgQIECAQIUFBAArPLi6RoAAAQIECBAgQIAAAQIECBAgQEAA0DNAgAABAgQIECBAgAABAgQIECBAoMICAoAVHlxdI0CAAAECBAgQIECAAAECBAgQICAA6BkgQIAAAQIECBAgQIAAAQIECBAgUGEBAcAKD66uESBAgAABAgQIECBAgAABAgQIEBAA9AwQIECAAAECBAgQIECAAAECBAgQqLCAAGCFB1fXCBAgQIAAAQIECBAgQIAAAQIECAgAegYIECBAgAABAgQIECBAgAABAgQIVFhAALDCg6trBAgQIECAAAECBAgQIECAAAECBAQAPQMECBAgQIAAAQIECBAgQIAAAQIEKiwgAFjhwdU1AgQIECBAYHKBj370o2nBggVpiy22SD/72c8mv6BDz1i0aFE69thjp9z6T33qU4XPD3/4wylf7wICBAgQIECAAIH5FxAAnP8x0AICBAgQ6DKBN7/5zfWASgSd4t+HP/zhKQs84QlPGFVHXlf+GcGsnXbaKR144IHpj//4j9OXv/zl1N/f39R98jom+txyyy3TLrvskp75zGem//E//kf6+te/noaHh8ete3BwMD3ucY8r2nreeeeNe14zOz/5yU8W9UTfent7m7ms4TnLly9PeZv+5E/+JD33uc/d7Pzf/e53xX0nMmm0//zzz9+szrE7oh3/8A//kI477ri09957p2222SZtu+22aa+99krHHHNMijqWLl069rI5+X7mmWemJz3pSalWq6V3vetdaWRkZFbv+61vfSu95S1vqT+722+/fdp1113Tc57znHTOOeek3/72t1O694oVK9I3v/nN+hi/+tWvrnuWx+qWW26ZUn333ntv+ud//ud09tln14OpT37yk9NjH/vYtNVWW6Udd9wxHXDAAel1r3td+tznPpd6enqmVHecPDAwUK//5S9/edp///3rz0E8A4cffnj6+Mc/nlatWtV0nWvWrElf+cpX0jve8Y502GGH1X8OFy5cWLTzjW98Y/33QvyMTqc88MAD6X//7/+dDjnkkHrd8byGx+tf//r0ta99rf68NFtv/G768Y9/nD796U+nU089tf67JX7P5GN12mmnNVvVZufdcccd6d3vfnd61rOeVf+9FT9bYfvf//t/T1dccUXatGnTZtfYQYAAAQIEKimQ/Y85hQABAgQIEJgjgfXr19e22267WvY/Kop/Bx100JTvnv0BW1xfrqvRdvbHee3WW2+d9F6N6pjo2POe97zar3/963Hrfs973lO0NQuQjHtOMzsPPvjgop63ve1tzVwy6Tmnn356vc4sYFrLgkvjnn/PPfcU952o/432X3bZZePWm+/MAju1LHgy6T223nrr2t///d/nl035c5999qllwcQpXxcXXHrppUX7/uVf/mVadUx2URawqmXBs+I+45lmAaxaFjCfrKr68Re84AUN64r6b7755qbqyk+KMRivXePtywJ3ta9+9av5pZN+/upXv6o9+9nPblj/4x//+Np//ud/Nqxr3bp1tVe84hW1xzzmMQ3rytucBXen7BDPw9jfY3l9+efLXvay2sMPP9ywrXHwwgsvnLSt0/l5zwKwtew/tkxq8JSnPKWWzWydtJ1OIECAAAECnS6wIDqQ/T9qhQABAgQIEJgDgc9+9rMp+2N2szvddtttKQtYbLZ/oh0xAzBm4ESJGWJPe9rTRp0aM/JittDtt9+e7r///uJYzKj63ve+l57//OcX+8ZuxKybvLz2ta9NWeAo/1r/HBoaSo8++miK5aAxcy0ve+yxR4p+xOyacokZODFLKC9ZEDL9t//23/KvTX1mwZH6rKD85Jtuuim95CUvyb9O6zNmc4Vb9CdmbV111VXj1rNy5cr0N3/zN+MeG29n9C9fSpwFjFIWBEk777zzeKemv/u7v0sf/OAHi2Mxk+yFL3xheuITn1ifaXfffffVZ0aVZ929733vS//4j/9YXNPsRiwBjv5++9vfbvaS4ryYoRVtivGOWW5ZsLc+6604YYYbMfPt+OOPTzfeeGNRU8zYygLL9ZmeWaCu7pgfzAKh6a//+q/zr+N+Rn8feuihcY/lO6PeF7/4xfnXST9j9lg+kzZ+BsMzC8rVZ2uuXbs2/eIXv0h33XVXUU/MxI2f+Zgp26g8+OCD6UUvelHRx/gZPPLII+szL+Nn7Tvf+U4xUy0L7KWYJRnHxysxU3TfffcddWjPPfes/8xHW8M6ns8777yzOCfqjFl7MfNwspIF/1LMCs1LzAY++uij6zMLo874nZOXLGifwjh+70xU3v/+96cYz0YlfmdefvnljU4ZdSzG6KUvfWn9d1R+IH6PxVjHTM343Rm/Q/r6+uqHd9hhh/r3aK9CgAABAgQqK9DpEUztJ0CAAAECnSRwxBFH1LL/UVH/V571ddZZZ02pG+UZgNlyw4bX/sd//EctWzJb3DdmRjUqefvi84Ybbpjw1CzIWMuW0NViVlZ+Tbb8b9zzs+XCxTlvf/vbxz2n0c4s6FVcHzOWsoBYo9ObOhbtyNvdqJ9NVVY6KVtGXNT7hje8oXRk9GbMmMwCfsW52fLfWsw2HFtiZlgWuCjOy4JDtZ/+9KdjTxv1PXz+3//7f7UYjyxQNWq2Vjx3WXCsdtRRR9U+8IEP1LKAcC0LmIy6frwv5557btGGK6+8crxTpr3vr/7qr4q6o33Z0tVRdUX7/uzP/qw4Jwyy5bujzhn7JWY8xiy4mJ2aBaxqWQCplgWjizpi7Kc6AzCemauvvrqWBeXG3q74HvfIliwX98mCX7UsKFccH28jC4gX52eB1loWSBx1WtwvC2gV5+y+++61mOk3XlmyZEn9vGzpdO1//s//uVld+TVZsLVW/j2SBakb9iuu++UvfznqmT3llFNqGzduzKusf15//fW1qCv/2YpZto1K/lztt99+tZNOOql2wQUX1LKAZ+2EE04o6pjqDMBsyW9xbbaUuBazbLNA/6hmLFu2rBazFPN2ZkHTWrYceNQ5vhAgQIAAgSoJxPs5FAIECBAgQGAOBLLZXLUIXMQfnPH5f/7P/yn++Iw/1psJwuTNLP/hPlkAMK7J3gFY3Cvuf/fdd+dVbfaZ/0Ecn80ExrJ31xV1xxLJDRs2bFbnRz7ykeKcbMbQlPoagcbsvXjF9dlsvM3qn+qO1atXF0GxmSxLHnvfn//850U7w+8b3/jG2FOK73ngI86LgFz2TsPi2NiNCPZkMyyLuv/yL/9y7CnF9+hbNiOrOLc8nhNtT7ZMOSqP5ze//tBDDy3uN9ONbFZhMRZRfwTqJioRIMrbkM0Anei0+v4Yi2yG16hzsvfdFddHPVMNAI6qrMGX7N2DtfiZztsaQa2JSjbzrjgvfn4i4DteiZ+rCObmdUbwdrwSwcL4GYnXDUxWslmwtWz2W1FnNhuv4SXloFz8x4yJAvHZe0GLOiPIPdHy+rhZNkO59sgjj2x23+ydnEUdUwkARgC0vPw5my27Wd35jvidW361QKNxyq/xSYAAAQIEOlVAEpDsf0UpBAgQIEBgLgS+8IUvFC/Gj+V7Z5xxRv3l+XHvLGiTrrnmmllrxoknnphiOWpeyssU833T/Tz55JOLS7M/qFMsrR1bIsFGvNQ/SiQniMQhzZZYsprN1qmfngVOUzbrqNlLJzwvkqLkSUSyWXoTnjfVA5EgIi+x7DKbYZR/3ewzlovmJZZaZzPf8q+bfcayxVe96lXF/iyAW2yP3YgkDN/97neL3ZFEIhLBRLKKWBYbfrFkMxKpTKXEEuB8mXos/45lwK0okTAjH4vsfZjjLpHP75MFkuuJIeJ7LC0tL2PNz8k/s/fpjXrm8/1z8ZnN0EuRdCQv2YzNfHOzz+w/BBT7/vRP/zQ9/elPL76XN2KZahbYK3ZdfPHF4yZkiaQ7kZwjxnuyEsld4vdQXrKAdb652Wf8DGazSov95bEodv5hI57VWIIbJZbYX3LJJfXt8f5PFtRM8fqAVpVoY57YJH4G3/ve905YdSRFiWX4ebnooouK39H5Pp8ECBAgQKAqAgKAVRlJ/SBAgACBthbI/kthigBgXiLLabzvLTJx5qUcPMr3teoz/tDdbbfdiuqmk6G0uHjMRgSYymW8uuOceMdbXr74xS/mm5N+lt3ivX8RiJpp+dd//deiigi+taLEexfL9Ua25zzoOV792dLJYne8R22yEtlw81J+J2C+Lz4j4228Ly4v2Yym+vvOsiW79XefPfWpT61nmY1z4h2REVyNZzCexWZKNgOsOC1LBlJsz2SjHFSKDLCNSox9BM/zkiXZyDfb7jMCcXnJZu/lm6M+s1l6KVuCXeybrP8RrM6Sb9TPjwzHU81iXNyotBEZhvOyePHifHOzz3Jm3whSxrsqG5W3vvWtxeG5HKcf/ehHxX2PPfbYFO83bFTinPj9GCXeGRnBbYUAAQIECFRRQACwiqOqTwQIECDQdgLxh3okdIgSiQQi6USUCATmJYI38Uf9bJSYEROzDPMSM2NaVSLJRblMVHc5EcK1115bD0CVrxtvO4Jk5eBBuY7xzm9mXyT1yP/Ij1lw5QQlzVw/0TnXXXddypYyFocna2v2zrPi3EhyMlnJ3r9WnJK9Y67YLm9EkCYvkTjhnHPOmTAAEsHJSCATsyEna2teZ8wczEsrZqxGsPjHP/5xXmUxa6zYMc5G9u7CYm95pmOxs002yrNsY5bbeCV+L0RSjigxYy+SnjQqMUs0koXkpRX9j1m1eYkg9kQlex1AcSif3VfsGGejPE7ZMt/UKLg4zuXT3lX+GcxelTBpPTEzuvw7qxxAn/RiJxAgQIAAgQ4SEADsoMHSVAIECBDoXIHy7L7XvOY19dlY0ZtYUpln8I0g3Ze+9KVZ6WQEhvKMlxGAbJQFeKoN+L//9/8Wl0SmzYlm6EW/82Wn0dcIPE1WsoQLxfLQmPkUy1tnWuIP/HwGXWQjjkytrSjlMY6gYpb4pGG1f/RHf1QczxK1FEHJYmdpI2bqRYAxSoxfLBUdr0SgJS+vfOUr881JP8tBoEYnx3OTL1WOJcyRoXYm5Te/+U2x5DLGYaLAZvke5SBZq5Yhl+tvxXaWCCP953/+Z1FVHvAvdvxho9z+6HujGaP5ta3uf3kZ9djswfk947Pc1nIbyueUtyPAXZ51XL6+fF6rt2O29UxKM8H4mdTvWgIECBAgMF8CrflfvPPVevclQIAAAQIdIJBllkxXXXVV0dLyrL/YWf5eDiIVF8xwIwJHWRbUopZ3vOMdRQCy2DmNjfhDO5a8ZokDiquz5BTFO9qKnX/YiMBVvIsuL80sAy4v/43lp8281yyvf6LP8hLBeE9cK8ratWtTBPHy0syMulh6nM+oi/ekxXaW7TjF++KypB/1dyX+5Cc/SVlG0xTBvPCO2Uqf//zn00QzyvL7x2fU2eoSAapyYPO2226b0S2y5BDF9bFMPF+KWewcZ6M8czICkPFOyfkuMTYxZj/4wQ/S2WefXX//Xx5kjmehPBuu3NZy/5uZrRbXlvsfAdSZlJjxV/45jOWw45XoX5ahujg0H20tbj7JRnnp9YMPPjjJ2ak+A7M8a3CuApWTNswJBAgQIECgxQLNvfClxTdVHQECBAgQ6CaBWMIa7/qKEn+cjk0MEQkyIogWf2RH8Cdm5DzrWc9qmijexXb77bePOj/+sI8lv3fccUf63e9+Vxx75zvfmeLl/c2WT37ykylm4ZVLBJZiqXIEf5YuXVo/FDPIzj///PSe97ynfOpm2xEMufTSS+v74/oIgMR76cYrWTbPUe9HayaoNl49Y/eVk2/ksy/HnjPV7zELMp9hGe8ce9Ob3jRpFTHjLZbRxrvSvvKVr6QIFH/0ox+t/xt7cZwb71AM4zwRx9hz4vtTnvKUFLPPokRwthUzJuuVlf5PJOrIl+1mmXZHJScpndbUZryHMC+Pf/zj882Gn+XlmnFiPOfNvEOxYaXTOBgJOf72b/92wivjOYiAbozZRGWm/S8v65/oHo32R9KL/PdDBHff/va3j3t6LMWPBD95mc5YzbSt+b0n+4zZt/ns4pg5G7+vGr3jMmYEl/s2V+2crB+OEyBAgACBVgsIALZaVH0ECBAgQGCMQHlWXwSGxv4xGrNpjjjiiHTjjTfWr4zzIxDUbIk/YCd7b9WBBx6YLr/88hRJNKZSygkaJroukgFcccUVo2aGTXRuLLmNIFU+myhm+P393//9uKfHzKQIikaJpcXxvrpWlPIy2ciK24pSnqkYGVAjC2wzJZbTRvAwMpXGLM3ycszy9RF0iwQQky2RjRmWeVbZWPYdS4U/+MEPNjVjsHy/RtsxFnmZ6XvdyolQ8qXFed0TfY49r1zHRNfM9f6YJRmB+cnGq9z2sf2aqM3l88rXT3T+RPsjEH7uuecWhyMb8EQB8bH3KbehqGCcjfJ5Y+sY5/SW7IrsyxF4jd8dy5cvT5/61KcmzAQcwcHImFwuEyVsKZ9jmwABAgQIdKKAJcCdOGraTIAAAQIdIxBZJWMWSl7Ky33zffF5yimnFF9j5lajl/EXJ05h4+67706xvC8CTflMtSlc3vDUmMl32GGH1Wc65cseG11Q7msESfIg39hryksTw61V7+orL/crv6Ns7P2b/R7BzO9///vF6VOZqRh9//SnP51OOumkevAvlvjGctFI4BH1HHroofV+x3vJTjvttHqSiHzGVnHD0kYEeOO8vHzuc5+rv5MxAlIx2ywCIjfddFMaL1Nzfs1kn+Xg5tgEMJNdO/Z4+VlsZvlvXB9G5RIzJ+ejROD7rLPOqv+LmXMRoM1ns0bClpipGfsbBZRm2v/p9j1mucWS+t7e3jpdBP4a/UeHcjvjgumM1XTbOtWxjf/YEWORlwgGRhBw7O+mWD4eBvls1vz8uWpnfj+fBAgQIEBgrgQEAOdK2n0IECBAoCsFIsCV/+EZf2RPlHwjkgTEO/KiRFAlT/jQDFoEeSKQVP4X94xlx/EOuQ984ANp++23r7/rKpb0vvzlLy8yj05Wf2T+LNcb21F3vHctgl7xvrNY6hize+I+EwU4y/eJc/KkE/GOrnzmY/mcCCqW3282laBauZ7xtvOgRxyLxCIzLeXZf7HE+xWveEVTVUaQ941vfGM9gBSBuQgCxoy6yOwaszU/n73rL94pF9lkI9gUJQKBMROy0XvvLr744hTLU/PnKb8ugjhR15FHHpl23XXXejunk8m3bDaTQGK0q9zGPBtu7G9Uyss147zyLLNG17X6WIxzBJbi32c+85n6TM54ZiOgFMG/SHRzySWX1AO6E81+m2n/p9P3CHBFApo8K/nOO+9cf0dpeVzHWpXbGcemM1bTaevYdjT7PYLqT3rSk+qnxyy/eI9mvDczZmDHTNv4HRjf4/mPpc/lhDyteM9os+10HgECBAgQmEsBAcC51HYvAgQIEOg6gfLy30bBsR133DFFlty8lK/L903lMwJs8YdsZOs877zz6oGk/A/b733ve+kf/uEfplLdqHOj7ggaxKy/T3ziE+naa68tspdGFuPJ2h5Lnl/60pcWdZYDaPnOch0R/JpoaWJ+/nQ/I6A5kxLXR5A3LyeffHI9IJp/b/QZYxDv/oty3HHH1bfHvt8ujsWssphFGkuno0TQdOyyxfqBP/yfCGjE8Vjq/OEPfzjFsuuxs+YigPPNb36znqwi7h3vdGy2zNSsfJ8ddtih+NrszKux55XrKCqbx40I8sfPWB7sjyD8OeecM26Lym0f269xL8h2ls8rXz/R+eX9EZSM90Leeuut9d0RlPv6178+6fL9sfcpt6Fc/9jt8nlj6xh7biu/R4A7+lh+5UG8U/TKK6+sv4M0MjRH2+L3bvzOOvzww4vbx+82hQABAgQIVFFAALCKo6pPBAgQINAWAjETKM8oGUGzSPbRqJQDhJFRNjLLtqpEUpGYBZOXf/qnf2p6Fk9+zUSfsbS4nN23mSQj5Rl9kWSkHCiI4FS8Fy8v5XPzfTP5LM90Kt93OnXG7MWYtZeXZtsasxDLTvEexEZLnCN4W35fWwRNJ1smHsHEyMocgZCYDRrfn/jEJ9ZnHZaDHBFcjNlsMVOqmVI2i5mlMynlJdjlpdmN6hy77DiCPe1W4hkrj2+8IzOyBI8tM+3/VPoeM3fjd8w3vvGNejNi5u6//du/pRe/+MVjm7XZ9wjelZf9TmesptLWzRowjR3xvMdy9whwRmA+nv0Yl+hLLIn/q7/6qxRLtWO58MqVK4s77LvvvsW2DQIECBAgUCUBAcAqjaa+ECBAgEBbCZRnscWsqSdkS84iEDjRv0gekZdYrlkOguX7Z/JZzj4c7wCLjMOtKuW6Y5nq2CDN2PvEctc8eBTvSCsnG4kARZ4dNYIOsUy2laU8y678h/907lEe4wiyHnzwwU1VE0t788zQEdzLZ4s1uvjoo48uDkcwqdG7AIsT/7ARjjEzMJZFRobUZcuW1ZcJ5+dFFulYctxMKc8WLFs2c+3Yc/J35sX+WAYdM9QmKzEDMi977LHHvGQAzu/f6DNmn+UzL2PZ8thM3XFtuf8PPPBAo+qKY+X+NzszNn7/nH766cXvlHgW4l2jsRS2mRK/s+LdenmZzbbm92jVZ/xejb7GkudYsh6/byLZzoc+9KGUB/vid1ZeGmXZzs/xSYAAAQIEOlFAALATR02bCRAgQKDtBWIWWwRaZlLKwaWZ1JNfu9dee+Wb9c9m/4gfddEEX8bWXQ5SjHdJzMKJIGBeysuAy9uR0bPVM4diJlBeli5dmm9O+TNm8cXsxby89a1vzTcn/YzkMHmJ/kWAZbJSTr4R5443o2yyOvLjsfQzlgm/853vzHelyBrcTCm3PYLaMykRwMr7HjPUfv7zn09a3R133FGcE9mR27VEkC2WmOYlD2rn3+Oz3P7IyhsGk5Xp9P8973lP+uxnP1uvOrwvu+yy+lLgye5VPl5uazP/8SB+B5T7XL6+XO98b8dM2njnaF5iybxCgAABAgSqKCAAWMVR1ScCBAgQmHeBeLl8zLKLstVWW6UXvehFTf0rzz6JWWKRvbdVpZz8IupstOR0qvecTt3l5bLf+ta36rMGI2AQ7xTMS/mcfN9MP5/97GcXVfz2t78ttqe68e///u/15CdxXYzxZEu8y/WXEyJEQo9m3qtXDqZEXeVlvOW6p7JdXrpdXsrcqI58WXuc85znPKfRqZMei1mg5Wc+3p03WSknjSnPipzsurk+HrP+8t8Bce/xAtnx7rlYihslZoSWg3v1nWP+T8wM/tGPflTsbab/8f7BSFSSl0gEdOqpp+Zfm/6M7NR5meo4RdB9psHi/N6t/vzOd75TLAGOIHuzsyJb3Q71ESBAgACB2RYQAJxtYfUTIECAQFcKlGfvxR+UP/zhD5v6FzNR4v1UeSnPhsv3TfdzbHBhn332mW5Vm103nbojoLDffvvV64pZOPEy/nhJf55hNJZ3zsYf43lG3bhxMzPONuvsH3aUxziWQD/+8Y+f6NTN9uf9jgPNBH7ivMgOnJdY0psvX8z3TeezPEOt/I63ieqK9wSWl0uWLSe6ZrL9r33ta4tTJluGHEHKcvCpfG1RSZtsRKKJ8nsax1uuu9NOO9WzBOdNnqz/V111VcqD7RGsKievyOsof/7t3/5t+sd//MdiVyx7Lb8LtDjQxEYkKcpna8YzMN6S5nI15b606zhF4L2cUOdP//RPR73rsNwf2wQIECBAoNMFBAA7fQS1nwABAgTaTiDekRYZVvPy5je/Od9s6rN8/he/+MWmZodNVnG8W608CygCP828d26yeuN4BLAiyUFeYqnf2CXB+bHyZwQTyolPIthZDnjGi/tjZl2ryzHHHFPMfoxZls0suxzbhlg6XA7ITWX5b9QV9uUZfO9///sbjvPGjRtTJArJyxFHHJHKswjz/ZMFZfLz8s9IkJCX8jve8n1jPyOjbZ4EJGZSRpB2piXs8sQsEVgqB47G1h1JTfLxiuQV5dmcY89t9fexMzAb1R/nRlvzEjOAFy1alH8d9Vlehh0/R7/5zW9GHc+/xPvrysGqt7/97UX27fyc8ufHPvaxUe95/Ou//ut64ovyOVPZ3nvvvUdlKo/+TTRzNWbx5j8fsRT6zDPPnMqt5uzcD3zgA/X/MBM3jP6FkUKAAAECBKoqIABY1ZHVLwIECBCYN4GYyZYnM4gED/Eeu6mUN73pTcVMm3iP1g033DCVyzc7N5IrvP71rx+1vDBmAeVLDze7YAo77rnnnnoG2fJ74f7sz/6s6RpOOeWU4tyYjVd+F9dsLP+Nm8XMqUMPPbR+33iPXgS1ploiMJsHonbZZZcpj3EEReK9bHmJ2WKR7OTRRx/NdxWfYXz88cePWg5eDi4VJ2Yb733ve1O8wyze51eefVY+J9+O5/T888/Pvzb1Trg8qBMXlZPWFJVMYyOCxWeffXZx5VlnnVXPTlvsyDZiVuj73ve+FDPgokTw+IILLqhvz9X/iWWzkTH2uuuumzBjcjwTsfw/nq/y8v1GbY2ZdYcddli9G7HE95WvfGU9O225X5Gs5o/+6I/S/fffX98dz3B4TFQuvfTS9Bd/8RfF4fAtB5CLA1PciOclD8rH76WYMZfPSMyriiW15f+IEeeUk53k583mZ7yj8M///M/TXXfdNe5tIglO/O7JTeLnMcxiRqZCgAABAgSqKrAg+y93tap2Tr8IECBAgMB8CDzvec8rMuxGEKvRjKaJ2nfkkUemm266qX44/lAtLzeNnfE+rTyJR8xoG295YcwYiiBEBNVi6WZeIkAU79zLZ13l+/PPfJlffI+le2OXCsf/dIhMmvHHdSz9Lf9PiRNPPLEepJnK+wWjPTETr1xiZtdMlueW6xpv+9Of/nSKQFOUmPWTBwLGO3e8fTHLMZ+p9Y53vCNFfVMtMZMuxq7c92222aYewItsvRHAi3vEO9/yYGPcIwKH8R638UrMirv11lvrh2J2XjxH8Tw+7nGPq89Ii88IOEWm5fJS3niX3Le//e0i8Dxe3bEvlvz++Mc/rh+O65/+9KdPdOqU9keA77jjjiue+bg4noFoezjFe//KmaVjvCabrfXVr341xRLYsaX8XB1wwAFFNur8vHiGP/jBD+Zfi88IeIZblHh3YbQvfg5jNm20PwLtEUwuZ0mOc2OsysHe2De2xM9yzBJ85JFH6ofi5yfGLp6DCArH2OQzLyNwf/3116eXvvSlY6upf//Zz36WDjnkkOKZiYQ78Tuk/HM97oV/2BkB/LjvROWSSy5JMfswL/Fuw3h+4j92xDNRDuJHVuybb755M+P82viM5zzaO7bEf/yI92NGiXuMXfIegciJZrzecsstKbIwR4kxeu5zn1ufrRrBynvvvXfUz1RYx8zjqbzDs16x/0OAAAECBDpNIAKACgECBAgQINAagSyTZ/yHteJfFmibVsXZbJSijizYUMsCbqPq2X///Yvj5ftNtp3NxqllS3ZH1TX2y2R1jHc8+2O8ls1Kq2WBkLHVTfr94osv3qwv2fLFSa+byQlZcoZaFgCt3zcLAk2pqiwgN6q98X26Ze3atbUs8DCqvvF8Y1/2jr5aFviqZQGTCW+XzdCqZUuLm6ovv0/2/sJaFmiZsM78QDb7rJYFkep1Z8GqfHfLPqMNr3vd6xq2PQyy2XRN3TPLdNuwrrz/Yz/f9ra3jVt/FhicUn1PfvKTa9mrAMata7ydWfCslgUVG94je89kLVteO97lxb74nTO2T1P5ngXsirom2oif2fznZ6K6s1mrtSxoO1EVxf5stvS02pvN2ivqGLsRfZioXeX92czEWjardezlvhMgQIAAgUoKmAGY/a8AhQABAgQItEoglt3Fu7eixNLGeFfcVGbD5e2ImS9xfWQSjfK5z30uvTV7V1peYlZLPgMw3zf2M94RF++Zi9mBMcsu3rfXzFK8ZmYKxQyoWPr6jGc8I8X76GKG0UTvOBvbrrHfsyBYva+x/DFKzOwJt6kk1RhbZzPfY+ZeFsionxpLW8tZThtdHzMH8xl/YVvOitvoukbH7rzzzvospJi9FzOUwiSem5j5FLPsom2xBLWZdyuGY8wYi1lisRTyd7/7XX0mVf4sxdhFPTGbL2Y9RaKVZsY83peWLxn+8pe/XF+y3KhP0z0W7Y4ZWZE4J5Zqbr311vXZX5Fo5bTTTmvqGY57X3755en000+fcjOyAGD92rEXxizMmI0ZSUhiFmRkkI6l7/F+xkigEstH4+cyZrPFsv+Y0TjVn/2YSRi28S9m08Xsv/gZzoKJKWYmxjOw2267jW3aqO8x9nHv6ZaYsRczSScrkZAl3lkYS55jtl7Mrttzzz3r/Y8lwOWkIY3qitnJ03kdQSzbLc9sLt8jnvX4mY7lyN///vfr4xQzM+N3Szz7MTPxpJNOqrexmeQ35bptEyBAgACBThUQAOzUkdNuAgQIECBAYEYCEWiLAF4EESIYcPXVV8+ovna/OAK00d8IEE21RGAqgluxzDWCUbE0OYIpCgECBAgQIECAQGcISALSGeOklQQIECBAgECLBSKQFTOqosT74spJG1p8q46vLmbkRfAvyt/8zd8I/nX8iOoAAQIECBAg0G0CAoDdNuL6S4AAAQIECBQCkSQikhfE8s6/+7u/K/bb+C+ByGidZ7GN5a0nn3zyfx20RYAAAQIECBAg0BECAoAdMUwaSYAAAQIECMyGQLwPLM/4+q//+q8pMqgqowWyhDT19xLGewIvuuiiKb/XbnRtvhEgQIAAAQIECMyHgHcAzoe6exIgQIAAAQIE5lhgJu8AnOOmuh0BAgQIECBAgECLBQQAWwyqOgIECBAgQIAAAQIECBAgQIAAAQLtJGAJcDuNhrYQIECAAAECBAgQIECAAAECBAgQaLGAAGCLQVVHgAABAgQIECBAgAABAgQIECBAoJ0EBADbaTS0hQABAgQIECBAgAABAgQIECBAgECLBQQAWwyqOgIECBAgQIAAAQIECBAgQIAAAQLtJCAA2E6joS0ECBAgQIAAAQIECBAgQIAAAQIEWiwgANhiUNURIECAAAECBAgQIECAAAECBAgQaCcBAcB2Gg1tIUCAAAECBAgQIECAAAECBAgQINBiAQHAFoOqjgABAgQIECBAgAABAgQIECBAgEA7CWzVTo3Rlu4R6OvrS3feeWe9w4973OPSVlt5FLtn9PWUAAECBAgQIECAAAECBOZKYGhoKK1YsaJ+u2c961lpm222matbu08bCYi6tNFgdFNTIvj3whe+sJu6rK8ECBAgQIAAAQIECBAgQGBeBW677bb0ghe8YF7b4ObzI2AJ8Py4uysBAgQIECBAgAABAgQIECBAgACBOREwA3BOmN1krEAs+81L/BeIvfbaK//qkwABAgQIECBAgAABAgQIEGiRwPLly4sVeOW/xVtUvWo6REAAsEMGqmrNLL/zL4J/ixYtqloX9YcAAQIECBAgQIAAAQIECLSVQPlv8bZqmMbMuoAlwLNO7AYECBAgQIAAAQIECBAgQIAAAQIE5k9AAHD+7N2ZAAECBAgQIECAAAECBAgQIECAwKwLCADOOrEbECBAgAABAgQIECBAgAABAgQIEJg/AQHA+bN3ZwIECBAgQIAAAQIECBAgQIAAAQKzLiAAOOvEbkCAAAECBAgQIECAAAECBAgQIEBg/gQEAOfP3p0JECBAgAABAgQIECBAgAABAgQIzLqAAOCsE7sBAQIECBAgQIAAAQIECBAgQIAAgfkTEACcP3t3JkCAAAECBAgQIECAAAECBAgQIDDrAgKAs07sBgQIECBAgAABAgQIECBAgAABAgTmT0AAcP7s3ZkAAQIECBAgQIAAAQIECBAgQIDArAsIAM46sRsQIECAAAECBAgQIECAAAECBAgQmD8BAcD5s3dnAgQIECBAgAABAgQIECBAgAABArMuIAA468RuQIAAAQIECBAgQIAAAQIECBAgQGD+BAQA58/enQkQIECAAAECBAgQIECAAAECBAjMuoAA4KwTuwEBAgQIECBAgAABAgQIECBAgACB+RMQAJw/e3cmQIAAAQIECBAgQIAAAQIECBAgMOsCAoCzTuwGBAgQIECAAAECBAgQIECAAAECBOZPQABw/uzdmQABAgQIECBAgAABAgQIECBAgMCsCwgAzjqxGxAgQIAAAQIECBAgQIAAAQIECBCYPwEBwPmzd2cCBAgQIECAAAECBAgQIECAAAECsy4gADjrxG5AgAABAgQIECBAgAABAgQIECBAYP4EBADnz96dCRAgQIAAAQIECBAgQIAAAQIECMy6gADgrBO39ga33357Ou+889Lxxx+fFi1alLbeeuu0ww47pAMPPDCdeuqp6ZZbbpn0hp///OfTggULmvoX5yoECBAgQIAAAQIECBAgQIAAAQKdK7BV5za9+1p+xBFHpJtvvnmzjg8MDKR77rmn/i8Cdqecckq67LLL0sKFCzc71w4CBAgQIECAAAECBAgQIECAAIHuEhAA7KDxXrZsWb21e++9d3r961+fXvKSl6T99tsvDQ8Ppx/84AfpYx/7WHrooYfSF77whTQ4OJi+9KUvTdq76667LkV9E5WYZagQIECAAAECBAgQIECAAAECBAh0roAAYAeN3dOe9rT0oQ99KJ100klpyy23HNXyQw89NL3lLW9Jhx9+eLr77rvTl7/85fT2t789xazBRiWWDj/hCU9odIpjBAgQIECAAAECBAgQIECAAAECHSzgHYAdNHjXXHNNesMb3rBZ8C/vwu67716fBZh/v/rqq/NNnwQIECBAgAABAgQIECBAgAABAl0qIABYsYE/6qijih7de++9xbYNAgQIECBAgAABAgQIECBAgACB7hQQAKzYuPf39xc9GrtMuDhggwABAgQIECBAgAABAgQIECBAoGsEBAArNtQ33nhj0aODDjqo2J5o49RTT60nAYmMwbGEON4l+P73v7+eTGSia+wnQIAAAQIECBAgQIAAAQIECBDoHAFJQDpnrCZt6cjISLrggguK8+J9gZOV733ve8Upq1atSvHvRz/6Uf1dgp/4xCfSmWeeWRyfysbSpUsbnr58+fKGx9v5YK1WSwsWLGjnJmobAQIECBAgQIAAAQIECBAgQKAQEAAsKDp/48ILL0y33XZbvSMnnnhiOuSQQybs1JOe9KQU5xx22GFp3333rZ933333pX/7t39LkTykr6+vnkU4Al1nnHHGhPVMdCCvc6Ljnbz/0Q396fE7btPJXdB2AgQIECBAgAABAgQIECBAoIsEFmSzmWpd1N/KdjWW/h577LFpaGgo7bHHHunOO++sf47X4XXr1qUdd9xxwllskW04goODg4Npu+22S5FMZM899xyvqgn3TWWG3JIlS9KiRYsmrKvdDtzx4Jr09L12TNs8Zst2a5r2ECBAgAABAgQIECBAgACBUQKxQi+fpNNpf3+P6ogvMxLwDsAZ8bXHxb/61a/SCSecUA/+bbPNNumqq66aMPgXLd5pp50mDP7F8Ve96lXpgx/8YGym3t7edMUVV9S3p/J/4pdKo3/5TMWp1Nku50bM/IFVve3SHO0gQIAAAQIECBAgQIAAAQIECDQUEABsyNP+B++///50/PHHpzVr1qTI+nvllVemI444YsYNj2W/+Sy+cmKRZiuOGX2N/u21117NVtWW563uGUhrewfasm0aRYAAAQIECBAgQIAAAQIECBAoCwgAljU6bHvZsmX1Zb/xGcG6z372s+k1r3lNS3oRy4h32223el0PPfRQS+qsWiWLs1mAVtBXbVT1hwABAgQIECBAgAABAgQIVE9AALBDx3TlypXpuOOOS5G4I8pFF12UTjnllJb2Jp8B2NJKK1TZpoHhtGxdX4V6pCsECBAgQIAAAQIECBAgQIBAFQUEADtwVCOJx8te9rJ011131Vt/wQUXpLPOOqulPVmxYkWKIGOUvffeu6V1V6myh9ZsSv1Dw1Xqkr4QIECAAAECBAgQIECAAAECFRMQAOywAY2kHK985SvTHXfcUW/5ueeem84555yW9+LSSy8tlrceeeSRLa+/KhUOj9TSktUSglRlPPWDAAECBAgQIECAAAECBAhUUUAAsINGdWBgoJ7t99Zbb623+uyzz07nn3/+lHqwePHi9NOf/rThNddcc00677zz6udsu+226dRTT214frcfXLFhIK3vG+x2Bv0nQIAAAQIECBAgQIAAAQIE2lRgqzZtl2aNI/CmN70pXX/99fUjRx99dHrb296WfvnLX45z5u93LVy4MB144IGjjkcA8KijjkqHHXZYevWrX52e85znpEj4ESXeJ3j11VfX/+XJLT760Y+mffbZZ1QdvmwusHhlT3rWPjsVmZM3P8MeAgQIECBAgAABAgQIECBAgMD8CAgAzo/7tO767//+78V13/3ud9Ozn/3s4vt4G/vvv3+KgN945Qc/+EGKfxOV7bbbLl144YXpjDPOmOgU+0sCPf3D6ZH1/WnPnbYp7bVJgAABAgQIECBAgAABAgQIEJh/AQHA+R+DOW3BIYcckv7lX/6lHvy7/fbb0/Lly+vJPoaGhtIuu+ySnvGMZ6RjjjkmnXbaacXMwDltYAffbMma3rTbDgvTY7a0sr6Dh1HTCRAgQIAAAQIECBAgQIBA5QQEADtoSPNluTNp8mMf+9j0J3/yJ/V/M6nHtZsLDA3X0oNZQpAnP26HzQ/aQ4AAAQIECBAgQIAAAQIECBCYJwFTleYJ3m2rKbBiQ3/a2D9Uzc7pFQECBAgQIECAAAECBAgQINCRAgKAHTlsGt2uArVaSpEQRCFAgAABAgQIECBAgAABAgQItIuAAGC7jIR2VEZgQ99QenRDX2X6oyMECBAgQIAAAQIECBAgQIBAZwsIAHb2+Gl9mwosyd4FODQ80qat0ywCBAgQIECAAAECBAgQIECgmwQEALtptPV1zgQGhmpp6ZpNc3Y/NyJAgAABAgQIECBAgAABAgQITCQgADiRjP0EZijw8Pq+1DsgIcgMGV1OgAABAgQIECBAgAABAgQIzFBAAHCGgC4nMJFAJAS5X0KQiXjsJ0CAAAECBAgQIECAAAECBOZIQABwjqDdpjsF1m8aSis39ndn5/WaAAECBAgQIECAAAECBAgQaAsBAcC2GAaNqLLAA6t60/BINh1QIUCAAAECBAgQIECAAAECBAjMg4AA4Dygu2V3CQwMjaRlayUE6a5R11sCBAgQIECAAAECBAgQINA+AgKA7TMWWlJhgQgA9g0OV7iHukaAAAECBAgQIECAAAECBAi0q4AAYLuOjHZVSiBWAC9e1VOpPukMAQIECBAgQIAAAQIECBAg0BkCAoCdMU5aWQGBNT2DaU3PQAV6ogsECBAgQIAAAQIECBAgQIBAJwkIAHbSaGlrxwvELMARCUE6fhx1gAABAgQIECBAgAABAgQIdJKAAGAnjZa2drxA32CWEGSdhCAdP5A6QIAAAQIECBAgQIAAAQIEOkhAALCDBktTqyGwbG1f6h+SEKQao6kXBAgQIECAAAECBAgQIECg/QUEANt/jLSwYgLD2RLgB1b1VqxXukOAAAECBAgQIECAAAECBAi0q4AAYLuOjHZVWmDVxoG0rnew0n3UOQIECBAgQIAAAQIECBAgQKA9BAQA22MctKILBSIhSK1W68Ke6zIBAgQIECBAgAABAgQIECAwlwICgHOp7V4ESgK9A8Pp4fV9pT02CRAgQIAAAQIECBAgQIAAAQKtFxAAbL2pGgk0LbB0zaY0MDTS9PlOJECAAAECBAgQIECAAAECBAhMVUAAcKpizifQQoGh4Vp6cLWEIC0kVRUBAgQIECBAgAABAgQIECAwRkAAcAyIrwTmWmDFhv60oU9CkLl2dz8CBAgQIECAAAECBAgQINAtAgKA3TLS+tnWAotX9koI0tYjpHEECBAgQIAAAQIECBAgQKBzBQQAO3fstLxCAhv7h9Kj2UxAhQABAgQIECBAgAABAgQIECDQagEBwFaLqo/ANAWWZO8CHBqWEGSafC4jQIAAAQIECBAgQIAAAQIEJhAQAJwAxm4Ccy0wmCUEWZJlBVYIECBAgAABAgQIECBAgAABAq0UEABspaa6CMxQ4JH1faknWw6sECBAgAABAgQIECBAgAABAgRaJSAA2CpJ9RBogUCtltL9K3taUJMqCBAgQIAAAQIECBAgQIAAAQK/FxAA9CQQaDOBDX1DaYWEIG02KppDgAABAgQIECBAgAABAgQ6V0AAsHPHTssrLPDg6p40PJJNB1QIECBAgAABAgQIECBAgAABAjMUEACcIaDLCcyGwMBQLS1d0zsbVauTAAECBAgQIECAAAECBAgQ6DIBAcAuG3Dd7RyB5ev60qaB4c5psJYSIECAAAECBAgQIECAAAECbSkgANiWw6JRBFKKhCCLV0kI4lkgQIAAAQIECBAgQIAAAQIEZiYgADgzP1cTmFWBtb2DaXXPwKzeQ+UECBAgQIAAAQIECBAgQIBAtQUEAKs9vnpXAYGYBTgiIUgFRlIXCBAgQIAAAQIECBAgQIDA/AgIAM6Pu7sSaFqgf3AkPbR2U9PnO5EAAQIECBAgQIAAAQIECBAgUBYQACxr2CbQpgLLsgBg36CEIG06PJpFgAABAgQIECBAgAABAgTaWkAAsK2HR+MI/F4gVgA/sKoXBwECBAgQIECAAAECBAgQIEBgygICgFMmcwGB+RGIZCBreyUEmR99dyVAgAABAgQIECBAgAABAp0rIADYuWOn5V0osDibBVirZdMBFQIECBAgQIAAAQIECBAgQIBAkwICgE1COY1AOwhsGhhOy9f1tUNTtIEAAQIECBAgQIAAAQIECBDoEAEBwA4ZKM0kkAssXbMp9Q9JCJJ7+CRAgAABAgQIECBAgAABAgQaCwgANvZxlEDbCQxnGUGWrJYQpO0GRoMIECBAgAABAgQIECBAgECbCggAtunAaBaBRgIrNgyk9X2DjU5xjAABAgQIECBAgAABAgQIECBQFxAA9CAQ6FCBxSt7JATp0LHTbAIECBAgQIAAAQIECBAgMJcCAoBzqe1eBFoo0NM/nB5Z39/CGlVFgAABAgQIECBAgAABAgQIVFFAALCKo6pPXSOwZE1vGhwe6Zr+6igBAgQIECBAgAABAgQIECAwdQEBwKmbuYJA2wgMDdfSgxKCtM14aAgBAgQIECBAgAABAgQIEGhHAQHAdhwVbSIwBYEVG/rTxv6hKVzhVAIECBAgQIAAAQIECBAgQKCbBAQAu2m09bWSArVaSpEQRCFAgAABAgQIECBAgAABAgQIjCcgADiein0EOkxgQ99QenRDX4e1WnMJECBAgAABAgQIECBAgACBuRAQAJwLZfcgMAcCS7J3AQ5JCDIH0m5BgAABAgQIECBAgAABAgQ6S0AAsLPGS2sJTCgwMFRLS9dsmvC4AwQIECBAgAABAgQIECBAgEB3CggAdue463VFBR5e35d6ByQEqejw6hYBAgQIECBAgAABAgQIEJiWgADgtNhcRKA9BSIhyP0SgrTn4GgVAQIECBAgQIAAAQIECBCYJwEBwHmCd1sCsyWwftNQWrmxf7aqVy8BAgQIECBAgO3q9ekAAEAASURBVAABAgQIECDQYQICgB02YJpLoBmBB1b1puGRbDqgQoAAAQIECBAgQIAAAQIECHS9gABg1z8CAKooMDA0kpatlRCkimOrTwQIECBAgAABAgQIECBAYKoCAoBTFXM+gQ4RiABg3+Bwh7RWMwkQIECAAAECBAgQIECAAIHZEhAAnC1Z9RKYZ4FYAbx4Vc88t8LtCRAgQIAAAQIECBAgQIAAgfkWEACc7xFwfwKzKLCmZzCt6RmYxTuomgABAgQIECBAgAABAgQIEGh3AQHAdh8h7SMwQ4GYBTgiIcgMFV1OgAABAgQIECBAgAABAgQ6V0AAsHPHTssJNCXQN5glBFknIUhTWE4iQIAAAQIECBAgQIAAAQIVFBAArOCg6hKBsQLL1val/iEJQca6+E6AAAECBAgQIECAAAECBLpBQACwG0ZZH7teYDhbAvzgqt6udwBAgAABAgQIECBAgAABAgS6UUAAsBtHXZ+7UmDlxoG0btNgV/ZdpwkQIECAAAECBAgQIECAQDcLCAB28+jre9cJLF7Zk2q1Wtf1W4cJECBAgAABAgQIECBAgEA3CwgAdvPo63vXCfQODKeH1/d1Xb91mAABAgQIECBAgAABAgQIdLOAAGA3j76+d6XA0jWb0sDQSFf2XacJECBAgAABAgQIECBAgEA3CggAduOo63NXCwwNZwlBVksI0tUPgc4TIECAAAECBAgQIECAQFcJCAB21XDrLIHfC6zY0J829EkI4nkgQIAAAQIECBAgQIAAAQLdICAA2A2jrI8ExhFYvLJXQpBxXOwiQIAAAQIECBAgQIAAAQJVExAArNqI6g+BJgU29g+lmAmoECBAgAABAgQIECBAgAABAtUWEACs9vjqHYGGAvEuwKFhCUEaIjlIgAABAgQIECBAgAABAgQ6XEAAsMMHUPMJzERgMEsIsiTLCqwQIECAAAECBAgQIECAAAEC1RUQAKzu2OoZgaYEHlnfl3qy5cAKAQIECBAgQIAAAQIECBAgUE0BAcBqjqteEWhaoFZL6f6VPU2f70QCBAgQIECAAAECBAgQIECgswQEADtrvLSWwKwIbOiTEGRWYFVKgAABAgQIECBAgAABAgTaQEAAsA0GQRMItIPAg6t70vBINh1QIUCAAAECBAgQIECAAAECBColIABYqeHUGQLTFxgYqqWla3qnX4ErCRAgQIAAAQIECBAgQIAAgbYUEABsy2HRKALzI7B8XV/aNDA8Pzd3VwIECBAgQIAAAQIECBAgQGBWBAQAZ4VVpQQ6UyASgixeJSFIZ46eVhMgQIAAAQIECBAgQIAAgfEFBADHd7GXQNcKrO0dTKt7Brq2/zpOgAABAgQIECBAgAABAgSqJiAAWLUR1R8CLRCIWYAjEoK0QFIVBAgQIECAAAECBAgQIEBg/gUEAOd/DLSAQNsJ9A+OpIfWbmq7dmkQAQIECBAgQIAAAQIECBAgMHUBAcCpm7mCQFcILMsCgH2DEoJ0xWDrJAECBAgQIECAAAECBAhUWkAAsNLDq3MEpi8QK4AfWNU7/QpcSYAAAQIECBAgQIAAAQIECLSFgABgWwyDRhBoT4FIBrK2V0KQ9hwdrSJAgAABAgQIECBAgAABAs0JCAA25+QsAl0rsDibBVirZdMBFQIECBAgQIAAAQIECBAgQKAjBQQAO3LYNJrA3AlsGhhOy9f1zd0N3YkAAQIECBAgQIAAAQIECBBoqYAAYEs5VUagmgJL12xK/UMSglRzdPWKAAECBAgQIECAAAECBKouIABY9RHWPwItEBjOMoIsWS0hSAsoVUGAAAECBAgQIECAAAECBOZcQABwzsndkEBnCqzYMJDW9w12ZuO1mgABAgQIECBAgAABAgQIdLGAAGAXD76uE5iqwOKVPRKCTBXN+QQIECBAgAABAgQIECBAYJ4FBADneQDcnkAnCfT0D6dH1vd3UpO1lQABAgQIECBAgAABAgQIdL2AAGDXPwIACExNYMma3jQ4PDK1i5xNgAABAgQIECBAgAABAgQIzJuAAOC80bsxgc4UGBqupQclBOnMwdNqAgQIECBAgAABAgQIEOhKAQHArhx2nSYwM4EVG/rTxv6hmVXiagIECBAgQIAAAQIECBAgQGBOBAQA54TZTQhUS6BWSykSgigECBAgQIAAAQIECBAgQIBA+wsIALb/GGkhgbYU2NA3lB7d0NeWbdMoAgQIECBAgAABAgQIECBA4L8EBAD/y8IWAQJTFFiSvQtwSEKQKao5nQABAgQIECBAgAABAgQIzK2AAODcersbgUoJDAzV0tI1myrVJ50hQIAAAQIECBAgQIAAAQJVExAArNqI6g+BORZ4eH1f6h2QEGSO2d2OAAECBAgQIECAAAECBAg0LSAA2DSVEwkQGE8gEoLcLyHIeDT2ESBAgAABAgQIECBAgACBthAQAGyLYdAIAp0tsH7TUFq5sb+zO6H1BAgQIECAAAECBAgQIECgogICgBUdWN0iMNcCD6zqTcMj2XRAhQABAgQIECBAgAABAgQIEGgrAQHAthoOjSHQuQIDQyNp2VoJQTp3BLWcAAECBAgQIECAAAECBKoqIABY1ZHVLwLzIBABwL7B4Xm4s1sSIECAAAECBAgQIECAAAECEwkIAE4kYz8BAlMWiBXAi1f1TPk6FxAgQIAAAQIECBAgQIAAAQKzJyAAOHu2aibQlQJregbTmp6Bruy7ThMgQIAAAQIECBAgQIAAgXYUEABsx1HRJgIdLhCzAEckBOnwUdR8AgQIECBAgAABAgQIEKiKgABgVUZSPwi0kUDfYJYQZJ2EIG00JJpCgAABAgQIECBAgAABAl0sIADYxYOv6wRmU2DZ2r7UPyQhyGwaq5sAAQIECBAgQIAAAQIECDQjIADYjJJzCBCYssBwtgT4wVW9U77OBQQIECBAgAABAgQIECBAgEBrBQQAW+upNgIESgIrNw6kdZsGS3tsEiBAgAABAgQIECBAgAABAnMtIAA41+LuR6DLBBav7Em1Wq3Leq27BAgQIECAAAECBAgQIECgfQQEANtnLLSEQCUFegeG08Pr+yrZN50iQIAAAQIECBAgQIAAAQKdICAA2AmjpI0EOlxg6ZpNaWBopMN7ofkECBAgQIAAAQIECBAgQKAzBQQAO3PctJpARwkMDWcJQVZLCNJRg6axBAgQIECAAAECBAgQIFAZAQHAygyljhBob4EVG/rThj4JQdp7lLSOAAECBAgQIECAAAECBKooIABYxVHVJwJtKrB4Za+EIG06NppFgAABAgQIECBAgAABAtUVEADssLG9/fbb03nnnZeOP/74tGjRorT11lunHXbYIR144IHp1FNPTbfccsuUevTNb34znXDCCUVdUWd8j/0KgVYLbOwfSjETUCFAgAABAgQIECBAgAABAgTmTmBBLStzdzt3monAEUcckW6++eZJqzjllFPSZZddlhYuXDjhuSMjI+mMM85IV1xxxYTnnHbaaemSSy5JW2zR+jjx0qVL07777lu/95IlS+oByAkb0mYHfvLA6iyhhR+b6Q7LY7ZckJ67785pqy1b/1xNt02uI0CAAAECBAgQIECAQFUFOvnv76qOyXz0y1/g86E+zXsuW7asfuXee++dzj777HT11Ven2267Lf3gBz9IH//4x9M+++xTP/6FL3whvfWtb214l3PPPbcI/h188MHpy1/+cr2u+IzvUS6//PL0/ve/v2E9DhKYqsBglhBkSZYVWCFAgAABAgQIECBAgAABAgTmRsAMwLlxbsldXvWqV6WY3XfSSSelLbfccrM6V65cmQ4//PB0991314/deOONKWYNji1x/BnPeEYaGhpKz3/+89NNN92Utt122+K03t7edOSRR6ZYbrzVVlulX//61+mAAw4ojrdio5P/C4QZgDN/AhYsSOlZ++yUtt96q5lXpgYCBAgQIECAAAECBAgQmFCgk//+nrBTDkxZwAzAKZPN3wXXXHNNesMb3jBu8C9atfvuu6ePfexjRQNjhuB45ROf+EQ9+BfHLrroolHBv9i33Xbb1ffHdgQJL7zwwthUCLRMIF48cP/KnpbVpyICBAgQIECAAAECBAgQIEBgYgEBwIltOvLIUUcdVbT73nvvLbbzjXjl49e+9rX616c97Wnp0EMPzQ+N+oz9T33qU+v74nyvihzF40sLBDb0SQjSAkZVECBAgAABAgQIECBAgACBSQUEACcl6qwT+vv/K8PqeMuE77///pS/SzCW+TYq+fGHHnooLV68uNGpjhGYlsCDq3vT8IiEKtPCcxEBAgQIECBAgAABAgQIEGhSQACwSahOOS3e+5eXgw46KN8sPu+6665iO2YANirl4/EeQIVAqwUGhkbS0jW9ra5WfQQIECBAgAABAgQIECBAgEBJwBv4SxidvjkyMpIuuOCCohvxvsCxJV7+mZdFixblm+N+7rvvvsX+JUuWFNvNbJTvM975y5cvH2+3fV0osHxdX9rjsdukbRduntimCzl0mQABAgQIECBAgAABAgQItFxAALDlpPNXYSTruO222+oNOPHEE9MhhxyyWWM2bNhQ7Nthhx2K7fE2tt9++2L3xo0bi+1mNsrBw2bOd073CkRCkMWretJBe+3YvQh6ToAAAQIECBAgQIAAAQIEZlHAEuBZxJ3LqmPp7//6X/+rfss99tgjfeYznxn39n19fcX+hQsXFtvjbWy99dbF7k2bNhXbNgi0WmBt72Ba3TPQ6mrVR4AAAQIECBAgQIAAAQIECGQCZgBW4DH41a9+lU444YQ0NDSUttlmm3TVVVelCAKOV+J4XgYGGgdcyglFtt122/yypj4nWzIcS4Bf+MIXNlWXk7pDIGYB7rztY9IWWyzojg7rJQECBAgQIECAAAECBAgQmCMBAcA5gp6t20RW3+OPPz6tWbMmRdbfK6+8Mh1xxBET3u6xj31scWyyZb09PT3FuZMtFy5O/MPGZO8XHHu+7wT6B0fSQ2s3pX133Q4GAQIECBAgQIAAAQIECBAg0EIBS4BbiDnXVS1btiwde+yxKT4XLFiQPvvZz6bXvOY1DZtRDsxNlqijPIvPO/0asjrYIoFlWQCwb3C4RbWphgABAgQIECBAgAABAgQIEAgBAcAOfQ5WrlyZjjvuuHTffffVe3DRRRelU045ZdLePP3pTy/O+c1vflNsj7dRPn7QQQeNd4p9BFoqMJIlBHlgVW9L61QZAQIECBAgQIAAAQIECBDodgEBwA58AtatW5de9rKXpbvuuqve+gsuuCCdddZZTfXkiU98Ytp7773r50bikEblpptuqh/eZ5990hOe8IRGpzpGoGUCkQxkbW/j91O27GYqIkCAAAECBAgQIECAAAECXSAgANhhg9zb25te+cpXpjvuuKPe8nPPPTedc845Tfcilgrny4Rjht8Pf/jDca+N/fkMwDg/rlMIzJXA4mwWYK2WTQdUCBAgQIAAAQIECBAgQIAAgRkLCADOmHDuKoisvZHt99Zbb63f9Oyzz07nn3/+lBvw3ve+t54wJC5897vfnTZt2jSqjvge+6NstdVWKc5XCMylwKaB4bR8Xd9c3tK9CBAgQIAAAQIECBAgQIBAZQVkAe6goX3Tm96Urr/++nqLjz766PS2t70t/fKXv5ywBwsXLkwHHnjgZsdj3/ve974US4dvv/32dPjhh9dnET75yU9O9957b/rwhz+cfvrTn9avi/Oe8pSnbFaHHQRmW2Dpmk1p9x22Tgu38t8pZtta/QQIECBAgAABAgQIECBQbYEF2TI76+w6ZIynugx3//33T4sXLx63dyMjI+n000+vZw4e94RsZwQYL7300rTFFq0PwEQG4jyzcGQbLmcnnqg97bL/Jw+sTgNDfmzmYjwe99iF6YA9HjsXt3IPAgQIECBAgAABAgQIVFKgk//+ruSAzFOnWh/ZmaeOuO3UBCKod8UVV6RvfOMb9XcCRmKQmDEYn/HOv2uvvTZdfvnlsxL8m1pLnd3NAis2DKT1fYPdTKDvBAgQIECAAAECBAgQIEBgxgKWAM+YcO4qmI3Jmq94xStS/FMItKvA4pU96Vn77CQRTbsOkHYRIECAAAECBAgQIECAQNsLmAHY9kOkgQS6W6Cnfzg9sr6/uxH0ngABAgQIECBAgAABAgQIzEBAAHAGeC4lQGBuBJas6U2DwyNzczN3IUCAAAECBAgQIECAAAECFRMQAKzYgOoOgSoKDA3X0pLVvVXsmj4RIECAAAECBAgQIECAAIFZFxAAnHViNyBAoBUCj27oTxv7h1pRlToIECBAgAABAgQIECBAgEBXCQgAdtVw6yyBzhWo1VKKhCAKAQIECBAgQIAAAQIECBAgMDUBAcCpeTmbAIF5FNjQN5Qe3dA3jy1wawIECBAgQIAAAQIECBAg0HkCAoCdN2ZaTKCrBeJdgEMSgnT1M6DzBAgQIECAAAECBAgQIDA1AQHAqXk5mwCBeRYYGKqlpWs2zXMr3J4AAQIECBAgQIAAAQIECHSOgABg54yVlhIg8AeBh9f3pd4BCUE8EAQIECBAgAABAgQIECBAoBkBAcBmlJxDgEBbCfw+IUhvW7VJYwgQIECAAAECBAgQIECAQLsKCAC268hoFwECDQXWbRpMqzb2NzzHQQIECBAgQIAAAQIECBAgQCAlAUBPAQECHSuweFVvGh6pdWz7NZwAAQIECBAgQIAAAQIECMyFgADgXCi7BwECsyIwMDSSlq2VEGRWcFVKgAABAgQIECBAgAABApUREACszFDqCIHuFIgAYN/gcHd2Xq8JECBAgAABAgQIECBAgEATAgKATSA5hQCB9hWIFcCLV/W0bwO1jAABAgQIECBAgAABAgQIzLOAAOA8D4DbEyAwc4E1PYNpTc/AzCtSAwECBAgQIECAAAECBAgQqKCAAGAFB1WXCHSjQMwCHJEQpBuHXp8JECBAgAABAgQIECBAYBIBAcBJgBwmQKAzBPoGs4Qg6yQE6YzR0koCBAgQIECAAAECBAgQmEsBAcC51HYvAgRmVWDZ2r7UPyQhyKwiq5wAAQIECBAgQIAAAQIEOk5AALDjhkyDCRCYSGA4WwL84KreiQ7bT4AAAQIECBAgQIAAAQIEulJAALArh12nCVRXYOXGgbRu02B1O6hnBAgQIECAAAECBAgQIEBgigICgFMEczoBAu0vsHhlT6rVau3fUC0kQIAAAQIECBAgQIAAAQJzICAAOAfIbkGAwNwK9A4Mp4fX983tTd2NAAECBAgQIECAAAECBAi0qYAAYJsOjGYRIDAzgaVrNqWBoZGZVeJqAgQIECBAgAABAgQIECBQAQEBwAoMoi4QILC5wNBwlhBktYQgm8vYQ4AAAQIECBAgQIAAAQLdJiAA2G0jrr8EukhgxYb+tKFPQpAuGnJdJUCAAAECBAgQIECAAIFxBAQAx0GxiwCB6ggsXtkrIUh1hlNPCBAgQIAAAQIECBAgQGAaAgKA00BzCQECnSOwsX8oxUxAhQABAgQIECBAgAABAgQIdKuAAGC3jrx+E+gigXgX4NCwhCBdNOS6SoAAAQIECBAgQIAAAQIlAQHAEoZNAgSqKTCYJQRZkmUFVggQIECAAAECBAgQIECAQDcKCAB246jrM4EuFHhkfV/qyZYDKwQIECBAgAABAgQIECBAoNsEBAC7bcT1l0CXCtRqKd2/sqdLe6/bBAgQIECAAAECBAgQINDNAgKA3Tz6+k6gywQ29EkI0mVDrrsECBAgQIAAAQIECBAgkAkIAHoMCBDoKoFICDI8kk0HVAgQIECAAAECBAgQIECAQJcICAB2yUDrJgECvxcYGBpJD0kI4nEgQIAAAQIECBAgQIAAgS4SEADsosHWVQIEfi+wfN2mtGlgGAcBAgQIECBAgAABAgQIEOgKAQHArhhmnSRAoCwQK4AXr5IQpGximwABAgQIECBAgAABAgSqKyAAWN2x1TMCBBoIrO0dTKt7Bhqc4RABAgQIECBAgAABAgQIEKiGgABgNcZRLwgQmIZAzAIckRBkGnIuIUCAAAECBAgQIECAAIFOEhAA7KTR0lYCBFoq0D+YJQRZu6mldaqMAAECBAgQIECAAAECBAi0m4AAYLuNiPYQIDCnAsuyAGDfoIQgc4ruZgQIECBAgAABAgQIECAwpwICgHPK7WYECLSbQKwAfmBVb7s1S3sIECBAgAABAgQIECBAgEDLBAQAW0apIgIEOlUgkoGsy5KCKAQIECBAgAABAgQIECBAoIoCAoBVHFV9IkBgygL3ZwlBarVsOqBCgAABAgQIECBAgAABAgQqJiAAWLEB1R0CBKYnsGlgOC1f1ze9i11FgAABAgQIECBAgAABAgTaWEAAsI0HR9MIEJhbgaVrNqWBoZG5vam7ESBAgAABAgQIECBAgACBWRYQAJxlYNUTINA5AsNZRpAHV/d0ToO1lAABAgQIECBAgAABAgQINCEgANgEklMIEOgegRUbBtL6PglBumfE9ZQAAQIECBAgQIAAAQLVFxAArP4Y6yEBAlMUWLxSQpApkjmdAAECBAgQIECAAAECBNpYQACwjQdH0wgQmB+Bnv7h9Mj6/vm5ubsSIECAAAECBAgQIECAAIEWCwgAthhUdQQIVENg6ZreNDgsIUg1RlMvCBAgQIAAAQIECBAg0N0CAoDdPf56T4DABAKDw7W0ZHXvBEftJkCAAAECBAgQIECAAAECnSMgANg5Y6WlBAjMscCjG/rTxv6hOb6r2xEgQIAAAQIECBAgQIAAgdYKCAC21lNtBAhUSKBWSykSgigECBAgQIAAAQIECBAgQKCTBQQAO3n0tH1eBHoHzAibF/h5uumGvqH06Ia+ebq72xIgQIAAAQIECBAgQIAAgZkLCADO3FANXSIwMlJLF37r7nT2lT9Pa3oHuqTXuhkC8S7AIQlBPAwECBAgQIAAAQIECBAg0KECAoAdOnCaPbcCG/oG0xlf/En65HfuSat7BuqBQBli53YM5vNuA0O1tHTNpvlsgnsTIECAAAECBAgQIECAAIFpCwgATpvOhd0k8KFrf5O+/etHii7f8+jG9LlbF6davCRO6QqBh9f3Jcu/u2KodZIAAQIECBAgQIAAAQKVExAArNyQ6tBsCPzly56aFu2y7aiqb/jto1lQ8NFR+3yprsDvE4L0VreDekaAAAECBAgQIECAAAEClRUQAKzs0OpYKwV22X5huvQtz0/bPGb0j8w/f39x+s3y9a28lbraWGDdpsG0amN/G7dQ0wgQIECAAAECBAgQIECAwOYCo6MZmx+3hwCBPwg8fe8d00de95xRHsPZtLALs/cCCgqNYqn0l8WretNwlhBGIUCAAAECBAgQIECAAAECnSIgANgpI6WdbSHw6ufsnV578N6j2rI+mxX28Sw78MDQyKj9vlRTIMZ56RpLgas5unpFgAABAgQIECBAgACBagoIAFZzXPVqFgVOfuF+6dmLdhp1h/tW9qQrbrlPUpBRKtX9snydhCDVHV09I0CAAAECBAgQIECAQPUEBACrN6Z6NMsCW26xIL376Kekx++49ag73XTPynTdr/4rU/Cog75USiASgty3oqdSfdIZAgQIECBAgAABAgQIEKiugABgdcdWz2ZRYIett0p/ftxT09Zbjf4R+uIPF6e7lq2bxTurul0ENvQNpUc39LVLc7SDAAECBAgQIECAAAECBAhMKDA6ejHhaQ4QIDBWYN9dt0vvfOkBo3ZHbohPZElBVmyQKXYUTEW/PJglBBkc9u7Hig6vbhEgQIAAAQIECBAgQKAyAgKAlRlKHZkPgRc+cdd04sH7jLp1zAz7+Ld+m/qHhkft96V6AoPDtfTgaglBqjeyekSAAAECBAgQIECAAIFqCQgAVms89WYeBE46ZFF63n47j7rz4mxm2GU3SQoyCqWiXx5d35/W9w1WtHe6RYAAAQIECBAgQIAAAQJVEBAArMIo6sO8CmyxYEE666gD0t47bTOqHbfeuyp9487lo/b5Uk2B+7OEILXIDKIQIECAAAECBAgQIECAAIE2FBAAbMNB0aTOE9huYZYU5Pinpm0fs+Woxn/ptgfTL5auHbXPl+oJ9A4Mp+XrJASp3sjqEQECBAgQIECAAAECBKohIABYjXHUizYQ2HvnbdO7spmAC0ptiUlh//Tde9Ij6wWHSiyV3Fy6ZpP3PlZyZHWKAAECBAgQIECAAAECnS8gANj5Y6gHbSTwvP13Sa/L3glYLj39w+lj37o79Q1KClJ2qdr2cJYCevFKCUGqNq76Q4AAAQIECBAgQIAAgSoICADOcBS//vWvp7e85S3p5S9/eXrnO9+Z7rjjjhnW6PJOF3htlhX4BU/YZVQ3lmSZYi++8V7viRulUr0vq3sG0prsn0KAAAECBAgQIECAAAECBNpJQACwwWjccMMNaY899kj77bdfWrt28/e4feADH0ivfe1r05e+9KV0/fXXp0suuSQdeuih6Ytf/GKDWh2qukAkBXnHkQekfbIlweXyo/tXp6/9fFl5l+0KCty/qieNZLMBFQIECBAgQIAAAQIECBAg0C4CAoANRuLaa69NK1euTC94wQvSzjvvPOrMX/ziF+lDH/pQfUZXZP+M4/E5NDSUzjzzzLR48eJR5/vSXQLbLtwy/UWWFGT77LNcvvLjJemnD64p77JdMYH+wZEU7wNUCBAgQIAAAQIECBAgQIBAuwgIADYYiVtuuSUtyGZzHXvssZud9ZnPfKYe8Ntll13ST37yk7Rq1ap02223pV133TX19/eniy++eLNr7OgugT132ia96+injE4KkhF86obfZRljBYiq/DQsy8Z3U5YZWCFAgAABAgQIECBAgAABAu0gIADYYBSWL19eP/qMZzxjs7OuueaaenDwXe96Vzr44IPrx5///Oen+B4zAb/97W9vdo0d3Sfw3H13Tm98wb6jOt6bBYY+dv3dAkSjVKr1JbI/37dyY7U6pTcECBAgQIAAAQIECBAg0LECAoANhm7FihX1o2OX/957773poYceqh874YQTRtXwkpe8pP49zlEIhMCrn7N3OvRJu47CeGjtpvTp7/0ujUSkSKmkwPpNQ2nFhv5K9k2nCBAgQIAAAQIECBAgQKCzBAQAG4xXzOSLsm7dulFn3XzzzfXvO+20U3ruc5876thuu+1W/97b2ztqvy/dKxDLyM884slpv123G4Vw+wNr0ld/+vtA8qgDvlRG4MHVPWloeKQy/dERAgQIECBAgAABAgQIEOhMAQHABuO255571o/++te/HnXWddddV/9++OGHj9ofX3p6eur74t2ACoFcYJvHbJn+/LgD0w5bb5Xvqn9e/ZOl6fYHVo/a50t1BAaGaunB1f5jQHVGVE8IECBAgAABAgQIECDQmQICgA3G7dBDD62/zy8SfuQz+u677770ta99rf7+v+OOO26zq+++++76vjx4uNkJdnStwB47bpPOPiZLCrJgNMGnb8iWlMsaOxqlQt8eWd+fNvQNVqhHukKAAAECBAgQIECAAAECnSYgANhgxE477bT60V/84hfpmc98Znrd616XIijY19eXtt1223TyySdvdvVNN91U33fggQdudswOAs/cZ6f05hftPwpi0+Bw+uj1v009/UOj9vtSHYH7V/bU/2NCdXqkJwQIECBAgAABAgQIECDQSQICgA1G6+ijj05nn312/Q/3xYsXp69+9atp5cqV9Ss+8pGPpN13333U1REYzGcHHnHEEaOO+UIgF3j5M/dMLz5g9LPz8Pq+9KkbsqQgI5KC5E5V+uzpH04xxgoBAgQIECBAgAABAgQIEJgPgdEvJJuPFrT5PS+88MJ0zDHHpKuuuio9/PDDaa+99kqnnHJKiuDg2PIf//Efaccdd0yRHOTVr3712MO+E6gLRFKQ01/ypBSZgGNmWF5+tmRtuip7J+Afv2DffJfPCgksWb0p7br9wrT1VltWqFe6QoAAAQIECBAgQIAAAQKdILAgy3RrylEnjFTF2rh06dK0776/D3QtWbIkLVq0qGN6+JMsaUckd5hpWbmxP5371TvT+r7RS3/fm70n8EVP+n026Znew/XtJbDbDgvTgY9/bHs1SmsIECBAgAABAgQIEKi0QCf//V3pgZnjzlkCPMfgbkcgF9h9h63T2ccemLYYkxTkMzfem5bIHJszVepz1caBtLZ3oFJ90hkCBAgQIECAAAECBAgQaH8BAcBpjNHQ0FBasWJF/V9sKwSmK/D0vXZMpxz2hFGX9w+N1JOCbBwzM3DUSb50rMD/Z+9O4KsqzsaPP2RfSYCwJ2yBAJFFaVHcQEGoWqu4vJW2VlHr1kWr1Ne2+tf6Vq20itjF1l3b+rav1brigiKCuCDuImQhe0LIvu/3hv/MoTe5J8vNfu859/7m84m558ycc2a+k+DN3Jl59LJv9nq0bfdRcQQQQAABBBBAAAEEEEDAlgIMAPaz2/bt2yfXXnutpKamSkREhEyaNMn40q/nz58vP/nJT2Tv3r39vBvFEOgUWJM6UVakjO88oV6V1rXIH97KZKDIpOIfB81t7cb+j/7RGlqBAAIIIIAAAggggAACCCBgBwEGAPvopfb2dtmwYYMsXrxY/vSnP0laWpoalGk3IgPr7RP16/T0dHnggQfkmGOOkeuvv94418dtyUagQ0AHBbnsxJmSPD6645x+8UVRjfxzT77pHAf+IXBQBYBpanX6R2NoBQIIIIAAAggggAACCCCAgOUFiALcRxd997vfNSIAu2KlHHXUUXLsscfKxIkTjStLSkpkz549xuw/p9Mpv//97+XgwYPyf//3f33cmWwEOgXCQoLkhtVz5ZcqKEhNU1tHxktfFMuMhGg5ITmh4xwv7C/QrmLI6KXAqVNG278xtAABBBBAAAEEEEAAAQQQQMDyAgwAeuiif/7zn/L000+LnqGlZwA+9NBDsnTp0h6v0IOAV199tXz66afyzDPPiL523bp1PZblJAI9CYyNDpPrVVCQX2/ZJ049QvSf9OCObJkSHykzxplnCLry+W5PAT3QW6aWeo+PDbdnA6g1AggggAACCCCAAAIIIICAbQRYAuyhq/SAn04pKSmya9euXgf/dBk9MLhz506ZO3eusTz4wQcf1KdJCAxIYO6kWLn0hBmma1qd7XLv1nSpbe6cGWgqwIFtBfIrG8Sh+peEAAIIIIAAAggggAACCCCAwEgKMADoQffzzz83Zv/ddNNNEh3d9+wrXUaX1UlfS0JgMAKr5k+UVfMmmC4tr2+V+9/MNM0MNBXgwJYCrY7DUlDVZMu6U2kEEEAAAQQQQAABBBBAAAH7CDAA6KGvWltbjdxFixZ5KGXOcpVta2O2llmGo4EIrFezAOdOjDVdsq+4Vp7anWc6x4H9BUpqm6W+xWH/htACBBBAAAEEEEAAAQQQQAABywowAOiha6ZPn27k1tTUeChlzqqtrTVOuK4153KEQP8EQoKD5KenzZExUaGmC17de0h2ZpSZznFgbwEVTFxyyhqMrQPs3RJqjwACCCCAAAIIIIAAAgggYFUBBgA99Mz5559v/FH+7LPPeihlztIBQHTQkHPPPdecwRECAxSIjwozIgOHBI0yXfnIrmzJKqs3nePA3gJ6BmBJbYu9G0HtEUAAAQQQQAABBBBAAAEELCvAAKCHrrnhhhtk1qxZogN66GjAfSU9+KfLzpw5U372s5/1VZx8BPoUmD0hRi4/aaapXJvzsGx6I0OqG48sUTdlcmBbgYKqRml1EBDEth1IxRFAAAEEEEAAAQQQQAABCwswAOihc+Li4uTNN9+UJUuWyHe+8x1Zu3atPP/881JUVCR6jz+Hw2G81uf0jL8LL7zQKLtt2zbR15IQGA6BU+ZOkDWpE023qmxQQUG2ZRJB1qRi7wOHGtjNq2iwdyOoPQIIIIAAAggggAACCCCAgCUFRh1WyZI1s0ClgoODO2qhmfTSXk+pP2X0PfTAYaCnwsJCSUpKMhgKCgokMTHRNiQf51WqmVre/bVxtLfLnVv2S9qhOpOTHhi89ETzDEFTAQ5sJ5A6ebTEddn70XaNoMIIIIAAAggggAACCCBgGQE7//1tGUQ/qAgzAD10oh7Qc33pYq7XvX3vTxl9LQmBgQqEBOmgICkyLjrMdOnWfSWyPa3UdI4Dewtkl9dLezv/Tti7F6k9AggggAACCCCAAAIIIGAtgRBrVcdatbntttusVSFqE9ACcZGhKihIivzqpa9E7wPoSo+9myOJYyJlzsRY1ym+21igua1diqqbJGlslI1bQdURQAABBBBAAAEEEEAAAQSsJMAAoIfeYADQAw5ZPhGYNT5Grjh5ljzwdlbH8x1qttimNzPkrnMXyhgVOZhkf4GDagBwfGy4RIR2bkNg/1bRAgQQQAABBBBAAAEEEEAAAV8JsATYV/I8F4FBCpw8Z7ycuXCy6erqxja5T0UGbnMSRdYEY9MDvQI4p5yAIDbtPqqNAAIIIIAAAggggAACCFhOgAFAy3UJFUKgb4HvHjtNFkwZbSqYWVovj7+ba+xVacrgwJYCelC3vL7FlnWn0ggggAACCCCAAAIIIIAAAtYSYAnwAPpDR+/95JNP5Msvv5TKykrjyrFjx8qCBQtkyZIlEhoaOoC7URSBwQsEB42Sa1fNkZuf2ytlboNE29NLZWZCtKxW0YFJ9hfIq2g0lnXr/iYhgAACCCCAAAIIIIAAAgggMFgBBgD7IdfQ0CC//vWv5dFHH+0Y+Ot62ZgxY+Tyyy+XW265RWJjCcbQ1Yfj4ReIjQiVDWtS5LYXv5IWR+fS3yffy5UkFRRk3mTzDMHhrwF3HGmBVtWvBZWNMkMN6pIQQAABBBBAAAEEEEAAAQQQGKwAS4D7kEtPTzdm+P3ud7+TiooKY3nl4cOHu33XMwLvueceWbhwoehrSAh4Q2D6uGi5anmy6VFO9fN537ZMqXCbGWgqwIGtBA7VNktDi8NWdaayCCCAAAIIIIAAAggggAAC1hJgANBDf9TU1MiqVaskPz/fGPDTS331QOCOHTskLS3N+NKvXQN/emBQlz3ttNNEX0tCwBsCxyePk7MXTzE9qrapTTapoCB6BhnJ3gLqnxUjIIj+94WEAAIIIIAAAggggAACCCCAwGAEGAD0oLZx40Y5ePCgUUIvAf78889lw4YNcvLJJ0tKSorxpV/fcMMN8tlnn8kdd9xhlNXX6GtJCHhL4MKvJ8nixDjT47JVFNlHd2UTFMSkYs+DumaHlNYREMSevUetEUAAAQQQQAABBBBAAAHfCzAA6KEPnnvuORk1apR8+9vflptvvtl43VtxXe6Xv/ylXHjhhcaAi76WhIC3BIJUkIgfr5wjE0eHmx65M7NcXv+qxHSOA3sK5Ku9AJnRac++o9YIIIAAAggggAACCCCAgK8FGAD00AN5eXlG7vr16z2UMme5yrquNedyhMDICcSEh8iG1XMlPMT8a/23D3Jl30GWpI+cvHfu7HCqLQYqG7zzMJ6CAAIIIIAAAggggAACCCDgVwLmkQK/atrQG+OK5jthwoR+38xVNiYmpt/XUBCB4RJIGhslPzxltul27WrruM0qKEgZS0hNLnY8KKtrlRq1vyMJAQQQQAABBBBAAAEEEEAAgYEIMADoQUtH9NUpMzPTQylzlqus61pzLkcIjLzAsTPHynnHTDU9SO8ht+mNdGlxOE3nObCfQI7a27Fdj+qSEEAAAQQQQAABBBBAAAEEEOinAAOAHqCuuuoqYz+/zZs3qz+4+46mqsvcd999xl6BV155pYc7k4XAyAqc/7VEWTIt3vSQ3IpGeXgnQUFMKDY8aGp1ysGaJhvWnCojgAACCCCAAAIIIIAAAgj4SoABQA/y//Vf/yWXXnqpfPDBB7J27Vo5dOhQr6VLSkrkvPPOk927d4veB1AHAyEh4CuBIBWU5kenzpYpcRGmKrybVSFbviw2nePAfgJFVU3S3MZsTvv1HDVGAAEEEEAAAQQQQAABBHwjEOKbx1rrqX/96197rdCKFStk79698vLLL8usWbNkzZo1snTpUtF7/enIv3rgb8+ePbJ161ZpaWkx8vQ1+p4XX3xxr/clA4GRFogKU0FB1syVW57fK01ug0X/+2G+TFN7BS5KNM8QHOn6cP/hE9ArgHMrGmTepNHDd1PuhAACCCCAAAIIIIAAAggg4LcCow6r5Let62fDgoKCjMG8voprKj3o11PqmqfLORyOnopyTgkUFhZKUlKSYVFQUCCJiYm2cfk4r1JaHfb5tfkkr0ru2Zou7jWODg+WO9culImjzTMEbdMJVNQQSJkYI+NiwtFAAAEEEEAAAQQQQAABBHoVsPPf3702iowBC7AE+D9kegCvry9dtLcyPeX959Z8Q8CnAkumj5EL1J6A7qmhxSn3vpHBMlJ3FBu+1vs6OgkIYsOeo8oIIIAAAggggAACCCCAgHcFWAKsvHNycryrztMQ8LLAWhUVOE8NFn2YW9nx5ILKRvnLjiy5btWcXme2dhTmhSUFWh3tUljVKNPHRVuyflQKAQQQQAABBBBAAAEEEEDAGgIMAKp+mD59ujV6g1ogMEICOijI1SuSjeixhSqAhCvtzqmUFz4/KGuPnuo6xXebCRTXNEuCWgYcHc4/5zbrOqqLAAIIIIAAAggggAACCHhNgCXAXqPmQQj4ViAyLFg2rJ4r0eq7e3p6T4F8ml/lforXNhLQu7jmlDfYqMZUFQEEEEAAAQQQQAABBBBAwNsCDAB6W3yIzystLTUiEt96661yxhlnSEJCgrF8UwcdWb9+fb/u/sQTT3Rco6/z9KXLkvxHYFJchPx4pVry69YkHRzkj9sPSHFN58xAt2xe2kCgrtkhpbXNNqgpVUQAAQQQQAABBBBAAAEEEPCFAGvGfKE+hGdOnDhxCFdzKQIiRyfFy7qlSfIPNfPPlRpbVVCQrRny63MWiJ4pSLKfQL7a03FMdJiEBvO5jv16jxojgAACCCCAAAIIIIAAAiMrwADgyPqO6N2nTZsm8+bNk61btw76Oa+//rpMmTKl1+sTE83RY3stSIatBL61eIrkVDTIB9mdQUGKqpvkgbcPyPWrU0TvGUiyl0Cb87AR6GX2hBh7VZzaIoAAAggggAACCCCAAAIIjLgAA4AjTjy8D9BLf5cuXWp86dmAubm5MnPmzEE/JCUlRWbMmDHo67nQngJ62fdVy1VQkOpm0TPHXOmjvCp57tMiOX8JA78uEzt9L6trkQmjw2V0RKidqk1dEUAAAQQQQAABBBBAAAEERliAtWIjDDzct7/99tvlrLPOEpYCD7ds4N0vIlQHBUmRmC7RY5/5uFA+yuucGRh4MvZucU5ZgxzWkUFICCCAAAIIIIAAAggggAACCPxHgAFAfhQQCGCBCaMj5LpVKihIlxW/D2zPkqIqgoLY8UdD7+d4sIaAIHbsO+qMAAIIIIAAAggggAACCIyUAAOAIyXLfRGwicCCqXFy0XHTTbVtanPKPVvTpaHFYTrPgT0E9OBts+pDEgIIIIAAAggggAACCCCAAAJagAHAAP85uPTSS40gIGFhYZKQkCDLli2TW265RYqKigJcJrCaf8aCSXLS7ARTow/VNssftx+Q9naWk5pgbHDgVH2WV9G5t6MNqkwVEUAAAQQQQAABBBBAAAEERlCAICAjiGuHW7/99tsd1ayoqBD9tXv3brn33ntl8+bNctVVV3XkD+RFYWGhx+LFxcUe88n0roAOCnLFybNERwLOKW/oePhnBdXyL7Un4IVLkzrO8cIeApUNraK/xkaH2aPC1BIBBBBAAAEEEEAAAQQQQGDEBBgA9ECro+sGBQXJ66+/LrNnz/ZQsjMrPz9fTjnlFLWn2ijJysrqzLDYq1mzZsl5550nxx9/vCQlHRncyc7OlmeffVaeeeYZaW5ulquvvtpox5VXXjng2rvuOeALucBnAmEhQXKDCgpy83NfSm1z59Lf5z8rkhnjouS4WeN8VjcePDiB3IoGiYsMleCgLps8Du52XIUAAggggAACCCCAAAIIIGBTAQYAPXRcXl6eMQDW2trqoZQ5q62tTXJzc43rzDnWOTr33HPlkksu6VbHpUuXyoUXXigvv/yyMTio23L99dfL2WefLZMmTbJOA6jJiAkkxITLT09LkTu37BenWyTZP+/IkinxkZI0NmrEns2Nh1+gpa3dCOYyTQ3gkhBAAAEEEEAAAQQQQAABBAJXgD0AA7Dv4+Liug3+uTOcddZZcuuttxqnGhsb5dFHH3XP7tfrgoIC8fT14Ycf9us+FPK+wPzJo+Xi481BQVoc7UZQkHq3mYHerxlPHIzAwZomaWztnNE5mHtwDQIIIIAAAggggAACCCCAgL0FGAAc5v6rqakx7hgVZe8ZN3rZr17GrNOOHTuM7wP5T2Jionj6mjx58kBuR1kvC6xOnSgrUsabnlpa1yJ/eCuToCAmFesf6Imc2WWd+zpav8bUEAEEEEAAAQQQQAABBBBAYLgFGAAcZtG///3vxh2nTzfPoBrmx4z47SZMmCDjxh3Z842IwCPObbkH6MHfy06cKcnjo011+6KoRv65J990jgPrC9SpmZuldc3Wryg1RAABBBBAAAEEEEAAAQQQGBEB9gB0Y125cqXbUefLSy+9VKKjzQMhnblHXrW0tIgOolFaWmrMnFuzZk3XIrY7ds0AtF3FqfCwCBwJCjJXfqmCgtQ0tXXc86UvimVGQrSckJzQcY4X1hfIr2iUMVFhEhrM5z7W7y1qiAACCCCAAAIIIIAAAggMrwADgG6eb7/9tjF4d9gt+IF+vWfPHrdSfb/UEXZ/8Ytf9F3QwiXKysqkvLzcqOGUKVMsXFOqNpICY6PD5HoVFOTXW/aJs12tJf1PenBHthEUZMY4zwPjrvJ8971Am/Ow5Fc2qlmdMb6vDDVAAAEEEEAAAQQQQAABBBDwqgADgG7cy5cv79j3Tp/We9/pWXBf+9rXPM4A1GUiIiJE72t3wgknyLp16zyWd3ukZV8+9NBD4hoIXbFihWXrScVGXmDupFi59IQZ8siunI6HtTrb5d6t6XLnuQtldERox3leWFugtLZFxseG02fW7iZqhwACCCCAAAIIIIAAAggMuwADgG6kegagewoKOrJU7oknnpDU1FT3LNu+zs3NlaqqKjnmmGN6bcPLL78s//M//2PkR0ZGil4CTQpsgVXzJ0pOeYNsSyvtgCivb5X738yUX545X4KDjgSM6cjkhWUFclU/LpzqORK4ZStPxRBAAAEEEEAAAQQQQAABBAYlwACgB7aLL77YmAE4ZswYD6W8m7Vr1y45cOBAx0Ndy3T1CX1eD1a6p/Xr17sfih4APPXUU+X444+Xb33rW7J48WLRAT900nsYPvPMM8aXa/bfPffcI1OnTjXdg4PAFFivZgEWVjVJekldB8C+4lp5aneeXHz8jI5zvLC2QEOLU4prmo0l3NauKbVDAAEEEEAAAQQQQAABBBAYLoFRaqCnc2Ov4bor9xkxAT2g9+STT/b7/l27V89y1AOAfaWoqCi577775Morr+yr6KDyCwsLJSkpybi2oKBAEhMTB3UfX1z0cV6ltDoC89emurHVCApS1dgZFET3wTUrkmV5ynhfdAfPHISAnrG5OClOwkOCB3E1lyCAAAIIIIAAAggggICdBOz897ednK1eV2YAWr2Hhrl+ej/Dv//97/L+++/LRx99JMXFxUawD4fDIXqm41FHHSWrVq2SH/zgBx0zA4e5CtzOxgLxKorsDavnyu0vfSUOt6Agj+zKlqljIgkwYZO+1QFd8lRU4JSJsTapMdVEAAEEEEAAAQQQQAABBBAYigAzAD3offnll3LOOedIcHCw6JlzfS2FLSoqEh0wQ8+6e/XVVyUlJcXD3QM7y86fQATyDEDXT+3b6aXy4M5s16HxXUcMvnPtAtGDhCR7CMxTAV7GqH4jIYAAAggggAACCCCAgP8K2Pnvb//tFe+37EiUC+8/1xZP1DPl9J55s2fP7nPwTzdIDxDqQT99jb6WhIC/Cpwyd4J846hJpuZVNqigINsyxaEiBJPsIZBT0SDtbjM57VFraokAAggggAACCCCAAAIIIDBQAQYAPYjt2LHDCAJy9tlneyhlztIzBvUMwG3btpkzOELAzwQuWjZN5k82LyFNO1Qnf/sgz89a6r/NaWlrl6LqJv9tIC1DAAEEEEAAAQQQQAABBBAwBBgA9PCDkJGRYeQuWrTIQylz1oIFC4wT6enp5gyOEPAzgZCgIPnpqhRJiDEvId26r0S2p5X6WWv9tzkH1QBgU6vTfxtIyxBAAAEEEEAAAQQQQAABBIQBQA8/BPX19UZuTEyMh1LmLFfZ2tpacwZHCPihwOjIUCMoSGjwKFPrHns3RzJL6kznOLCmgF4BnF1+5N86a9aQWiGAAAIIIIAAAggggAACCAxVgAFAD4I6Kq5Ohw4d8lDKnOUqGxtrXhppLsURAv4jMDMhWq44eZapQTpC8KY3M6SqsdV0ngNrCtQ2OaSsrsWalaNWCCCAAAIIIIAAAggggAACQxZgANAD4Zw5c4zc1157zUMpc5aO/qtTcnKyOYMjBPxY4OQ54+XMhZNNLaxubJP73siQNoKCmFysepBf2UAAF6t2DvVCAAEEEEAAAQQQQAABBIYowACgB8BvfOMbRkCPhx56SPbv3++h5JGsr776Sh5++GEjcMjpp5/eZ3kKIOBPAt89dposmDLa1KTM0np5/N1c4/fIlMGB5QRaHYclv7LRcvWiQggggAACCCCAAAIIIIAAAkMXYADQg+E111wj0dHR0tzcLCtXrpSXX36519IvvviinHbaadLU1CSRkZHyox/9qNeyZCDgjwLBQaPk2lVzZHxMuKl529NL5c39BAUxoVj0oFQtA65rbrNo7agWAggggAACCCCAAAIIIIDAYAVCBnthIFyXkJAgf/nLX+T73/++lJaWyjnnnCOzZs2Sk046SSZPPrLcsbi4WN555x3JyckxZjmNGjVK/vznP8vEiRMDgYg2ImASiI0IlQ1rUuS2F7+SFkd7R96T7+VK0phImTfZPEOwowAvLCFwWAUEySlvkIVT44yZzJaoFJVAAAEEEEAAAQQQQAABBBAYsgADgH0Qfu9735P29nbRswEbGxslKytLsrOzTVcd1n81q6RnC+rBv4suusiUzwECgSQwfVy0XLU8WX7/VmZHs53qd+S+bZly19oFMq7LDMGOQrywhEBDi1MO1TbL5LhIS9SHSiCAAAIIIIAAAggggAACCAxdgCXA/TDUMwAPHDggP//5z2XhwoXGFXrQT3/pGX+LFi2Sm2++2SjD4F8/QCni9wLHJ4+TsxdPMbWztqlNNqmgIK1uMwNNBTiwjEBBZZOawem0TH2oCAIIIIAAAggggAACCCCAwNAERqlBrCPT14Z2n4C62uFwSGVlpdHmsWPHSkgIEykH+gNQWFgoSUlJxmUFBQWSmJg40Fv4rPzHeZVqEItfm746oL39sPz29TT5vLDGVHT5nAS5ekUyS0xNKtY7SIgJkzkTY61XMWqEAAIIIIAAAggggAACAxKw89/fA2oohT0KMAPQI0/PmXrAb8KECcYXg389G3EWgSAVFOTHK+fIpNERJoydmeXy+lclpnMcWE+gvL5VqhtbrVcxaoQAAggggAACCCCAAAIIIDBgAQYAB0zGBQgg0F+BmPAQuWF1ikSEmv+p+dsHubLvoHlmYH/vSTnvCeiAIHomJwkBBBBAAAEEEEAAAQQQQMDeAua/yu3dFmqPAAIWFEgaGyU/XDHbVDM9prRZBQUpq2sxnefAWgLNbe1SVN1krUpRGwQQQAABBBBAAAEEEEAAgQELsHldP8m2b98uzz//vHzdPsm7AABAAElEQVT++edSXl4uTU1NRhCQ3i7XwUF0xGASAgiILJ05Vs47Zqr8+9OiDo66ZocKCpIuvzr7KAkPCe44zwtrCRxUA4DjY8PVLE76yFo9Q20QQAABBBBAAAEEEEAAgf4LMADYh1VpaamsW7dOduzYYZTsLWaKHvBzz9PHJAQQ6BQ4/2uJklvRIJ/kV3eczK1olId3ZsuPTp1NUJAOFWu90LM1s8saJHXKaGtVjNoggAACCCCAAAIIIIAAAgj0W4ABQA9UbW1tcsYZZ8hnn31mDO4dffTRMnXqVNmyZYsxWHHRRRcZ0YA/+eQTKS4uNs4tWbJEFixY4OGuZCEQmAJBalBcD/T9v+f3ysGa5g6Ed7MqZEZCtJy1aErHOV5YS6CmqU3K61skISbcWhWjNggggAACCCCAAAIIIIAAAv0SYA9AD0xPPPGEfPrpp0aJxx9/XPRA3913391xxZNPPikvvfSSFBUVyb///W+ZPHmy7Nu3T8466yzR5UkIIGAWiAoLkQ1r5kpkl+Wk//thvnxR2Dkz0HwVR1YQyFOzNx3OditUhToggAACCCCAAAIIIIAAAggMUIABQA9gzz77rJF7+umnyyWXXOKhpMjatWuNZcJhYWGyfv16yczM9FieTAQCVWBKfKT8WC/5dQM4rJaZ/v6tTCmp7ZwZ6JbNSwsItDoOS0EVAUEs0BVUAQEEEEAAAQQQQAABBBAYsAADgB7IdMAPvZefXurbU3Lf80/nJycny3XXXScNDQ1y//3393QJ5xBAQAksmT5GLlB7Arqnhhan3PtGhjS3Od1P89pCAnqAtr7FYaEaURUEEEAAAQQQQAABBBBAAIH+CDAA6EGpsrLSyJ05c2ZHKT3Dz5UaGxtdLzu+r1q1ynj9xhtvdJzjBQIIdBdYq6ICHztjrCmjoLJR/rIjyxRQx1SAA58K6JmaOSogSNcPP3xaKR6OAAIIIIAAAggggAACCCDQpwADgB6IXIN9ru+66OjRnZEw9d5/XVNERIRxqqe8rmU5RiCQBXRQkKtXJEvimEgTw+6cSnnh84OmcxxYR0DPACypbbFOhagJAggggAACCCCAAAIIIIBAnwIMAHogmjZtmpFbUlLSUWrixIkSGxtrHO/evbvjvOvF3r17jZd66TAJAQQ8C0SGBcuG1XMlWn13T0/vKZBP86vcT/HaQgIFVY3S6iAgiIW6hKoggAACCCCAAAIIIIAAAh4FGAD0wLNkyRIj1xUJ2FV0+fLlxhI4vc9fS0vnTJjq6mrZuHGjsW9gamqqqzjfEUDAg8CkuAj5yco56vems5BaaSp/3H5AimsIOtGpYp1XDudhya9ssE6FqAkCCCCAAAIIIIAAAggggIBHAQYAPfDo/fz0Xldbtmwxlbr66quNYz0wuGjRIrnxxhvlhz/8oSxcuFAyMjKMvIsvvth0DQcIINC7wOKkeFm39MiMW1epxlYVFGRrhjSp7yTrCZTVtUpNY5v1KkaNEEAAAQQQQAABBBBAAAEEugkwANiNpPPE2rVrRS8DLiwslKysrI6Mb37zm3LZZZcZg4OZmZmyadMmefDBB8W179+aNWvkmmuu6SjPCwQQ6FvgW4smy/GzxpkKFlU3yQNvH5B2HX2CZDmBnIoGaW+nbyzXMVQIAQQQQAABBBBAAAEEEOgiwABgFxD3w/j4eMnNzZW8vDxJTk52z5JHHnlEHn74YTnuuOMkOjpawsPDjRmAv/vd7+Sll16SoCBoTWAcINCHgN4388rls2Ta2ChTyY/yquS5T7sH3DEV4sAnAnp25kGWafvEnocigAACCCCAAAIIIIAAAgMRCBlIYcqaBS6//HLRXyQEEBgegYhQHRQkRW5+fq/oaLOu9MzHhTJ9XJR8ffpY1ym+W0SgqKpJEmLCRfcdCQEEEEAAAQQQQAABBBBAwJoCTFOzZr9QKwQCVmDC6Ai5bpU5KIjGeGB7lujBJpK1BPQK4JxyAoJYq1eoDQIIIIAAAggggAACCCBgFmAA0OzBEQIIWEBgwdQ4uei46aaaNLU55Z6t6dLgNjPQVIADnwlUq2AgFfWdEdF9VhEejAACCCCAAAIIIIAAAggg0KMAS4B7ZOl+0ul0ygsvvCBvvvmmfPnll1JZWWkUGjt2rCxYsEBOO+00OeeccyQkBNLuepxBYOACZyyYZMws23WgvOPiQ7XN8sftB+TGNXPVPpujOs7zwvcCuRWNEh8VJsH0i+87gxoggAACCCCAAAIIIIAAAl0EGK3qAtLT4Ysvvig//vGPO6L86jKH/xOVVAcueO+99+Shhx6SyZMnyx//+EfR0YNJCCAwNAH9u3XFybNERwJ2X2L6WUG1/EvtCXjh0qShPYCrh1Wg1dEuBZWNMiMheljvy80QQAABBBBAAAEEEEAAAQSGLsAS4D4M77//fjn33HONwT/XoN+MGTNk2bJlxpd+rZPOO3jwoJx//vmyefNm4xz/QQCBoQmEhQTJDSooyOgI82cVz39WJLuzK4Z2c64edgE9Q5Ml2sPOyg0RQAABBBBAAAEEEEAAgSELMADogXD37t2yYcMGY3AvNjZWNm7cKCUlJZKVlWXM+tMz//RrfU7nxcXFGWVvvPFG0deSEEBg6AI6wuxPT0uRYDUj0D39eUeWMePM/RyvfSugJ0br2ZquD0t8WxuejgACCCCAAAIIIIAAAggg4BJgANAl0cP3TZs2SXt7uzGwpwf79MBeQkJCt5L6nM7TZfQgoL5GX0tCAIHhEZg/ebRcfLw5KEiLWnKqg4LUNzuG5yHcZVgE6lR/lNYREGRYMLkJAggggAACCCCAAAIIIDBMAgwAeoB85513RO9DdtNNN0lqaqqHkkey5s+fb5TVs1927tzZZ3kKIIBA/wVWp06UU1LGmy7QA01/eCtTDbqrqWckywjkq70A9Z6AJAQQQAABBBBAAAEEEEAAAWsIMADooR+qqqqM3FNPPdVDKXOWq2x1dbU5gyMEEBiSgB6Mv/TEmTJ7QozpPl8U1cg/9+SbznHgWwGH87DoQUASAggggAACCCCAAAIIIICANQQYAPTQDzqq72DTUK4d7DO5DgF/F9BBQa5X+wHGR4aamvrSF8XyXla56RwHvhUoU7Mza5rafFsJno4AAggggAACCCCAAAIIIGAIMADo4QfhtNNOM3J37NjhoZQ56+233zZOrFy50pzBEQIIDIvA2OgwuV5FBg4OMgcFeXBHtuRWNAzLM7jJ8AgQEGR4HLkLAggggAACCCCAAAIIIDBUAQYAPQjqCMCRkZFy9913S0ZGhoeSR7J0GR0NODo62ggK0ucFFEAAgUEJpEyMlUtPmGG6ttXZLveqoCC1zcw6M8H48KCp1SkHa5p9WAMejQACCCCAAAIIIIAAAgggoAUYAPTwczB37lx55plnjBLLli2TzZs3S2VlZbcr9F6B999/v5xwwglG3tNPPy36WhICCIycwKr5E2XVvAmmB5TXt8r9b2aKk6AgJhdfHhSqvQCb25y+rALPRgABBBBAAAEEEEAAAQQCXmCUilhL+Mxefgxcy3iLiookMzPTiAisAxHMnDlTJkyYYByXlJRITk6OuBhnz54tU6dO7eWOYlyzbdu2XvMDJaOwsFCSkpKM5hYUFEhiYqJtmv5xXqWKcMqvjRU6zKFm/d2xZb+kl9SZqnPGgkly8fEzTOc48J3AmOhQmTdptO8qwJMRQAABBBBAAAEEEAhgATv//R3A3TbsTQ8Z9jv60Q31fn56wM+V9CCf/srKyjK+XOfdvx84cED0l2tA0JWn76PPud/Plcd3BBAYnEBIcJD89LQ58svnvpSqxs6lv6/uPSQzxkXL8pTxg7sxVw2rQFVDm1Q2tIrev5GEAAIIIIAAAggggAACCCDgfQEGAD2YL1++nAE7Dz5kIWAFgfioMLlh9Vy5/aWvxOG29PeRXdkydUykJI+PsUI1A74OOiBInIre3DV4S8DDAIAAAggggAACCCCAAAIIeEGAAUAPyK6Ivh6KkIUAAhYQmD0hRi4/aaY8uDO7ozZtzsOy6Y0MuXPtAtGDhCTfCrQ62qWwqlGmq5mZJAQQQAABBBBAAAEEEEAAAe8KEATEu948DQEERkjglLkT5BtHTTLdXS87vX9bpui9Akm+FyhWEYEbWx2+rwg1QAABBBBAAAEEEEAAAQQCTIABwADrcJqLgD8LXLRsmsyfHGtqYtqhOvnbB3mmcxz4RkCHnMoua/DNw3kqAggggAACCCCAAAIIIBDAAgwABnDn03QE/E0gJEgFBVmVIgkx5iW/W/eVyPa0Un9rri3bU9fskNLaZlvWnUojgAACCCCAAAIIIIAAAnYVYA/AfvZce3u77Nu3T7Kzs6Wurk6cTmefV1588cV9lqEAAggMr8BoFWhCBwW57cW9ovcBdKXH3s2RRBUUZM5E8wxBVz7fvSeQX9koY1RE4FAVxZmEAAIIIIAAAggggAACCCAw8gIMAPZh3NjYKHfccYc88sgjUlFR0UfpzuxRo0YJA4CdHrxCwJsCMxOi5crlyfKn7Qc6HqsjBG96M0PuOnehjCEoSIeLL17ogdm8ikbRwVtICCCAAAIIIIAAAggggAACIy/A9AsPxvX19bJixQrZuHGjlJeXy2G1gdVAvjzcmiwEEBhhgZNmJ8g3F042PaW6sU3uU5GB2wgKYnLxxUFZXYvUNrf54tE8EwEEEEAAAQQQQAABBBAIOAFmAHrocj3z7+OPPzZKLFu2TK688kpZvHixxMfHS5Daa4yEAALWFvjOsdPUTLMG2XuwtqOimaX18vi7uXLFyTNFz9Ql+U4gRwUEWZQYRz/4rgt4MgIIIIAAAggggAACCASIAAOAHjr6mWeeMf4wPfPMM+WFF15g0M+DFVkIWFEgOGiUXLtqjtz83F4pq2/pqOL29FLRy4RXp07sOMcL7ws0tjqluKZZpsRHev/hPBEBBBBAAAEEEEAAAQQQCCABprF56OyioiIj99prr2Xwz4MTWQhYWSA2IlQ2rEmR8BDzP3dPvpcracWdMwOt3AZ/rlthVZM0t/UdVMmfDWgbAggggAACCCCAAAIIIDDSAua/iEf6aTa7/4QJE4waJyQk2KzmVBcBBNwFpo+LlqtUUBD35FR7et63LVMq3GYGuufz2jsCThWcRQcEISGAAAIIIIAAAggggAACCIycAAOAHmyPPfZYIzc9Pd1DKbIQQMAOAscnj5OzF08xVbW2qU02qaAgrY5203kOvCtQ2dAqVeqLhAACCCCAAAIIIIAAAgggMDICDAB6cL3++uuN3D/+8Y9G9F8PRclCAAEbCFz49SRZrIJOuKfs8gZ5dFc2v+PuKD54naOCtejZgCQEEEAAAQQQQAABBBBAAIHhF2AA0IPpCSecIBs3bpT33ntP1q1bJ9XV1R5Kk4UAAlYXCFJBQX68co5MGh1hqurOzHJ5/asS0zkOvCvQ0tYuRWo/QBICCCCAAAIIIIAAAggggMDwCxAFuA/Tn/3sZ5KcnCxXXHGFJCUlyerVqyUlJUWioqL6uFLk1ltv7bMMBRBAwLsCMeEhcsPqFLn1xb0q+ETn0t+/fZAr08ZGSuoU8wxB79YusJ92sKZJEmLDJCqM/zUF9k8CrUcAAQQQQAABBBBAAIHhFuCvrD5ES0tL5bnnnpOamhppb2+XF154oY8rOrMZAOy04BUCVhJIGhslP1wxWza9mdFRLb36dLMKCnLn2oUyPja84zwvvCeg4rJIjlqSfRSDsN5D50kIIIAAAggggAACCCAQEAIsAfbQzRUVFbJ8+XJ56qmnxOl0GnuEHVZ/ofb3y8OtyUIAAR8LLJ05Vs5bMtVUi7pmhwoKki4tDqfpPAfeE6htckhpXbP3HsiTEEAAAQQQQAABBBBAAIEAEGAA0EMn33XXXZKRkWEM+F1wwQXy1ltviR4U1IOBejZgX18ebk0WAghYQOD8JYmyZNoYU01yKxrl4Z0EBTGhePkgX/VBm7NzebaXH8/jEEAAAQQQQAABBBBAAAG/E2AA0EOXvvjiizJq1Cj5/ve/L08//bSccsopMmbMGOOch8vIQgABmwgEqd/vH52aLFPizUFB3s2qkC1fFtukFf5XzTbnYcmvbPS/htEiBBBAAAEEEEAAAQQQQMBHAgwAeoAvKioyci+77DIPpchCAAE7C+iAExtWz5XI0GBTM/73w3z5opDI3yYULx6U1rZIXXObF5/IoxBAAAEEEEAAAQQQQAAB/xVgANBD3yYkJBi5sbGxHkqRhQACdheYEh8pPz51toxya4gOSPH7tzKlpJb96NxYvPpSBwTRe66SEEAAAQQQQAABBBBAAAEEhibAAKAHv5NPPtnI3bt3r4dSZCGAgD8ILJk+Ri74WqKpKQ0tTrn3jQxpbiMoiAnGSwfa/xADsF7S5jEIIIAAAggggAACCCDgzwIMAHro3Q0bNkhoaKjcc8890tzMLCAPVGQh4BcCa4+ZKsfOGGtqS4Hai+4vO7KYiWZS8d5BQWUTUZm9x82TEEAAAQQQQAABBBBAwE8FGAD00LFLliyRRx55xIgEvGbNGuO7h+JkIYCAzQV0UJCrVyRL4phIU0t251TKC58fNJ3jwDsCzvbDkqeiApMQQAABBBBAAAEEEEAAAQQGLxAy+Ev9/0pX8I/U1FTZtWuX6O+LFi2SlJQUiYqK8gigowc/+uijHsuQiQAC1hOIDAs2goLc8vyX0tDaufT36T0FMn1slBwzbYz1Ku3nNaqob5Xq2FaJjwrz85bSPAQQQAABBBBAAAEEEEBgZARGqQ3W2WG9F9ugoCDRA3mupKncj13nu353lXM6OwcPupYJ9OPCwkJJSkoyGAoKCiQx0bz3mpV9Ps6rlFYHvzZW7qPhqNvnBdWy8fU0tfS3825RanDwjrULZHKceYZgZwlejZRARGiQLE6Ml6Cgzn+TR+pZ3BcBBBBAAAEEEEAAAX8SsPPf3/7UD75uCzMAPfTAtGnT+jXg5+EWZCGAgE0FFifFy7ql0+QfH+Z3tKBRzQi8d2uG/PqcBaJnCpK8J9Dc1i5F1U2SpGZhkhBAAAEEEEAAAQQQQAABBAYmwACgB6/c3FwPuWQhgIC/C3xr0WTJLW+Q97MrOpqqB6EeePuAXL86RfSegSTvCRxU9gkx4Qy+eo+cJyGAAAIIIIAAAgj0IOBwtktIMCEVeqDhlIUF+Im1cOdQNQQQ8K2AXvJ/5fJZxt5/7jX5KK9K/vVRofspXntBQMUDkezyei88iUcggAACCCCAAAIIINCzgA5Sl1nKe9KedThrZQEGAK3cO9QNAQR8LhARqoKCrEmRmHDzhOnnPyuSl78gMrC3O6i2ySFldS3efizPQwABBBBAAAEEEEBA7Q9+WA6owb/GVgcaCNhOgAFA23UZFUYAAW8LjI+NkOtWzVFLfs1Pfmp3vryx75D5JEcjLpBf2SB62QUJAQQQQAABBBBAAAFvCuRVNEplQ6s3H8mzEBg2AQYAh42SGyGAgD8LLJgaJ5edOLNbEx97N1d2ZJR1O8+JkRPQUbjzKxtH7gHcGQEEEEAAAQQQQACBLgIltc1SXNPc5SyHCNhHwLymzT71HtaaBgcfieap9/tyODqn8rrOD+ZhXe81mHtwDQIIWEtg1fyJoqPR/n13nqliD+7MkvCQIFk2a5zpPAcjJ1CqlgFPGB3RbWn2yD2ROyOAAAIIIIAAAggEqkB1Y6vkqOCAJATsLMAMQNV7eh2/68u9M13nBvvd/V68RgAB/xD4pooMfMHXEk2NUf+EyB/fOiCf5FeZznMwcgLaPKeswfi3e+Sewp0RQAABBBBAAAEEAl1A7/eng37o958kBOwswAxA1Xu33XZbj33Y2/keC3MSAQQCRuC8Y6ZKS5tTXvqiuKPNTvWOYPObGfLf35gnerkwaeQF6lscckgtxZgcFznyD+MJCCCAAAIIIIAAAgEn0Opol7RDdWr/aUb/Aq7z/bDBo9TsNn6S/bBjrd6kwsJCSUpKMqpZUFAgiYnmGVVWrv/HeZWi9yAjBbaA/qfzifdyZeu+EhOEXgr8izPmy9xJsabzHIyMQEjwKFmcGC9hyp2EAAIIIIAAAggggMBwCbS3H5Z9xbVS19y5TZjr3mEho+Rr08e6Di3/3c5/f1se10YV5C8mG3UWVUUAAesI6H0+LzlhhqxIGW+qVIv6lHDja2mSXVZvOs/ByAjoT2PzKtiPZWR0uSsCCCCAAAIIIBC4Alnq/XxPg3+BK0LL7S7AAKDde5D6I4CAzwSC1CDglSfPUsE/zJ/+Nanlwb95NU0KiFTrlb4pr2+VmsY2rzyLhyCAAAIIIIAAAgj4v4B+H6/fY5IQ8CcBBgD9qTdpCwIIeF0gKGiU/OjU2bJk2hjTs/X+dHe9sl+Ka5pM5zkYGYHs8nrRyzRICCCAAAIIIIAAAggMRaCsrkUKq3gPPxRDrrWmAAOA1uwXaoUAAjYSCAkKkutWzekW/KO6qU3u3LJf9JsI0sgKNLe1S1E1b9RGVpm7I4AAAggggAAC/i1Q29zGVj7+3cUB3ToGAAO6+2k8AggMl4AOQrFhdYrMnWgO/lHR0Cp3vrJPqhpZQjBc1r3d56AaAGxWy69JCCCAAAIIIIAAAggMVEC/j8xQEX9ZVDJQOcrbRYABQLv0FPVEAAHLC0SEBst/nz5XZiVEm+paUttizASsVTMCSSMnoN+sZZcREGTkhLkzAggggAACCCDgnwIOZ7ukqcG/NhVgjoSAvwowAOivPUu7EEDAJwJRYSHy8zPmSdKYSNPz9fLU37y6XxrU3oCkkROoUYOs5fUsuR45Ye6MAAIIIIAAAgj4l8Dhw4clo6RemlpZSeJfPUtrugowANhVhGMEEEBgiAKxEaHyyzPny+S4CNOdcisaZeNraSxTNakM/0FeRYPoT3FJCCCAAAIIIIAAAgj0JZBd3iD6Q2QSAv4uwACgv/cw7UMAAZ8IxEeFyc1qEDAhJsz0/MzSevnd6+nS6mCAygQzjAetjsNSQOS2YRTlVggggAACCCCAgH8K6FU6pWq7HhICgSDAAGAg9DJtRAABnwiMiwmXW76ZKmOiQk3P31dcK/e9mcEsNZPK8B6U1DZLPcuthxeVuyGAAAIIIIAAAn4kUKG2jclXK3RICASKAAOAHnr6r3/9q+iv2tpaD6XMWfX19cY1+joSAgggMHF0hJoJmCqxESEmjM8KquUP2w+IkzBjJpfhOlBbuUiOCgii93QhIYAAAggggAACCCDgLqA/KM4ieJw7Ca8DQIABQA+dvH79ern00kulsLDQQylzVklJiejrLrvsMnMGRwggELACU1VAEL0nYHRYsMngw5xKeXBHlrQzSGVyGa4D/cZOR2AmIYAAAggggAACCCDgEmhxOCX9UC0fxLtA+B4wAgwAjlBXM+tkhGC5LQI2FZgxLlpuOn2eRISa/9l950C5PLYrh5lqI9SvBVWN7Lc4QrbcFgEEEEAAAQQQsJuAXn2TfqhOvT9klYjd+o76Dl3A/Jfo0O8X8HdwOo+EDg8JMS/3C3gYABBAQOZMjJUb18yV0OBRJo1taaXy9935DAKaVIbnwOE8LPmVDcNzM+6CAAIIIIAAAgggYFsBPUkns7ROGlqO/M1u24ZQcQQGKcAA4CDherssPT3dyBo7dmxvRTiPAAIBLJA6JU5uWD1XgoPMg4CvfFksz3zS/+0GAphwwE0vq2uVmsa2AV/HBQgggAACCCCAAAL+I5CnAn5UNfCe0H96lJYMVIBpam5iO3fudDvqfLlnzx4pLy/vPNHDq5aWFsnKypJ77rlHRo0aJUcffXQPpTiFAAIIiBydFC/XrZwjm7dlqP3/OkX+/UmRhIcEy9mLp3Se5NWwCORUNMiiiDgJ6jLwOiw35yYIIIAAAggggAAClhY4VNMsxeqLhEAgCzAA6Nb7p5xyijF453bKWJI3kIAeelqxHgC86qqr3G/DawQQQMAksHTmWLnmlNnygIoE7DYGKP/4MF8iQoJkzVGTTOU5GJpAU6tTDtY0SeKYqKHdiKsRQAABBBBAAAEEbCVQ3dgquerDYBICgS7AEuAuPwF6AM/15cpyHffne2JiovzpT3+StWvXui7nOwIIINCjwEmzE+Tyk2d2y3v8vVx5O72023lODE2gqKpJmtvY82VoilyNAAIIIIAAAgjYR6Cx1SEZJfXqb3z71JmaIjBSAswAdJPdvn17x5Ee7Fu5cqUxm+/RRx+VmTO7/5HuKqxn/EVERMjkyZMlKSnJdZrvCCCAQJ8Cq+ZNlJa2dvnbB3mmsg+9k62WAwfJ8ckJpvMcDF5AL7fOKW+Q+ZNHD/4mXIkAAggggAACCCBgC4FWR7ukqYi/OvIvCQEERBgAdPspWLFihdtR58tjjz1WUlNTO0/wCgEEEBhGgTMXTpYW9Qbl6Y8KOu6qP6X80/YsCVN7An5t+piO87wYmkC1CgZSUd8i42LCh3YjrkYAAQQQQAABBBCwrEC7GvTLKKkzPmi3bCWpGAJeFmAJsAfwnJwcyc7OlpSUFA+lyEIAAQSGLrD26Cndgn841Sjg5jcz5MuimqE/gDt0COSqCHB8EtzBwQsEEEAAAQQQQMDvBA6U1Utds8Pv2kWDEBiKAAOAHvT04N/06dMlJGTgEyV/+MMfergzWQgggIBZQG8lsG5pknyjS/APh/r08t6t6Wr5Qq35Ao4GLaCXgxRUNg76ei5EAAEEEEAAAQQQsK5Avvqwt6K+1boVpGYI+EiAAUAP8DqQx8cff+yhRM9ZV155pTz44IM9Z3IWAQQQ6EVADwJefPx0OSVlvKmEXh7829fSJUt9kkkaHoFDtc3S0MKnwsOjyV0QQAABBBBAAAFrCJTWNUtRdZM1KkMtELCYAAOAHjqkrq5OzjzzTElPT/dQypz1gx/8QB555BHzSY4QQACBfgoEqUHAK06epYJ/jDNd0aSi1979aprkM3PN5DLYA73Hog4IQkIAAQQQQAABBBDwD4GapjbJKeP9nX/0Jq0YCQEGAD2ozp49W8rKymT16tVSUNC5OX9vl6xfv14ef/xxI3vdunW9FeM8Aggg4FEgKGiU/PCUZPl6l+Af9WrG2p2v7JdiPtX06NffTL0vTImaCUhCAAEEEEAAAQQQsLdAs/qwPFMF/VC755AQQKAXAQYAe4HRp9944w2ZOnWqFBYWGoOApaWlPZY+rKaSfP/735e//vWvol9fdNFF8re//a3HspxEAAEE+iMQEhQk166aI4umxpmK16pPNu9Qg4BlankDaegCekZlm7N96DfiDggggAACCCCAAAI+EdDv5fYX16r3dIz++aQDeKhtBBgA9NBVOgDI1q1bZdy4cZKZmSmnn3661NaaN+Jvb2+X733ve/LUU08Zd7rkkkvkySeflCD1xzsJAQQQGIpAaHCQ3LAmReZNijXdprKhVe7Ysl/0d9LQBBzqjWKe2iiahAACCCCAAAIIIGA/gXY15S9DzfxrbuMDXfv1HjX2tgCjVH2Iz58/X1555RWJjo6Wzz//XM466yxpbj4y88bpdMp3vvMd+ec//2nc5bLLLpPHHntM9Eb+JAQQQGA4BMJDguXGb8yV5PHRptuV1rWo5cD7RO91QhqaQJmyxHFohlyNAAIIIIAAAgj4QiBb7elc20RgN1/Y80z7CTAA2I8+W7p0qTz//PMSFhYm7777rpx//vnS1NQk3/72t+Vf//qXcYcrrrjCCP7B4F8/QCmCAAIDEogKC5Gfnz5fpo2NMl13sLpZfqOWA+u9AUlDE8hVbx71Fg4kBBBAAAEEEEAAAXsIFFY1qm1xWuxRWWqJgAUEGADsZyesXLlS/vGPfxhLe1977TWZOXOmMSioL7/66qvlwQcf7OedKIYAAggMXCAmIkR+ccY8mRIXYbo4T+1ht/G1NGlqdZrOczAwgUbld7CGfRUHpkZpBBBAAAEEEEDANwIV9S1SUNnkm4fzVARsKsAA4AA6bu3atfLwww8bV+iAIHq2yI9+9CN54IEHBnAXiiKAAAKDE4iPCpNfnjlfJsSGm25woLRefvt6mrQ4GAQ0wQzwoKiqSe0fg+EA2SiOAAIIIIAAAgh4VaCuuU30+18SAggMTCBkYMX9s3R+fn6/G6ZnAl577bVy//33ywUXXCA33nij9Hb9tGnT+n1fCiKAAAL9ERgXEy43q0HA21/eZwoCknaoTja9kSE/WzNXdPAQ0sAFnGoT6dyKBhV0ZfTAL+YKBBBAAAEEEEAAgREX0B/W6qAf6m0bCQEEBigwSs1iC/hfneDg4AGy9V1c7wXocLAvV29ShYWFkpSUZGQXFBRIYmJib0Utd/7jvEppdQT8r43l+iXQKlRU3ST/89JXUtts/nfm69PHyHWnzZEQIpEP+kdiroq6PDY6bNDXcyECCCCAAAIIIIDA8As4nO3y1cFa0Vu3+DqFhYySr00f6+tq9Pv5dv77u9+NpGCfAkwTUUR6DHQkvvrUpwACCCAwSIGp8ZHGcuDocPMHGB/lVclf3s6Sdj4WHaSsSI4KCKJnA5IQQAABBBBAAAEErCGg/17PVMt+rTD4Zw0RaoHAwAVYAqzMHn/88YHLcQUCCCDgY4Hp46KN6MB3qUjATW57172bVSFhIcFyxckzhcjkA++kVke76Khy2peEAAIIIIAAAggg4HuB3IpGqW5s831FqAECNhZgAFB13iWXXGLjLqTqCCAQyAKzJ8TIjd+YK3e/miatalmEK21PL5Xw0CC5eNl0BgFdKAP4XqwiAo9XwVaiwvjf5ADYKIoAAggggAACCAy7QHFNkxxS781ICCAwNAGWAA/Nj6sRQAABnwvMnzxaNqxJUfv+jTLV5bW9h+TpjwpN5zjon4DeHTe7rKF/hSmFAAIIIIAAAgggMCICVQ2tkqdm/5EQQGDoAgwADt2QOyCAAAI+F1iUGC/XrZojXcYA5fnPiowvn1fQhhWoUwFWSmv5tNmGXUeVEUAAAQQQQMAPBBpaHMa+f/qDWRICCAxdgAHAoRtyBwQQQMASAl+fMVZ+eMpsMc8DFPm/PQXy2t5iS9TRbpXIr2yUNrel1XarP/VFAAEEEEAAAQTsKKD3ZE47VEdgNjt2HnW2rACbG/WjaxwOh2zZskXeeecdyc7Olro69Q+R03Pocb3x/rZt2/pxd4oggAACwydw4uwEYy/Ah3Zmm2765Pt5Eq4Cg5w6b4LpPAeeBdqch0UPAiaPj/FckFwEEEAAAQQQQACBYRFwth+WdDX4pwcBSQggMHwCDAD2Ybljxw5Zv3695Ofnd5TUIch7S3rgT+cTebM3Ic4jgMBIC5w6d4K0tLXLk+/nmh718DvZKjpwkOhBQlL/BUprW4yAIKMjQvt/ESURQAABBBBAAAEEBiVwoLRe6tXyXxICCAyvAAOAHjw/++wzOf3006W1tdUY1IuIiJA5c+ZIfHy8BAX5ZvV0aWmpfPjhh8bXnj17RH9VVFQYrdDRjJ944gkPLeqe9eqrr8pDDz1k3KesrEzGjx8vS5culSuvvFLOOOOM7hdwBgEEbCFw+oJJ0uJwyj/V8l9X0h9dPPD2ATUTMEj0cmFS/wVyVECQRYlxfLjTfzJKIoAAAggggAACAxbIVwE/KlXgDxICCAy/AAOAHkx/9atfSUtLi4SHh8umTZvk0ksvFT0I6Ms0ceLEYXl8e3u7Mcj36KOPmu5XVFQk+uv555+XH/zgB/Lggw/6bLDTVDEOEEBgwALnHD1VmtVMQB0IxJXUigq5f1um/GzNXFmcFO86zfc+BBpbnVJc0yxT4iP7KEk2AggggAACCCCAwGAEdPC1ouqmwVzKNQgg0A8B30xj60fFrFBk165dxmyPm2++Wa655hqfD/51NZk2bZqsWbOm6+l+Hes2uQb/jjnmGPnHP/5hzCrU3/WxTo888ojccsst/bofhRBAwJoC3/56oujZgO7JoUYBN72RIfuLa91P87oPgcKqJmNWZR/FyEYAAQQQQAABBBAYoEBNY5tklzcM8CqKI4DAQAQYAPSg1dzcbOTqZcBWSbfeequ89NJLcujQIcnLyzNm6A20bhkZGXLPPfcYl33961+Xd999V9atW2cs/dXf9cCnPq/T7373Ozlw4IDxmv8ggID9BPR+pBcvmy56X0D31Koi2/729TTRe6yQ+iegN6TOLW/sX2FKIYAAAggggAACCPRLoEmttMgorVPbbvWrOIUQQGCQAgwAeoCbMWOGkdvW1uahlHezbr/9djnrrLNkKEuBN2/eLDqysU5/+MMfJDLSvKQtKirKOK/zdbn77rtPvyQhgIBNBfQg4A9OmiknJo8ztUAvD7771f2SV8GnrSYYDwd6T5oq9qXxIEQWAggggAACCCDQf4E29aF02qFacTgZ/eu/GiURGJwAA4Ae3NauXWvk7ty500Mpe2XpCMUvvPCCUel58+bJsmXLemyAPj937lwjT5f3FPm4xxtwEgEELCUQFDRKrjlltiydMcZUrwb1ietdr+xnvxWTiueDHDVg2q43UyQhgAACCCCAAAIIDFpAv59KP1Rn7Fk96JtwIQII9FuAAUAPVNddd51MnjzZWC6bm5vroaR9snJycuTgwYNGhVesWOGx4q58HRTEX9rvscFkIuDnAsFqEPAnK+fIYhXN1j3VNjvkzi37pERtvEzqW6BFzZxkg+q+nSiBAAIIIIAAAgh4Esgur5c69T6UhAAC3hFgANCD8/jx4+WVV14xlsged9xx8vDDD0tNTY2HK6yftW/fvo5K6hmAnpJ7/v79+z0VDai80GB+bQKqw/2ssfrn9/rVKTJ/cqypZVVq4+U7t+yXivoW03kOehY4qCLU6f1qSAgggAACCCCAAAIDFyiobJSyutaBX8gVCCAwaIGQQV8ZIBcuWrRI9BJgPQB49dVXG9GAExISRO+T5ynpPbeysrI8FfFJXmFhYcdzExMTO1739CIpKanjdEFBQcfr/rxwf05P5YuLi3s6bYtzqZNHS6YKnFCtBkxICNhRIDwkWG5cM0/uUvv/uQcBKVODf3eq5cC3npUq8VFhdmya1+qsVwDnqEh1qVNGe+2ZPAgBBBBAAAEEEPAHgXL1nrOwqskfmkIbELCVAAOAfXTXs88+K5dffrnU1emoRIeNr9LS0j6uEtEDgFZMuh2uFBMT43rZ4/fo6OiO8/X1A4sU6j542HETP3kRomZQzZsUKwWVTSwD9JM+DcRmRIYFy02nz5M7Xt4neeoTWFcqrmlWA4Npcus3UyUmgv9FuFx6+l7T1GbMmBwXE95TNucQQAABBBBAAAEEugjUNbdJlppMQUIAAe8L8NedB/P3339f1q1bJ07nkWVe06dPFz0jMD4+XoKC7LkMtLm5c4+vsDDPM3zCwzv/qG1q4hMa9x8VPcA7bVyURIcHS1ZZgzgJCODOw2ubCMSEh8gvzpwv//PyV3KwuvPfBr0k4+7X9ssvVV5UGP+b8NSduRWNxmxJvb8iCQEEEEAAAQQQQKB3geY2p2SU1Al/OvVuRA4CIynAX3YedO+44w5j8C8uLk6eeuopOfPMMz2UtkdWRERER0VbWz3vudDS0rkXWGRkZMd1/XnR15JhvQT42GOP7c+tLF1Gz/zRM6mIXmXpbqJyHgTiIkPl5jNT5faXvpLSus7feT2w/bvX041ZghGhwR7uENhZrY52tYSlUaaP65wxHdgitB4BBBBAAAEEEOgu4HC2G38ztTrUPiokBBDwiQADgB7YP/roI2Mp7+233+4Xg3+6qbGxnRv/97Wst6GhoUOnr+XCHQX/86Kv/QW7lrfzsZ4htXBqnBwoq5eqBvYFtHNfBmrdx0aHyS3fnC+/emmfVDZ0fjCQdqhONr2RITd+Y64Q/Kb3nw69bHp8bDizJXsnIgcBBBBAAAEEAlhAb6WVUVIvjQRQC+CfAppuBQF7rmP1klxj45F9sU466SQvPXHkH+M+MNdXoA73WXz+vKffcKgf2RdwtCSOGdhMyeF4NvdAYDgExsdGyC1qya+eEeieviyqkfu3ZYqjvd39NK/dBNR7WiMgiNspXiKAAAIIIIAAAgj8R0AHTtN7J5MQQMC3AgwAevCfOXOmkesaCPRQ1DZZqampHXVNS0vreN3TC/f8+fPn91SEc10EksZGyVwVICQkmP3AutBwaAOByfGRxr5/em9A9/RxXpU88HaWtLNhizuL6XVtk0PK3JZQmzI5QAABBBBAAAEEAlTgYHWTlNR2bjMToAw0GwFLCDAA6KEbzjvvPCPq7+uvv+6hlL2y9KDmlClTjErv2LHDY+V37txp5E+dOlVmzJjhsSyZnQJ6OeWCKXHG3oCdZ3mFgD0EpqlB7J+fMU8iu+z7935WhTz8TrbatJl9W3rryfzKBtH725AQQAABBBBAAAEExNhaJl8FlyMhgIA1BBgA9NAPGzZskDlz5sjmzZtF7wfoD0lHrz3nnHOMpugZfh988EGPzdLnXTMAdXl9Han/AjowyIIpo0UPBpIQsJtA8vgY+e/T50p4iPl/EW9nlMlf388zPhixW5u8UV+9qXVBFRHTvWHNMxBAAAEEEEDA2gINLQ45UFqv3jdau57UDoFAEjD/dRdILe9HW3XAjG3btsmCBQtk+fLlcvPNN8sXX3whzc3N/bjaukV++tOfSnDwkaieP/nJT6SpyfwHqz7W53UKCQkRXZ40cAG9L6BeDsy+gAO34wrfC8ybNFo2rJkrIUHmwf/Xvzok/9xTwCBgL11UUtss9eoNLwkBBBBAAAEEEAhUgRaHU3QwOSfbxwTqjwDttqiAeaMni1bSV9VyDZLp5+vIRXfffbfx1Z/66BlzDsfw/xG4a9cuOXDgQEcVysvLO17r80888UTHsX6xfv1607E+SElJkRtvvNFoi57ZeOKJJ8pNN90kycnJkpWVJRs3bpRPP/3UuE6X07MgSYMX0PsC6j3VdJRgh5OPwAYvyZXeFtDRrX96WorcpyIBO90+vn3x84MSoZYIn3vMVG9XyfLP00y5aqPrBcqOhAACCCCAAAIIBJqAHvTLOFQvrQ7/3hZFjw80tzmN98SB1se0174Co9QPLiMSvfRfUNDgJ0jqAUCn09nLnQd/Wg/oPfnkk/2+QW/d264iel5xxRXy2GOP9Xqvyy+/XB566CEZikNvN9cRiF2RhXW0YffoxL1dY/fz+n8Q6eqTsMbW4f+5sLsN9be2wPtZ5fKH7Qe6LeH4/rLpcubCydauvI9qlzw+WiaMjvDR03ksAggggAACCCDgfQH9t2dGSb2x95/3n+69J+o9nx9/L0fa1OSOx9cvVQEgBz9u4K1aB+Lf396ytdNzmAHoobduu+02D7n2ztKDeo8++qicf/75xiDfnj17RM8mTEhIkKVLl8pVV10lZ5xxhr0babHa6xlTelZQlpoJWFHfarHaUR0Eehc4PjlBWtUbnb/syDYV+tsHecY+gavmTzSd50BEb3g9Ru0BGmqDN4T0FwIIIIAAAgggMBwC+v1PZYN//51T29xmrI7RS5x1umPLfvnV2UcNBx/3QGDEBZgBOOLEPKAngUD/BKKoukkK1P8gmX/b008H56wqsFXt//f4e7mm6ukdAq85JVlOnjPedJ4DUTMAw0UHVCEhgAACCCCAAAL+LqD3Qc4ua/DrZuq/3+7Zmi6ldS2mdt6xdoFcpFbGWDkF+t/fVu4bb9bN+nNVvanBsxDwksDU+EiZpwKEhASbAyx46fE8BoFBCaw5apJ859hppmv1HhJ/2ZElH+ZUms5zIFJa2yJ16lNiEgIIIIAAAggg4M8CNY1tkqP2QPbn9FlBldz24lfdBv8i1SqvhJgwf246bfMjAQYA/agzaYq9BOKjwkQHWYgKOxKR2V61p7aBKnD24ily3hJz8A8d4O33b2WKfmNEMgvoN8O97cVqLskRAggggAACCCBgP4Emtb95Rmmd365s0u/jXvmyWH77ero0qT3d3dPkuAj519XHy+kL2BPb3YXX1hVgANC6fUPNAkDAtS8gnxoFQGf7URMvWJLYLfiHjvi2SUUL3newxo9aOvSmNLQ4pUTNBCQhgAACCCCAAAL+JtCm9ojef6hWHCoYhj8mHezj4XdyRO973XXrpjkTYuSFH51o7PHuj22nTf4pwACgf/YrrbKRQHDQKJkzMVamj4sSFTyahIDlBXSU84uOmyanzZ9gqquOhKY/Hc0sObIpsikzgA8Kqhql1dEewAI0HQEEEEAAAQT8TaBdffibrgJhtLT553scHezjrlf3y/b00m5dd3zyOLn9nFS133NEtzxOIGBlAQYArdw71C2gBKaofQHnTxqtooYyChhQHW/TxupBwEtPnCknz04wtaBFDXRtfC1Nciv8ex8YU6P7ONCfiudX4tEHE9kIIIAAAgggYCOBrLJ6tdexw0Y17n9VC9WHt//v+b2yv7j7h9r/9bVE+cmpsyU8hG2c+i9KSasIMABolZ6gHggogbioUGMaeXQ4/0PhB8L6AkFqEPCqFcly7Myxpso2qL1g7nplvxRVNZnOB/JBWV2r1DQRECSQfwZoOwIIIIAAAv4ioKPhlte3+ktzTO34rKBabn2he7CPsOAg+elpc9Re2Ilq1RYTNkxoHNhGgAFA23QVFQ0UAWNfwClxMj6WaFKB0ud2bqdewq4/BT06Kd7UDP2J8B2v7FP73zWbzgfyQS4BQQK5+2k7AggggAACfiFQVtcihX74Ia8O9vHqXh3sI61bsI+x0WHyq7OPkuNmjvOLPqQRgSvAAGDg9j0tt7BAkBpUmT0hVmYksC+ghbuJqv1HIER9Inr9aSmSOnm0yaS6sU3u2LJPKuoJgqFhGtXMyOIaBkRNPyQcIIAAAggggIBtBPS+eNlq6a+/JUd7uzy6K0f++n73YB/J46PljrULZGZCtL81m/YEoAADgAHY6TTZPgKT49S+gGpQhX0B7dNngVrTsJAgufEbc0VHRHNPennIHVv2S3Wjfy4TcW9rf17rT8xbHM7+FKUMAggggAACCCBgGYHmNqdkqKAfKvaHX6U6Naj5m1fSZFtaD8E+Zo2TW886SsZEsTLLrzo9gBvDAGAAdz5Nt4dAXOSRfQFjwkPsUWFqGbACevn6TafPkxkqorV7OqSWAes9AfUbrEBPTvWuOa+iMdAZaD8CCCCAAAII2EjA4WyXNDX416YCm/lT0vtV/78X9sq+4tpuzbpAB/tYOVv0h9wkBPxFgJ9mf+lJ2uHXAnpg5agpo9W+gOF+3U4aZ3+BaDVQ/Ysz58tUFdXaPRWoN1i/eTVNLYP1z2hx7m3t63WFmhXJjMi+lMhHAAEEEEAAASsI6L3xMkrqpUltZeJP6XMV7EMP/pXUmreq0cE+rls1R84n2Ic/dTdt+Y8AA4D8KCBgE4Ej+wLGGPtPEHjKJp0WoNUcHREqN39zvkwcbR6wzlFBMH77WrroJSSBnrRFu7+toQn0TqX9CCCAAAII+KFAtnrPUtPkP6s49IDma3sPycYegn2MiQqV276VKsvU0l8SAv4owACgP/YqbfJrgUlxEca+gGEhhJ/36462eeP0Xik3n5kq41TUNPeUXlIn976RIa2OdvfTAfe6ua1dDtY0BVy7aTACCCCAAAII2EegqLpJSrvMkLNP7bvX1BXs48n3c0WNA5rSLBXk4461C2XWePN+1qZCHCBgcwEGAG3egVQ/MAVc+wLGRrAvYGD+BNij1XrJup4JGK/2sXRPe4tq5P5tGaLfhAVy0vvOMBsykH8CaDsCCCCAAALWFahsaJV8P9q3uL7ZIXer7Wh6CvaxbNZYuVXN/Bvb5YNr6/YONUNgcAIMAA7OjasQ8LlAeEiwpKoIwRO6LLP0ecWoAAJuAjqS9S/VnoBdg9h8kl8tf9p+IKCXweoVwLkVDW5avEQAAQQQQAABBHwvUN/ikAOl9b6vyDDVQM9k1Pv9fXWwe7APvdfftSvniP7bioSAvwswAOjvPUz7/FpA7wuYrKapzxofLewL6NddbevGJY2Nkl+cMU8iVTAb9/RBdqU89E62tHddg+FeyM9fVzW0if6EnYQAAggggAACCFhBoMXhlPRDteL0k72KvyisllvV4N+h2mYTb2jwKGPgT0f7HcUfUiYbDvxXgAFA/+1bWhZAAhNHR0iqihLMvoAB1Ok2a6reT+XnahAwPMT8v50dGWXyxHt6H5YuG7HYrH1Dqa6eBUhAkKEIci0CCCCAAAIIDIeAHvRLP1Sn9mr2j/dlW79SwT5eS5PGLhGM441gH0fJ8ckE+xiOnxvuYR8B819i9qk3NUUAgS4COvLqwqnxwr6AXWA4tIxAysRY+dmauaI/cXVPb+wrkf/9MD9gBwFbVEAQvTSFhAACCCCAAAII+EpAfxibWVonDS1OX1Vh2J7rCvbxuPqQuetExpkq2MedKtiHXkVFQiDQBBgADLQep71+LRCmZlfpfQEnsi+gX/eznRu3YGqc/PS0FAnustTi5S+K5d+fFtm5aUOq+0E1ANjU5dPpId2QixFAAAEEEEAAgQEI5KmAH3prErsnHexjowr28eb+km5NOW7mWLmNYB/dXDgROAIMAAZOX9PSABHQ+wLq5ZbJal9A9ZKEgOUElkwbIz9eObvbvpXPfFwoL39x0HL19UaF9KfTOeUEBPGGNc9AAAEEEEAAAbPAoZpmKVZfdk/6A1Ud7GNvD8E+zlsyVa5dRbAPu/cx9R+aQMjQLudqBBCwqsAEtS9gZFiwZJTUq3082q1aTeoVoALLZo0zfi7/vCPLJPDU7nwjCtvq1Imm84FwUNPUJuX1LZIQEx4IzaWNCCCAAAIIIGABgerGVtH7Eds9fVlUI/e/mSENXVZU6K1nrl6RLCckJ9i9idQfgSELMANwyITcAAHrCsQa+wLGsS+gdbsooGu2PGW8XHbijG4Gj72bIztVcJBATHr5jb9E3QvE/qPNCCCAAAII2EmgsdWh9v2rV/sw26nW3eu6dd8hufvV/d0G/3Swj1vPOorBv+5knAlQAQYAA7TjaXbgCOh9AY9SEYInxUUETqNpqW0EVqdOku8dN61bff+yM0t2Z1d0O+/vJ/Rs3cKqRn9vJu1DAAEEEEAAAR8L6PccaSrir8Np39E//aHp4+qD48ff7R7sY8a4KLnjnAUyewLBPnz8o8bjLSTAEmALdQZVQWCkBEapgAs64lV0eLDklDV0i4Y1Us/lvgj0R+CsRVOkWUXCffaTwo7i+pPoP7x1QELVALbeMzCQkt6DZ3xsuESF8b/oQOp32ooAAggggIC3BNrVwFlGSZ20qPdfdk31LQ65f1um7FVLf7umY1Wwj2vUst+I0OCuWRwjENACzAAM6O6n8YEmMCE2Qo5SUVj1rEASAlYSOF9tzHzWosmmKjnVKOBmtZdLT2/sTAX97EAPfhIQxM86leYggAACCCBgIYGssnqpU9Fy7ZqKVbCPW3Wwjx4G/849Zqpcp4J9MPhn196l3iMpwCjASOpybwQsKBATHiKLEuNkdCSziyzYPQFbJT1L9bvHTpOuwT/a1LKUe7amG59SBxJObZNDyupaAqnJtBUBBBBAAAEEvCBQUNmogo61euFJI/MIPeinI/12jVqsg338+NTZ8u2vJ0mQel9JQgCB7gIMAHY34QwCfi8QGhwkqZNHy2T2BfT7vrZTA/Ug4PoTZsjyOeYobS1qj5q7X00LuFlx+ZUNal8e+y7NsdPPHnVFAAEEEEAgEARK65rVXsNNtm3qG/tK5Dc9BPuIi9TBPlLlxNnm95C2bSgVR2CEBBgAHCFYbouA1QX0YMsMtS+g3hg3iA/JrN5dAVM//YntlcuT5Ti1d4t7ampzyl2v7Bf9qXWgpFbHYSmw8Zv0QOkn2okAAggggIAdBGqa2oy9wO1Q1651dAX7eEwF/FDbF5rSdBXs4861OthHrOk8Bwgg0F2ADFI/4gAAQABJREFUAcDuJpxBIKAEdLCBBWpfwPBQ/jkIqI63cGOD1Yi0XsKxZFq8qZZ6s2c9CFhcY99Prk0N6sdBSW2z6HaTEEAAAQQQQACBwQo0qw9SM1XQj66DZ4O9nzeva1Dvg377WppsVbP/uqalM8bIr751lIyLCe+axTECCPQgwF/8PaBwCoFAE4hW+wIuVIOAevo8CQErCISoZerXrUqRBVNGm6pTrT69vnPL/oDZH08HBMktbzAZcIAAAgj8f/beAzyus8z7vm2NepdsdbnbkmU5cRKSEAdweuyElKW8BPj2pewuC+GFFxYIAQJpXBCysCzLsi/w8V6Q3Y+SDewSOz0hlcSkV9tykyWrWb2XKVK++3/kGc9IM9KMpp1z5v9c11yaOXPK8/ye0cw5/3Pf958ESIAESCBcAm4tJ9J0YlRQV9lqDTd9YfbxRhCzj2u3VcsXLtlEsw+rTSr7m1QCFACTip8HJwHzEEBdwM2V+VJVlGWeTrEnKU0AbtVfuqxO6soDUzr6x11GJODghHULWEcysXDp69FIQDYSIAESIAESIAESiITA23on8ZBG/k26piPZzBTr7uucNfvoHA48B4LZx2c1U+RDZ9PswxQTxU5YigAFQEtNFztLAvElgLqAq0tzZWN5niANk40Ekk0gKz1NbthZJ2u1XqV/O6GCGCIBR6bc/ott+/y41j7EHXw2EiABEiABEiABEgiXwNHecRmZtF4pkccOqNnHA00y7gwULpGt9M0rG+RdNPsI9yPA9UgggAAFwAAcfEECJAACK7SOxhZNvcxiXUB+IExAICfDIV/bVS+1xdkBvekYmtSTwwN6cmi9E9uAgYTxAmk7EAHZSIAESIAESIAESCAcAjhP6h11hrOqadaB2cddz7XI//3zMZlGHRS/trokR76tZh8b52SG+K3CpyRAAosQoAC4CCC+TQKpSgB1AWEOUpTDuoCp+hkw07jzs9Ll61dsloqCwBT1lv4J+Z4WhkZxa7u3nhGnjKZIxKPd55LjIwESIAESIIF4Eugfc8pxPUeyUjPMPh5ukof2nZjX7XesVrOPq7cYQQrz3uQCEiCBsAlQAAwbFVckgdQjgLqA9RX5Ul0UGHmVeiQ4YjMQKMrJkJuu3KwnfxkB3TncMyb/+PBBcXnsnyJ7TA1BUM+HjQRIgARIgARIgASCEcDNwiN6bmSldkLr/H1rt5p9tA/P6/Y126rki5fS7GMeGC4ggSUQoAC4BGjchARSiQDqAq4qzZFNrAuYStNu2rGWanr6TVr7ZW5k6v6uEfnhY4fEY/M6eaiF062RgGwkQAIkQAIkQAIkMJcAMiJg+qGZtJZp+71mH0OBZh8OrUd+/QXr5bqzV8lyvR5hIwESiJ4ABcDoGXIPJJASBCC8NFazLmBKTLbJB1muacDf0HTg/CxHQE9faxuSf33iiKB+jJ1b2+BESkQ72nkOOTYSIAESIAESiDUBnP8cPDGq5wjWOQ/6U1O3fEfNPsbm1HMugNnHexvk3RtXxhoT90cCKU2AAmBKTz8HTwKREYAZw1atC1icy7qAkZHj2rEmUFOco8YgmyU3Iy1g188fG5CfPXVU73xb5+Q3YABhvPAYhiDjYazJVUiABEiABEiABFKBAMqDIPJvwmWNmsgQK/99b4v84pn5Zh+rYPZxTaNmH+WnwtRxjCSQUAIUABOKmwcjAesTcGhdwDr9Qa6Z48hq/ZFxBFYjsHZFrnx1Z71kOgJ/yp450ie/fPaYrWvl9Y66ZHjSbbUpY39JgARIgARIgATiQACmaEMT1jgvmHB5tHZzkzz41nyzj7PU7ONWNftYmZ8ZB0rcJQmQQOBVE3mQAAmQQBgEUBewVu/O1alBSJrW52AjgWQR2Khi9A2X10l6WuDn8LEDPfLr54/bWgRsoSFIsj52PC4JkAAJkAAJmIYADDTwsELrHlGzj3v3yetBzD6uPr1K/oFmH1aYRvbRwgQoAFp48th1Ekg2gZLcDCMlOHtOGmay+8XjpxaBhqpC44Rxrhh9/5td8odX2m0LA2k+XRY54bftJHBgJEACJEACJJBEAoPjLmnpt0ZZEBi23fTHt6RjaDKAGMw+PrNjvXz4HJp9BIDhCxKIAwEKgHGAyl2SQCoRgPjXWFUgEAPZSCBZBLbVFsvnL9qoLnGBPfjDKx2y5/XOwIU2etU+OClOjzXq/dgIO4dCAiRAAiRAAkknMK7GGYd7xjTbIeldWbQDTzT1yHfuPzDf7EMN3W66skHes4lmH4tC5AokEAMCFABjAJG7IIFUJ2DUBdR0YNYFTPVPQnLHf87aEvm03kGeowHKb144Lo/sn19nJrm9jc3RUUS7Vev+sJEACZAACZAACaQOAZdnRprU8RfnAWZuM9q//9jbIj9/plmm5yiVKCf07WsbjZJCZh4D+0YCdiLgsNNgOBYSIIHkEsAPeV6mQ470jgmcStlIINEE3r1xpeCk+Bd/PhZw6F8+22KYhezYVBaw3A4v+sdcMpTvkqIcRuHaYT45BhIgARIgARJYiABEv4Mq/uF8x8wNZh8/fvyIvNY2NK+bZ64qlv914QZhGaF5aLiABOJKgBGAccXLnZNA6hEo1lTgRq3Jxh/01Jt7s4z44s3l8tfvXD2vOz97uln2Hu2ft9wOC46pIQjusrORAAmQAAmQAAnYm8BRvdE+pum/Zm5es49g4t9Vp1XKl9Tsg9cKZp5B9s2uBCgA2nVmOS4SSCIB/KBvrS6U0jxGJCVxGlL60FdsrZQPnlUTwACZJz954oi83DoYsNwOL6bcM9I5HFhU2w7j4hhIgARIgARIgAROETiuZT8Q+W/m1qRmH9+8d77ZB8zaPr1jnXzk3NWyfG7RZjMPiH0jARsRoABoo8nkUEjATATwI7+pPF9qS7Jl2dyibGbqKPtiWwJ/dUa1XH16VcD4UH/mR386JG92DAcst8OLDjUEmXLTEMQOc8kxkAAJkAAJkMBcAj0jU/McdOeuk+zXTxzskW8/cEBGpwIjFPMNs4/NYsdSLMlmzuOTQCQEKABGQovrkgAJREygpjhH6tUgxJFGFTBieNwgKgLLVHm+7uxauayhPGA/bq1P+YNHDmrx7JGA5VZ/gQzglv5xqw+D/ScBEiABEiABEphDYHjSLc1a7sOsDWVI/r+/tMrPtdzKXGOS2uJs+fY1jXo9UGDW7rNfJJAyBCgApsxUc6AkkDwCMCdASnCOpgazkUAiCUAE/Nj2NXrHeWXAYZ1aOPvOhw4K6ujYqQ2Ou2Vg3NypQXbizbGQAAmQAAmQQLwJTLqm5VD3qMwx0Y33YcPeP8w+vq83Vu9/s2veNmfUFsktV2+RsoKsee9xAQmQQOIJUABMPHMekQRSkkBWepo0qgi4gnUBU3L+kzno5SoCfurd6+S8daUB3ZjUdNk7HmyS4wMTAcut/gJRgDQEsfossv8kQAIkQAIkIOKenjEyFjyavWDGhrTkm3fvk1eDOP2+V80+vnxZnQYAOMzYdfaJBFKSAAXAlJx2DpoEkkMAdQE3al3AVaU5rAuYnClI2aOi2PT1F66Xs1YXBzCAi953tFZN15B9DDScagjSYaPxBEwYX5AACZAACZBAihDAzbyDJ0a1vu+MKUeMUio3qdlHu9Yg9m843//796yTj9Lswx8Ln5OAKQhQADTFNLATJJBaBKqLsmWz1gFJZ13A1Jr4JI/WsXy5fP6ijUY6un9XUFcHBat7R6f8F1v6eacKgEgZYiMBEiABEiABErAmgea+sXlmGmYZyVOH1Ozj/vlmH3mZDrnpis1yQV2ZWbrKfpAACfgRoADoB4NPSYAEEkegMCfdSAnOzWRdwMRR55EyHMvlHy7dJHUaierfUDcPJ7J2qZ8HQ5BjJi4W7s+ez0mABEiABEiABAIJtA9O6I1J89X0RVTir59vlZ8+Nd/sAzf4v32tmn1U0uwjcDb5igTMQ4ACoHnmgj0hgZQjgLqAW6pYFzDlJj7JA8bn7oaddbJuRW5AT3pGnUY68IhGBNqhIbKxb8xph6FwDCRAAiRAAiSQMgTw2902EJhWa4bBI7PgB48elPvemG/2sU3NPm67ZouU0+zDDFPFPpBASAIUAEOi4RskQAKJIOCtC7iadQETgZvHOEkABam/tmuz1JbkBDBB7bzvPHhAUBvQDq21f0KmEQ7IRgIkQAIkQAIkYHoCo1NuOdozZrp+okzKzbvfkleOD83r2xVbK+UrNPuYx4ULSMCMBCgAmnFW2CcSSEECVagLqCkDrAuYgpOfpCHnZTnk67vqpbIwK6AHEM2+91CTLWrouTwzWpzbXi7HAZPFFyRAAiRAAiRgEwJT7mk51D0qZrtvByOSm/74lrTNNftYtkw+9e518tfvXC0wW2MjARIwPwEKgOafI/aQBFKGQGH2bF1AFBBmI4FEECjKyZBvaLHqlXmZAYc7onff//GRJnF6rG+k0TU8JRMue0Q0BkwSX5AACZAACZCATQh4pmcMx1+Xx1xR+08d6tUayftlZCrwPALn6l+/crNcWE+zD5t8BDmMFCFAATBFJprDJAGrEJitC1ggK/MDBRmr9J/9tB6BUhX/vqEnscVqTOPfDnSNyg8fPSRuPSm3cnubhiBWnj72nQRIgARIwOYE3tYf6sN643FCa+yZpcHs4zeG2cdR8cwJSfSafTTQ7MMs08V+kEDYBCgAho2KK5IACSSKANIINpTlyVo1adDsAjYSiDsBFK3+xpUNUqBpwf7t9fZh+fHjhy1fR29k0iM9Wr+HjQRIgARIgARIwFwEjvWNy9CEeQzIYPbxT48dkj00+zDXB4W9IYEYEKAAGAOI3AUJkEB8CFRobTbUBcxwUAWMD2Hu1Z8A7mh/XdOBczPS/BfLiy2D8n+eOiq4G27l1jYwIUgxYiMBEiABEiABEjAHga7hSekecZqjM9qL3lGn3LJnn7zcOjivT1c0VtDsYx4VLiABaxGgAGit+WJvSSDlCLAuYMpNeVIHvLo0V25UY5Cs9MCfx2eP9Mn/ffaYIE3Hqg11heYW8LbqWNhvEiABEiABErA6gcFxl8B4zCwNBiQ33fuWHNcbhv4tTdNx/vbda+Wvz1tDsw9/MHxOAhYkEHiFY8EBsMskQAL2J5DpSJMtVQVSVsC6gPaf7eSPcENZvtxweb1kpAX+RD7e1CP/8ZdWS4uA3SNTMuYMLOSdfOLsAQmQAAmQAAmkFoFx/S1G3T+z3Fd85nCv3H6fmn1MBqYiG2YfV9TLxfXlqTVBHC0J2JRA4NWNTQfJYZEACVifAOoCrl+ZJ+tWsi6g9WfT/CNA6vk/XLpJHPq5828PvnVC7nm53X+RpZ7jQqNFaw2xkQAJkAAJkAAJJIeA0zMtTSdGTVFfeEZPDH77wnH5tyfnm31UFWXJ7dc0SkNVYXJA8agkQAIxJ0ABMOZIuUMSIIF4EoBZQ4NGA7IuYDwpc98gcHptkXz+4o0yRwOU/361Q+59rcOykEan1BBEIwHZSIAESIAESIAEEktgWusJHzoxJi5P8mvyTrmn5YePHpLdr3fOg3BaTaEh/qEeNxsJkIB9CFAAtM9cciQkkDIECrLSpbG6UPLnOLamDAAONGEEzl5TItdfsEEC4wBFfvdimzyy/0TC+hHrA6G+j5uGILHGyv2RAAmQAAmQQEgCqCN8RNN+zVCKo29MzT5275OXgph97FSzD5RCyclwhBwL3yABErAmAQqA1pw39poEUp4A6gI2aJpmOesCpvxnId4Azt+wQv7u3evmHeZXz7bIC8cG5i23wgL39Nvzinxbod/sIwmQAAmQAAlYlQBuvg2o8Ueym2H28ce3pDWI2cffvGutfEzNPtLmpj8ku9M8PgmQQEwIUACMCUbuhARIIBkEUBdwndYFXK91AXmekowZSJ1jXlhfpifEqwMGDD/gf33isDR1jQQst8qLnhGnjE4FFvu2St/ZTxIgARIgARKwEgGU3ugcSn75jT8f6ZNv379fhueYfeRmpsnX1Ozjks00+7DS54p9JYFICVAAjJQY1ycBEjAdgTJfXUB+pZlucmzUoZ2NlfL+M2sCRoRIuu8/clDa5txFD1jJxC+OqSEIUpLYSIAESIAESIAE4kNgeMItzUk24ILZx90vHpefPHFES4AE/u5XaZ0/mH1sodlHfD4A3CsJmIgAr5ZNNBnsCgmQwNIJ5GtdwK2sC7h0gNwyLALvP7NaLtZoQP827pqWOx5qkn6tp2O1Nu6clm6NBGQjARIgARIgARKIPYFJPUc41DOqN9tiv+9w9wizj39+7JD88bUgZh967nybin+Vhdnh7o7rkQAJWJgABUALTx67TgIkEEggw7Fc714WCB3LArnwVewILFu2TD5x/lo5a3VxwE5R0+d7KgKaobB3QMfCeNE2OGEKN8IwuspVSIAESIAESMAyBGC21XRiRDxzIu4SOQDcnLxlzz55sWVw3mEv36JmHzvrJTeTZh/z4HABCdiUAAVAm04sh0UCqUoAAs3aFbmyvox1AVP1MxDvcaMw9ucu2iAby/ICDtU2OCk/0HRgl2cmYLnZX+DC5PjAuNm7yf6RAAmQAAmQgGUIzMy8LQdPjMqUO3nnBEc08vAbMPvonwjghrrZn9SbmR/fTrOPADB8QQIpQIACYApMModIAqlIoCw/S7ZoWgOiAtlIINYE4EL9lcvrpKooK2DXTXqy/5MnjwhO/K3Uekdd8wqCW6n/7CsJkAAJkAAJmIlAc9+YGm15ktalZ9Xs47b7Qph97NoslzbQ7GOpk6OxBlJWkMmaiUsFyO2SSoBXxknFz4OTAAnEk0CepjSgLmBBNlMb4sk5VfeNupM37twsRTnpAQheODYgd+1tsZy5RgsNQQLmkS9IgARIgARIYCkEYAyGG2vJaLNmH23yr0HMPipPmn006rkxW+QEvMLfttoiWb8yT7LS0yLfCbcggSQToACY5Ang4UmABOJLABGADZUFWtw4MFIrvkfl3lOFwMr8TBUB6yV7zkngI/u7Zffr84ttm5nLhBYq7xqeMnMX2TcSIAESIAESMDWB3lGntGtJkGQ0mH386LHDavbRMe/wEP1o9jEPS1gLkDJdrhF/FP7CwsWVTE6AAqDJJ4jdIwESiJ4A6gKu0bqAG7RmG37E2UgglgRWl+bKP1y6SVAb0L/97sU2efpQr/8i0z/HRYvTM236frKDJEACJEACJGA2AiNTbmnuHUtKt2D2cauafbzQMjDv+Jdpui9uViIzhi18Ajitg7HgtlVFso4Rf+GD45qmJkAB0NTTw86RAAnEkgCitVAXMDOdX32x5Mp9ieDO+vUXrJ+H4udPN8trbUPzlpt1wbTWLpxbLNysfWW/SIAESIAESMAsBBB9d0jrACejBPCRnjG5Sc0+WoKYfXzi/DXyCTX8mHuT0izczNgPCH/IHDpjVbFhLIi6z2wkYBcCvAq2y0xyHCRAAmER8NYFLMwOrNsW1sZciQQWILB9/Qr563euDlhj+u235Z8fOyRHkxQRENCZMF/0j7lkaCI5tYvC7CJXIwESIAESIAHTEPBMzwhMwNzTiTcAe+4ozD72ydCkO4BHbkaa3KhmH5c1VAQs54vQBCCSwtwNwh8yh2gkGJoV37EuAQqA1p079pwESGCJBNLTlsvmyvx5Dq5L3B03IwEfgSu2VsqV+vBvTs+M3PnwQekesU59vWNqCGI1J2N/5nxOAiRAAiRAAokg8Lbe6DvUPSaTWkc3kQ1mH/e81CY/fvzIPOGxoiBLbr+m0TDCS2SfrHosCH/VRdkq/BUJyrpQ+LPqTLLf4RCgABgOJa5DAiRgOwKoC4gf+Y3leUyLsN3sJndAHzl3lZy/vjSgEyN6Z/67Dx6Q4Tl36ANWMtGLKfeMdA4np4i5iTCwKyRAAiRAAiSwIIFmvWGW6N92w+zjT4flv14NYvZRVSC3X9solSposS1MwJG2TGqKZ4W/VaU5ggABNhKwOwF+yu0+wxwfCZDAggRW5GldQD1ZYl3ABTHxzQgILFdx+dM71ht1Af036x5xyp0PNQlO3K3QOtQQxCp9tQJP9pEESIAESMBeBDqHJqVHf9sT2QbGXZryu19eODbf7OOSzeXy1V00+1hsPnzCX22R1JZQ+FuMF9+3FwEKgPaaT46GBEhgCQRy1RVtq5o4FOWwLuAS8HGTIAQcehf5i5ds1CjTnIB3ESmAmoCemZmA5WZ8gULmLf3jZuwa+0QCJEACJEACSSOAEhkt+nueaNMs1BP+xh/fFJTp8G8wrfjE9jXyN+9aK47lvLz3Z+P/fK7wh3M1NhJINQL81KfajHO8JEACQQkg7L++It+oARJ0BS4kgQgJ5GQ45Mad9bJSo0z92+vtwwJ3YNQNMnsbHHcLog3YSIAESIAESIAERMacHnmjY1i6hhNb13evmn3cukfNPiYCzT5y1Ozjq3qucdkWmn2E+nyma6pvbYmm+p6M+KPwF4oUl6cCAQqAqTDLHCMJkEBYBFAXEDVANrEuYFi8uNLiBIpyMuRrQdJxnjncJ3dr8W4rNEQB0hDECjPFPpIACZAACcSLAG7atQ1MyFsq/iXS8ANmH79/uU3+JYTZx21q9nFaTVG8hm3p/UL4w3k9XH1rinOEwp+lp5OdjxEBCoAxAsndkAAJ2IdAqUZsNVYXSFY6vyLtM6vJGwkKcX91Z51kzEk1ufe1Tnl434nkdSzMIzvVEKRD6xyxkQAJkAAJkEAqEphweeRNFf7atTZuIoP3nZ5p+Rc1+/jDK/PNPlC/Gk6/cK9lCySQ4YDR36zwBz5w+WUjARKYJcCrW34SSIAESCAIAaRvoi5gcS7rAgbBw0UREthQli//++KNMvcc9K7nWuT5Y/0R7i3xq6PQeSIjHhI/Qh6RBEiABEiABAIJIOoPN8De1NId487EGnih/Mate/brOcJ8s4+L68vkRmQXZDkCO5ziryD8rVmhwl9tsVRR+EvxTwOHH4oABcBQZLicBEgg5QkgVaCuPF/TBnh3NeU/DDEAcObqYi3QvS5gT6gC+JMnjkhT10jAcrO9gCHI3KLjZusj+0MCJEACJEACsSIw5Z6WfZ0jcrx/QvAbmMgGs4+bgph9aKUa+dh5NPuYOxcZjuWydkWuIfxVFmbL8rl3W+duwNckkMIEKACm8ORz6CRAAosTQF3A2pIcqVODEKYQLM6LayxM4CK9a/+Bs2oCVnJPvy3ff+SgUVso4A2TvRiedEvfmNNkvWJ3SIAESIAESCC2BE6owccbGvU3OuWJ7Y7D2Nveo/1ym0b+DQYz+7i8XnY2VgjOTdlETgl/RVJRmEXhjx8KEgiDAAXAMCBxFRIgARIoyc0wUoKz1W2NjQSiIfC+M6oF6Tv+bdw1LXc81CT9JhfYWjUSYjrRoRD+oPicBEiABEiABOJEAFF/+zXqDxHvif6tQ7rx719uV7OPw+KangkYYXlBpsDs43R1sWUTydQa3etWIuKPwh8/DyQQKQEKgJES4/okQAIpSwDiX6MWXUZKMJzF2EhgKQRw5/4T56+VszQl2L+h3g9EwDFn4iMO/Pux0HOXZ0aLoE8stArfIwESIAESIAHLEegZnTKMPhDtnuiG39Yfq8vvH15pn3fohkqafXihwJxv/Unhr7yAEX9eLvxLApEQoAAYCS2uSwIkkPIEUBcQKcFnrCo2Cg0j/YCNBCIlgHTyz120QTaW5QVsCofBH2g6MC4GzNq6NDUKjohsJEACJEACJGB1Avi9bToxIkd7xsWjJTkS3XDz77b79sne5vmGYCgb8rUr6iU/K7UN6QzhryxXtmnEX5kKf0yBTvSnlMezEwFeudppNjkWEiCBhBGAgINCw2euKpL1elLC1OCEobfNgTIdafKVy+vUqS4rYExNJ0blJ08ekRmTptpqlpI0944H9JkvSIAESIAESMBqBFB24432IRkcT3zUH1gh1fib974lR+f8pqLE3/88b7X87bvWimN56l6u49x6g94oNYS/fAp/Vvv/Yn/NSSB1v1HMOR/sFQmQgMUI4C5kmZ6U4OQERiH5WQ6LjYDdTSYB3NW/cedmKc4JvLv/wrEBuWtvi6AmkBkbCqMjXYqNBEiABEiABKxGwK019g53j8qh7jGBEVcy2vMa8XfL7n2CCED/lp2eJjeo2ceuxsqUjXTLUeFvY3menF5TKCvzM1OWg//ngs9JIFYEeKUaK5LcDwmQQMoTgFEIHiNTbukcmkzaHeWUnwiLAcDJ7Vd31sut6vo3qQXIve2R/d1SrJ+na7dVexeZ6m/bwISU5GQI0uLZSIAESIAESMAKBAZVcGvuG9NSG8kR/nBj779f7ZB71PBjbivT8wGIf9VaazoVG4Q/1NkuzctMxeFzzCSQEAIUABOCmQchARJIJQIFGtVVUJFu1EmDENg35tJIrlQiwLFGSmB1aa586bJNcseDTeLxS/29+8U2jQ7MkB2bVka6y7ivj4unNq1ZuHZFbtyPxQOQAAmQAAmQQDQE4Orb0j8uPSPOaHYT1baoN/jTp4/K3qPz6/1trsyXL16yKSXr/eVmpkl1EYW/qD5c3JgEwiRAATBMUFyNBEiABCIlkJPh0Nol+Xo3c1pgnNA76hScgLKRQDACW6oK5foL1su/qBOgf/u5XiwUZqcbaeb+y83wvHtkykjPycvk6YQZ5oN9IAESIAESmE8Azr5He8fE6U6ewdbghMsw+Zpb7w+9vbCuTD55/pqUi6jHuQOiHZE9w0YCJJAYAszbSQxnHoUESCCFCWRpPRdESZ2hhiFIbUhP0+rObCQQhMB561fIX79zdcA70Iz/+bFDxsVLwBsmeIHI1hYtYm7WWoUmQMQukAAJkAAJJIkAzLTwG7W/cySp4h/MPm76Y3CzD/zm/9271ewjhcppoF52vdbN3qo1/ij+Jemfg4dNWQIUAFN26jlwEiCBRBNI15O72pIcFQKLZc2KHMlM51dwoufACse7YmulvPe0yoCuOjVt6M6HmuSERpKarc0agiQvpcpsPNgfEiABEiCB5BMY1XrMb3QMGxkYyewNTL1u3RPK7KNO8JsPQ7lUaBD+kOrcWF1o1DhOhTFzjCRgNgLM2THbjLA/JEACtieQtnyZVBZmS0VBllEfEHUCJ1ynzB9sD4ADXJTAh89ZJYMTbnn2SJ9v3RF13r3joQNy69WNRkqw7w0TPDEMQTSFByI3GwmQAAmQAAkkiwAi0tu1Pm2HnlshSj1ZDf3442ud8p8vtc3rAsw+vnxZnXFTeN6bNlwA4a+2OEcKc9JtODoOiQSsRYBn6taaL/aWBEjARgRwxxcOsKfXFhmpEDhBYiMBEFiun41Pv2edcZfcn0i3Fi9HJOCUn1uw//vJeu6efluOqyswGwmQAAmQAAkki8CEyyNvatQfBMBkin8w+/jJE0eCin9Ifb392saUEP8Ksh3SUFVgnMtQ/EvWfwWPSwKBBCgABvLgKxIgARJICoFijZ5CSsSW6gJNi+Ad0qRMgskOinpAX7xko6wuzQnoWbPWEvqh1gT0zCSvmHlAh06+gLMiUq7YSIAESIAESCCRBBBth4i/N9uHZdyZ3IyKITX7uP3+/fJsEKffCzatlG9csVkKsux9ngfjMgh/MDfDczYSIAHzEKAAaJ65YE9IgARIwDgprK8o0KjAQiM6MEXKwnDmQxCAk/SNO+tlZV5mwBpv6EXOz59uNp35Bgqd0xAkYKr4ggRIgARIII4EEBG/T00+jvdPCEyzktla+mfNPo70jAV0AxX+/p9zV8unNLLfzmYfRZriixvZEP8o/AV8BPiCBExDgAKgaaaCHSEBEiCBUwQg/GwoyzOcgysLswR1A9lSk0BRToZ8bVe9zE0Rf+Zwn9wdpLZQMikh8gJpymwkQAIkQAIkEG8CMMbCDTGYUSW7vahmH7fs3if9466ArmSnp8mXL6+TK9Xcy65mH8hcaVThb3Nlge2jGwMmly9IwIIEKABacNLYZRIggdQhkOlIU8fgXEMIrCnOVpMFCoGpM/unRlpZlC036AVEpiPwZ/teLTD+8L4Tp1Y0wbO2wQlB/SM2EiABEiABEogHAUT97deoP0SdTyc57M8w+3i1Q/5JS3M45/z2wezj1qu3yJmriuOBIen7LNHyNVtrCrWOdYHepGSqb9InhB0ggTAIBF5JhLEBVyEBEiABEkg8Abir1pbkqBBYLGtVEMxM59d34mchuUfcUJYvn794oxqEBPbjruda5Pnm/sCFSXzlMQxBxpPYAx6aBEiABEjArgR6RqYMo4/hyeTXnDXMPp48GjQav65czT6usafZB4S/01T4q1NDk7xMGtjZ9X+N47InAf7H2nNeOSoSIAGbEkAqcIWmBJcXZErfmEs6tej1hCu5Ba9titqUw0IUwd++e51R/8/bQZQ8+smTR6RAC20j/cYMrXfUpTUs3awBZIbJYB9IgARIwAYEILY1943J4HjyhT/ghNnHPz16SA7PqfeH93ao2cffvGutZm3Y62ZtaV6GVGtGQi5FP0wzGwlYkoC9vpUsOQXsNAmQAAlETgB1ZFZqasnptUWaepGv4g/v50RO0ZpbXFhXJh88qyag826Nuvv+IwelbWAiYHkyX7TQECSZ+HlsEiABErANgb4xp9b6GzKN+Le/c1hu+uNb88Q/BOh/9NxV8vdq9mEX8Q9mdCtU+IM53SaNaqT4Z5t/Kw4kRQnwijFFJ57DJgESsA+BYk3FwGN0yq0RgVMyMKcAtX1GypF4CfzVGdXGPP+pqce7yIgEveOhJrlN6w2VznEN9q2UwCeITO3SAu1VGi3ARgIkQAIkQAKREnBPzwhuJiHjwQwNN9l++8JxebVtaF53srQ0y+cu3ChnrrZHvT+v8FddlCPZGWnzxssFJEAC1iRAAdCa88ZekwAJkMA8AijAXFeRLpMqvHRoajDumL+N/FA22xFABOgnz18rqIH0Uuugb3wQf7/7YJPcoiKgGerytA9OqhiZoeYlvHjwTRKfkAAJkAAJLEpgUH/PkPLr8iT/RAa/rb9/uU2ePNQb9Lxqpd50g9PvKq3VbPU2K/xlCoznstTBmI0ESMBeBJgCbK/55GhIgARIwLhTu6Esz3AOrirKEtQNZLMfgeU6r5+7aKOm5OQFDA7i7w80HdgMTrxwZ2ztN09acgAoviABEiABEjAdAfxuHO0dk6YTo0kX/yZcHrn7xTb54t2vyRMHg4t/htnHtY2WF/8g/JVpfeltWloG55AU/0z3r8EOkUBMCDACMCYYuRMSIAESMB8BRF2tLs01CjafUNe8E5qOiVpxbPYhkOFYLl+5rF5u3vOWkf7tHRkunH7yxBH533ANTrIA3K+pW0P5LinKyfB2j39JgARIgARIYB4BRLVD/HO6Z+a9l8gFHk09RomNP7zSruVVPEEPDeOtD5xZIxfVl1n6RitOEVBTGuU6KPoFnWouJAFbEaAAaKvp5GBIgARIYD4Bh7rQ1RTnSFVhtvSMOqVzeDLpJ9fze8klSyWQl+WQG3dulpt3vyWDE6fcEV9oGZC79rbIx7evEaQMJ7Md0xpOp9ekJ12MTCYDHpsESIAESCA4gRmN+juu9fVQNzaZ7W2tm/LCsQH5nUb94cZpsJapN97ee1qlXLm1ytK18Sj8BZtdLiMB+xOgAGj/OeYISYAESMAggEiwisIsKdcUj36tZ9OpqaLjzmnSsQEB3L3/6s56uXXPfpl0n5rTR/Z3GwYx126rTuoopzSaA8IzhGg2EiABEiABEvASgIHZ0d5xo36xd1ky/jadGJHfPH98nrOvty+4j3ZRXZm8/6waKbZwRDuEv7KCLI34y2J9Xu/k8i8JpBABCoApNNkcKgmQAAmAAKLBVmjBajyGJlyGYcjIZPAUFxKzDgGke3/psk1yh5qAeDSawttQv6g4J112bCrzLkrK3w41BMFnjilGScHPg5IACZCAqQgg6g81a/FIpmEZjv87dfb1N9SaC+osdfb98NmrpFqNMazaIPyVG8JftqB8CBsJkEBqEqAAmJrzzlGTAAmQgEEAddnwwB34zqEpTSF1JfVEnNMSHYEtVYVy/QUb5MePH5ZTEqDIz59ulkKtV7Sttji6A0SxNTTJlv5xqa8oiGIv3JQESIAESMDqBMadHqPWXzKzEHADFDX+Htdaf373zALQrl+ZKx89d7VsrrTu7xaM4JD5UallYCj8BUwvX5BAShKgAJiS085BkwAJkEAggfysdKmrSDdScJCq2ae1AkOdEAduyVdmI3De+lIZmnTJv+9t9XUNc/nPjx2Wb763QdavDHQN9q2UgCeD424Z0PTzklwagiQANw9BAiRAAqYigBp7RtSfRoQn6xxjSstk3PdGlz46xekJbjYCwew6jfg7d21J0mvoLnUCIfxVaMRfpab6pmstaDYSIAESAAEKgPwckAAJkAAJ+AhkZ6QZAlGNprnANbh7xCnTyTpL9/WKTyIlsKux0hDacJHjbbjQufOhJrn16kajFqR3eaL/IgoQ0Yi4OGEjARIgARJIDQKTrmkj6i+Uq268KeBc5omDPfL7l9sFbsPBWr6aar3vjBq5ZHOZwEDNis2RNiv8oeYzhT8rziD7TALxJUABML58uXcSIAESsCSBTEeaoKZcdVG2dGs04AmNCnR5/JNKLTmslOr0h89ZZbgCP3ukzzfukSmPfPfBAyoCbjFSv31vJPCJE4YgWnOptoSGIAnEzkORAAmQQNIIdOk5xPH+iaRE/SHq8OXWQfnti8eNUifBIKSraHbl1kq56vQqycmw5uWxV/irVOHPquJlsLnhMhIggdgSsOY3XGwZcG8kQAIkQAIhCOAkEiJgpaaR9I45DeEGjq5s5iewXM1ePv2edTKikQ5vdgz7Otyjgu6dDx+Ub2k6cLIMOSAAwhAEEadsJEACJEAC9iSAdNujvWP6O5Qco7HD3aPya3X2Pah/gzXEoe/YtFI+oM6+pfqbZMVG4c+Ks8Y+k0DyCFAATB57HpkESIAELENguVFEOkvK8jOlX2u4QcBJZvFuy4BLckch4H7xkk1y23371IBjwtebY33j8sPHDslXLq8Tx/LEpzkhqxx9aKiybmF1H0w+IQESIAESmEegZ2RKWgcmxDOd+OwBRBze/WKbPH9sYF6/vAu21RYJIuVXWTQaHVGLSPNFnT9G/HlnlX9JgAQWI0ABcDFCfJ8ESIAESMBHYJlGlSFyC4/hCbdRzDtULR3fRnySVAKIsvvqznq5efc+QfSft73RPmy4A39mx/qkFDnH56ZPo0rxWWIjARIgARKwBwGX1ptt7hsTmD4luiHiHc6+fzrQI9Oa+husrV2RKx9R4a+xujDY26ZfBuGvUjMzIPyxlq7pp4sdJAHTEaAAaLopYYdIgARIwBoECnPSBY8xp8eICIS7a4jzbWsMyMa9LMrJkBshAu7ZJ/4F2J853CfF+h6iIJLRWjUqEcfnRUwy6POYJEACJBBbArip06LR3e4ER/05PdPy4JsnZPfrnTKpacfB2kq92fShs2vlvPWlghIZVmsZDhX+CrOlnMKf1aaO/SUBUxGgAGiq6WBnSIAESMB6BPIyHbKpPF9Q66dDU4P7NMqMxsHmm0dEDNygKb/fvv+AwBHY23DBVJKbIZdvqfAuSthfRIq0D04YhjMJOygPRAIkQAIkEFMC7ukZQ/jrG3PFdL+L7WxGTzaeOtwr97zUZpheBVs/NzPNcPa9tKHckq64EP6q9Pe7PD9LUI6FjQRIgASiIUABMBp6Ft4WaXzhtB07dsiTTz4ZzqpchwRIIMUJwFBi/co8qS3OUdfgKXUPnkpK7Z8Un4YFh7+hLF8+f/FG+cEjBwNE2ruea5Gi7HQ5d13pgtvH480u/ays1NqSVnVejAcT7pMESIAErEJgUKP/kfLr8gRPuY3HOODs+1rbkPzmheN6E2ky6CGQKrtTb2xdva1acKPSai3DMWvChtrLFP6sNnvsLwmYl4D1vg3Ny5I9IwESIAESUAI4aV1VmqN3rLNUBHSqGDiZ0AsDTsLCBM5cVSx/9+518rOnm30r4rLtX584IvkqAjZUJtaYA2njzb3jlq3H5IPIJyRAAiSQQgSmNfqupX9cekZO1ZZNxPDhKvwbdfbd3zUS9HAIcXjXhhXywXfUGjeXgq5k4oU4h6opzhakLFP4M/FEsWskYFECFAAtOnGx6vZnPvMZuf7660PuLjc3N+R7fIMESIAEFiIAV7pqTVup1Ho1vVoXCM7BU+5TqacLbcv34kvggroyQc3Ge15u9x3IoxdziAy8+aotCXdFRF3CHo0YLdMUJzYSIAESIAFzE4CJE4Q4ZwJ/0+EqfLem+j53tD8kHBh7wOADRh9Wa5nps+dMFP6sNnPsLwlYiwAFQGvNV8x7W1ZWJo2NjTHfL3dIAiRAAl4CuIONotVIY4Ho1Dk0ZRiHeN/n3+QQ+KszqrVmkkseU7dEb5twTcv3HmqS267eIqUJdudtG5iQEjUEgXDMRgIkQAIkYD4CqLnXqt/VKPORqDY65ZY/vtohj+zvFtyoCtZWleTIR89dJafVFAV729TLsrzCn54jhVuiydQDYudIgARMTYACoKmnh50jARIgAfsQwIktRCU8hifchmEIogjYkkMA8/GJ7WtlSOfipdZBXycg0n73wSa5RSMB87ISd5qA+lFtWsvJipEbPnh8QgIkQAI2JQAh7qiWa5jUG0WJaDCJemjfCbn3tQ7BzalgrVQNrP6Hpvoi5ddq6bKG8Hcy1ZfCX7DZ5TISIIF4EEjcmX08es99kgAJkAAJWJJAYU664DHm9EiXpgb3q+iEWnBsiSWAC6bPXbRRvvPAATnYPeo7ONycv6/pwF+/YrNR09H3RpyfdGuKFwxBrFiwPc5ouHsSIAESSAoBRP3BaKNT6/km4nd6Rg/y58N98p+a7otzg2AtJyNNrlFzD5h8oGaelVq29h3lUVbkZTDiz0oTx76SgE0IUAC0yURyGCRAAiRgRQIQejaW50ute9qoEdirpiEhMnysODxL9BkXT1++rE5u2bPPiMr0dhqC4L8+cVi+cPGmhEVW4OKypW9ctlQV8MLIOxH8SwIkQAJJIjCuN+lQ62/cGTwCL9bdeqNdnX3V4ANpxsFamt60uryhXK7VEhb5WenBVjHtMoiW1Rrxh6hFRvyZdprYMRKwPYFlaqPOmAvbT/P8AXp/eBoaGvRunrp4tbRIWlqaVFRUyPbt2+XjH/+4XHjhhfM3jNGS9vZ2qa2tNfbW1tYmNTU1Mdozd0MCJGBlAkj5QW2hbjWE8Ezz5ymRc9mnRi3fuvctrQsYmJZ9qV5sfWL7moResKxbmWvUjUzk+HksEiABEiCBWQK4NkAkeIdG/iXiphzchH+rwt8bHcMhp2D7+lIj3Rc1ha3UIPzB1TfRdXWtxIh9TQwBXn8nhrPZj0IB0OwzFKf+eQXAhXZ/7bXXyq9+9SspLCxcaLWg7+ELZqHW1dUl55xzjrEKBcCFSPE9EkhNAp7pGXWFdUqXphyhNhxbYggc16iLW3bvk0mNyPRvH9IaS4i4SFRLT1smp9cWSToNQRKFnMchARIgAYMAavwh6g/u7PFuuPGEVF+k/Ib6pW+oLJCPqMHH+pV58e5OTPefmzmb6kvhL6ZYubMoCFAAjAKejTalAGijyYxkKLm5uXL11VfLxRdfLPX19ZKXlye9vb3y1FNPyU9/+lPp7+83drdjxw559NFHJT09sjD7cARGb38pAHpJ8C8JkMBcAqg9hAuETo0KTFTh8bl9SLXX+zuHDROQuW6Ln96xTnZsKksYjrKCTMtd8CUMDg9EAiRAAnEggJtux/sn4h71h9RimHvA5MMdItofUXMfOWeVbNObQZFcV8QBS0S7RGkTpPqWaKovGwmYiQAFQDPNRvL6QgEweeyTeuShoSEpKioK2ofu7m7ZtWuXvPrqq8b7P/rRj+Tzn/980HVDLYzkh5oCYCiKXE4CJOAlgHQkuNN2qRCYiKgE73FT9e/eo/3y48cPB0RkaOkl+crldXoxVpwwLI3VBZar85QwODwQCZAACcSIwJRGfSPqb2QyvlF/bo3uf3R/t/z3qx2GCViw7herQdgHz6rVG04rE1Z/Nlg/Il2Wn6XCn5p7FFP4ixQd108QAQqACQJt8sNQADT5BCWre83NzUZkoNvtlg0bNsjhw4cj6gpTgCPCxZVJgAQiIDA86TYMQ4bm1KqLYBdcNQwCD73VJXftbQ1YM1MNQ266skE2lCUmFQspVFurCy0V/REAjC9IgARIwOQEetR9vUWj/qbjWOwPzr64sXT3i23Sq1H9wVp2eppcdXqV7GqskCx9bpUG4Q/RikU5jPizypylaj8pAKbqzAeOmy7AgTz46iSBdevWyaWXXioPPPCAHDlyRDo7O6WqqipsPjT1CBsVVyQBEoiQQGF2uuCBFKJOLVLer5GBem3BFmMCOxsrjajLPW90+fbsVJOWOx9ukluv3iKVhdm+5fF6AufJE3pxmohjxWsM3C8JkAAJmJEATLea+8ZkcDzQ+CnWfd2nZSV+rQYfx9ThPVhLW7ZMLt5cJu87s8b4bQ+2jtmWaZeNvlbp72ChRiyykQAJkIBVCFAAtMpMJaGfcAiGAIjW0dERkQCYhO7ykCRAAilGIFfr7Gwsz5daTV1CajCiGOIYwJBidGeHe53WX4Ir8J+P9PnGjxTsOx6cFQETEfHQri6UpbmZkqHRh2wkQAIkQALRE0Bt3RYV5ELV34v+CCJtair1mxeOy2ttQyF3d+7aEvnQ2bWWuckDg6qy/CxBjVorRSmGnAC+QQIkkHIEKACm3JSHP+BI6viFv1euSQIkQAKxJYCT8LUrco3aO90qAiJizBOiqHhsj2z/vS3XMIe/f886rQvlljc6hn0DhkPznQ8flG9qOnB2RnxTtTCXxwfGNe0433d8PiEBEiABEoicAGrwQfjrG3NFvnGYW6Be7z3q7PvU4d6Q0fl1evMOzr6b9K8VGtJ8ywuy9GZUhqXqElqBLftIAiSQWAIUABPL21JH279/v6+/kaT/+jbiExIgARJIIAFEiNWW5EiVFuGGEIioQKQ4sUVHwJG2XL5wySa5/f79ASlcSOf658cOGcYgWCeerXfUJSvz3ZZJD4snC+6bBEiABJZCYFCFOaT8ujzxqZkx4fLIntc75YE3T4hLhcZgraowSz6skeVnrS42fW3XNHW+WpGXYQh/yDhgIwESIAE7EOC3mR1mMQ5jOHbsmDz66KPGntevXy/V1dVxOAp3SQIkQAKxJ4CTdoiAFXq3HmlOnSoETrqmY3+gFNojovxuUAfgm3fvE0T/eRuiAn/+dLN85oL1cb+YQ9TKaTU0BPGy518SIAESCIeAB1F/avLR6/fdHc524a6D/f+pqUf+8Eq7oEREsIa6vR84q0YurCsT/EabueH3DucPEP/ifXPLzBzYNxIgAXsSoABoz3ldcFR79uyRXbt2icMRfPq7u7vl/e9/v7hcs+kB119//YL745skQAIkYEYCy/Uio0xP4vFAShIMQ0JdnJix/2brE+r93bir3hAB/Tk+o/UBizUtClEd8WwTKuJCzK1WcZeNBEiABEhgcQLDWsP1qEb9Od3BI/IW30PoNd5W960Xjg3I79TZF6U3gjU4x7/3tEp9VJm6Zh5MPUr0dwxpvhAr2UiABEjArgSCK0B2HS3HZRD43Oc+J2632xD5zjvvPFmzZo1kZ2dLX1+fPPnkk/Kzn/3MeI6V3/Wud8lnP/tZkiMBEiABSxPAiT0ew1rLDkLgkF4UsUVOAG68N1xeL9/WdGA4Anvbbk37KlaBcGdjhXdRXP52qCEIojIyHfGtOxiXznOnJEACJJAgAtPqiHVcTThO6E2TeLSmrhH5tRp8HOkZC7p7BPkh2u/9GvWH3wazNpQOKVdDDxh70GjKrLPEfpEACcSSwDK9exOfQhCx7CX3FVMCEPxaW1sX3SeiAH/xi19IUVHRoutGukJ7e7vU1tYam7W1tUlNTU2ku+D6JEACJLBkAuNOj9YInDQKofNXMHKMrx4flO8/cjDAdVmv9+TzF2+Ud64rjXyHEWxRqgKgVQrHRzAsrkoCJEACMSEwOuU2hLmpOET9degNtN+p8PdS62DIvr5D6/tdd/YqqS42b7Q2ovwqtB5hcU563MtXhATFN0ggwQR4/Z1g4CY9HCMATTox8ezWXXfdJU899ZTs3btXmpubjWi/kZERycvLM0S57du3y8c+9jFBdCAbCZAACdiRAAp6w1W2pnjaMAvp0fQlDZhgC5PAGauK5e/evU5+pvX/vA34fvLEESnQC6uGygLv4pj/7Vf3yqF8lyAlmY0ESIAESGCWwIz+iLVrlHSn3tyK9Y2toQmX/P7ldnniYE/I38oNZXnyUS0FUR/H7/9o5jo9bZmaSWUaab5Z6Ywij4YltyUBErAuAUYAWnfuLN1z3oGw9PSx8yRgOwJuLWKOVCm4B7unqQSGO8H/pUXf79GLQv+WowXUb75qi6xSR+Z4taz05XJ6TZGgziMbCZAACaQ6AUS1Ix0XtVJj2abc03LfG5366Aoo++B/DBhmXHd2rZyztsSU0XR5esOvvDBTVuRm8jfDf+L4POUI8Po75aY86IAZARgUCxeSAAmQAAmkEoH0tOVSq4IV3IN7Rqe0TuCUuPxq3KUSi0jG+ldnVMug1lN87EC3bzNcgH7voSa59eotWq8v07c8lk+Q2oYol5ri+ImMsewv90UCJEAC8SCASk5Iy0XkXyyj/jwzM/JEU6/h7IvaucFafpZD3n9mjVxcX2Y6t1zcG1pxMtoPAiAbCZAACZDALAF+I/KTQAIkQAIkQAInCaTpVQOMLhDR0DvmlC4VAmMdUWEn2MvUOvET29eouYpLXmw5VRMKrst3PNgkt2gkYJ5eJMajzRqCZJraWTIe4+Y+SYAESAAEJvVmy9HesZi620NQRH0/1PmD63qwlqE3zK7YWilXnV4pORnx+X4PdtxwlmVrBDpMPVbqzSeH9pONBEiABEggkIC5vrUD+8ZXJEACJEACJJAUAhC24AqIB8QsOAePTnmS0hezHxRpuP/rwo3ynQcOyMHuUV93EZUCo5CvX7E5Lu6KqNnY0j8u9RXxqzfoGwyfkAAJkICJCMDE6nj/RMh6fEvp6iH9/v7N88cDvsf996M/i3LBppXygbNqpSTXPDVY0S/0p1x/rwvV1IONBEiABEggNAEKgKHZ8B0SIAESIAESMC4scHExos6KEAIHx4OnQ6UyqgzHcvnyZXVyy559RjqalwUEwR8/fli+eMmmuNRewlxAoDXTxah37PxLAiRAArEmgJp8iPobmYzdDSmIib97sU1eODYQsrvbaovkI2rwgVIZZmkZjpM36jTiL9NBUw+zzAv7QQIkYG4CFADNPT/sHQmQAAmQgEkIFGSlS0FFuqYEe6RVIy+GtPYd2ykCSPW9cVe93Lx7nyHKed9BOtkvn2uRT56/Ji4F4hEFWKjOw0jfZiMBEiABuxKAW32L/vZMx8iyHrX9YOT0pwM9Mh2igODaFbmG8NdYXWgarPi+R5ovbvwgWp+NBEiABEggfAIUAMNnxTVJgARIgARIwKh5tLmywBC5WlV8giEF2ywBmH58dWe93KqRgP61E2ESgos1mIbEujlhCKKRmWaKTIn1GLk/EiCB1CUAQ6rmvrGYRZ8jivCBN7tkj7r7hvr9Qg29D6mz73nrS2W5CUQ2R9oyo65fudbnRZ0/NhIgARIggaURoAC4NG7cigRIgARIIMUJQNAq0kgEuNHCNThWURlWx7pKU8S+dOkm+a6agHj8IlX+86U2Kdb6TBfUlcV8iBAAIT7ywjDmaLlDEiCBJBLoHXVqxPm4uKe16GmUbUa/j5861Cv3vNxmuLcH211uZpq874waubShXNJNYIEDMNgAAEAASURBVKIBB19E+5Xq9zujvIPNGJeRAAmQQGQEKABGxotrkwAJkAAJkICPAAwwaopzZGV+plGQvW/M5XsvlZ80VBXKZy/cIP/yp8Pif9n6/z7TbKTrnrGqOKZ4oDMe6xuXhioagsQULHdGAiSQFALu6RnjO60/Br8pcPZ9tW1IfqvOvu2Dk0HHk64Rdju3VMjV26oFolsyG6o5QPCD8JevpTfYSIAESIAEYkcgud/wsRsH90QCJEACJEACSSOAAuQby/OlvNAtLSpEjTunk9YXsxz4netKtU6iS+7a2+rrEoS6H6koeNOVDbKhLM+3PBZPUM+qb8xpRALGYn/cBwmQAAkkg8CgGhsh5dfl8b99srSewDAEzr77u0aC7gAV9N61cYX8j3fUJv27Myt9uYp+WcYNNTNEHwYFxoUkQAIkYHECFAAtPoHsPgmQAAmQgHkIwChkqxZL79G0rbaBiZikbZlndJH3ZGdjpVErcc8bXb6NnVrP6s6Hm+TWq7dIZWG2b3ksnsCcpTgng6lisYDJfZAACSSUgEej/mDygbTfaFu3GobcrWUX9h7tD7mr0/S36sPnrpI1pbkh14n3GygviO9sRPsV6V82EiABEiCB+BKgABhfvtw7CZAACZBAihGAKyGiGEq1RmCbplvhQiyEwWJKkLnunFVGvak/H+nzjXd0yiN3aI1AiICxvOhDsfz2wQlZncQLWt8g+YQESIAEwiQwrK7yRzXqD6ZG0bTRKbf896sd8sj+7pB1aVdrndaPqPB3Wk1RNIeKatsMxzIpy5+N9stKp6lHVDC5MQmQAAlEQIACYASwuCoJkAAJkAAJhEvAoQXU167INSIbUJ9uZNIT7qa2Wg8Okn//nnU6fre80THsGxuiJO98+KB8U9OBY2ne0TU8ZaSQ5WTwFMcHm09IgARMSQDmUcc1WvyEfm9F03Dz46G3uuTe1zsDHNj994mbUnD2PX/DiqQ5+xZkw9Rj9gYZbpaxkQAJkAAJJJYAz44Ty5tHIwESIAESSDECEKK2qClGv9ana9ULvWgjPKyID2LoFy7ZJLffv98obO8dA4TRHz52SG64vE6wTiwaoi2be8elUdPb2EiABEjArAQQrXekZ0ymooj6g7PvMxpdDZf1Aa0dGKzlZKTJtWrucbmafGQ4YvM9G+w4oZY51GAELu1I8+WNmVCUuJwESIAEEkOAAmBiOPMoJEACJEACKU4AroaoddQxNCmd+oAhRio1RPlB6Lt59z6jRqJ37G9qVODPnm6Wz1ywPmZRKUgx7hmdMlLMvMfhXxIgARIwAwGIdnDj7RyejKo8xOvq7PsbdfZFBGGw5lA73ctU9Lt2W1VS3HRzM9OMaD+If2mw9mUjARIgARJIOgEKgEmfAnaABEiABEggVQgs14ugWq2/tDI/07ho6x8LHrFhVx6o93fjrnpDBIRI522oD1ick651qVZ7F0X9FyYsJXq8WEUWRt0h7oAESCDlCYw7PUbU34Rr6U7xiJz+rQp/uHkSqp2/vtRw9i3TdNtENuh8pXkZguPCFIuNBEiABEjAXAQoAJprPtgbEiABEiCBFCCAouebyvNluMAtLXoxF83FoNVwwfn3hsvr5duaDgxHYG+DU3CJ1qiCc3AsmsvztmHCgjqMbCRAAiSQTAJva20CRH8j8m+pplBwB75HU31xwyRUAHlDZYF8VA0+1q3MS+hwM9OXG9F+ZXpzKz1G5RwSOgAejARIgARShAAFwBSZaA6TBEiABEjAfAQKs9PVibFQnYKdKlZNiGc61GWd+foeTY82lOVpTcCN8o9qAuKfCv3ve1ulMDtDztPolVg0ODAj2jIvk6c7seDJfZAACUROYFKj/VDrb0yj/5bSsN29r3XIw/tOiDvEb0RNcbZ8RB3Xt9UWSaLMNeDhUaSR2+Xq5ou/iTruUhhyGxIgARIggVkCPCPmJ4EESIAESIAEkkgAF00VheqKqGlTSFuFO+5SI0SSOIyID72ttlg+pe7AP32q2bct5M9/e/KIioAOaVDjlGgbOCLCcktVAS9Oo4XJ7UmABCIm0KV1/o73TwTc6Ah3J+7pGXlkX7f892vtMu4MnjKM0gkffEet7Ni4UlBiIhEtXU09ylT0K1NTD0Szs5EACZAACViHAAVA68wVe0oCJEACJGBjAkibQtpWudZOQo0n/xp5dh32jk1l6lzpNhwsvWP0aEjgDx49JDdftUVWab3EaNusIYjT4Brtvrg9CZAACYRDYMo9LUd7x2RkMvKovxm9c7H3aL/c/WKb9Kp7fLCWrcLb1adXya6tFZLpSIwIl5/lML5HS7VUQ6LExmBj5zISIAESIIGlE6AAuHR23JIESIAESIAEYk4gV9NVG6sLpU8v/Fo1csTlVycv5gczwQ7hUDk44ZJH93f7eoOaiHc8eEBuu6ZR4CAZbTMMQfSilbWpoiXJ7UmABBYjgNID+O6e9q9vsNhGJ99/S4094OyLm0DBWppGjF/SUC7vO6NaCrSERLwb3HtXaHQ6bkzht4mNBEiABEjA2gT4TW7t+WPvSYAESIAEbEoAwlexuth2auF4PJZwLWkJMkiB/vh5a2RIRcAXWwZ9fR6ccKsI2CS3aCRgnkaeRNNQN+u4plevT3Bh/Gj6zG1JgASsRcDpmZbm3nH9LnNH3HF8P8HZ97W2oZDbnru2RK47e5VRMiLkSjF6IycjzRD9IP7RST1GULkbEiABEjABgejOqE0wAHaBBEiABEiABOxKANEXtZoGCyMLRJQMjLtsOVSkk/2vCzfKdx44IAe7R31jhGvm9x85KF+/YrNkOJb7li/lSY8arcChMj8r/lEzS+kftyEBErAuATj0tvSPR2zk1K+R3ve83C5PH+oN6exbp47xcPbdqH/j2WDqgfTeMo32g0EVGwmQAAmQgP0IUAC035xyRCRAAiRAAjYjgELrdRX5RpRciwqBcJW0W4PA9+XL6uSWPfsEwp+3QRD88eOH5YuXbIq67hTS6rZqejXdKr10+ZcESCAaAjDqwPdK/1hkN2cmXB7Z83qnPPDmCXHpPoK1qqIs+bA6+561qjiu31n47i1XQw8Ye0R7oyXYOLiMBEiABEjAPAQoAJpnLtgTEiABEiABEliQQJGmBJ+ukRkntMZU++BkxNEmC+7cBG8i1fdru+rlW7v3BUQ7vtQ6KL987ph88vy1UV0Iw0kT7CoLs00wWnaBBEjAygQQkX2sb0zrtMK/PLzmUbHvsQM98l+vtoc0ekL03QfPqpEL6soEUeDxakXqIIzafnAS5k2ReFHmfkmABEjAXAQoAJprPtgbEiABEiABEliQAC7UIGCV5mZK2+CEILXVTq1Uax/euLPeiASEGYi34aIZNRHfd2aNd9GS/kI4BTtGuiwJHzcigZQmgIg/pPv26COSSOy31dn3+WMD8rsXj0t3iO/sTI3Eu0qdfa/cWimI+o5HS09bZpSUgPAXr2PEo9/cJwmQAAmQQGwIUACMDUfuhQRIgARIgAQSSgACFkwtcCHXoiloo1OehB4/ngdD3cMvaTrwd7UmoMfP/QS1soq1RtWFGhmz1OYxDEHGZUNZfOtpLbV/3I4ESMBcBCDeDU+6DdEPUX/6MqJ2oGtEfv18qxxVg5BgDUF+F9WXy/vPrBZEecej5Wt0dZmm+a7Qmx+oucpGAiRAAiSQmgQoAKbmvHPUJEACJEACNiGQl+mQRq1r1zM6JW3qJBlJOpqZETRUFqgxyAb50Z8OBxTH/8UzzUaB+jO1LtZSW++oS6Ng3Cx0v1SA3I4EUoDAlHvaF+3n8gSv07cQhg6NNv6tRvy9rCUMQrV3rC6W67TOX3VR7MsSIH24VF18cZMIvxNsJEACJEACJMBfA34GSIAESIAESMAGBFDAHamt7ZoWfGJ4SvwC5yw7unPXlcr/nHDLXXtbfGPAuP5FRcGbrmzQKL483/JInyBq8rQaGoJEyo3rk4CdCczoF8zAhMsQ/hD1F2m0H9gM6vZ/0Gjlxw/2hNx+o353fUSdfesrCmKOMzsjzTD1WKnlFBxp0bmnx7xz3CEJkAAJkEBSCVAATCp+HpwESIAESIAEYkcAER+rS3Nn04L7x2Vw3B27nSdpTzsbK4wL6t3qmOltTo3GufPhJrn1qi1SucTIGdQX7FShNB6RN95+8i8JkIA1CMCVF/VU+8ac4tYyAUtpqAl435udcv8bXYLvqGCtQqPxrjunVs5ZUxJT4w0tDSslWh6hXG8EFaqpBxsJkAAJkAAJBCNAATAYFS4jARIgARIgAQsTQHF3RJYMar2qFhUCp9zBL0atMsTrzq41xvLMkT5fl1Hz8I6HVAS8esuS62YhRW+FpshlOuJTcN/XWT4hARIwHYFpjfbrV8EPhh7R1FD1zMzIE0098vtXOmREowaDtQKtwfd+NTC6aHOZOJbHLioPtWDL8jON+n78HgtGnstIgARIgAT8CVAA9KfB5yRAAiRAAiRgIwIwzCjMTpeukSmB2IULXis2OB9/asc6oxD/Gx3DviHgwv3Ohw/KNzUdGGlvkTbwaO2fkE3lNASJlB3XJwGrEhidmjX06B9zRfWdCHOQl1oGjTp/XRpNHKxlaAruladVynv1kZMRu8sufK+Xq6kHov7w/chGAiRAAiRAAuEQiN0vUThH4zokQAIkQAIkQAIJJQDHR6S5oh7U8YFxrW3lSujxY3UwRM184ZJNcvv9++WY1u/zNjz/4WOH5IbL65ZU7woiwFC+a8lRhN5+8C8JkIB5CbinZ4z0XqT5Iv0/mgbh7+CJUUP4O9Q9FnRX0OQu2FQmHzirxhDpgq4U4UJH2jLjexymHku54RHh4bg6CZAACZCADQlQALThpHJIJEACJEACJDCXAFLFNpTla9SIW1r6JmTM6Zm7iulf46IXQt/Nu/cZaXveDr+pUYE/fbpZrr9gvSxfQjQMRMTTa9IFYikbCZCAfQgMq4kQHNIHtBxCtAHQXUOT8uzRPnnuaL+EivgDuTNXFcl1Z6+S2pKcmICEgy+i/Ur1Jg7qvLKRAAmQAAmQwFIJUABcKjluRwIkQAIkQAIWJJCflS6N1QWGy2WbOga7PNZKCy7KyZCv7dqsIuBbMqJ1AL3tWa0PWKzF7z967mrvorD/okZih17cx+qCPewDc0USIIGYE3B6po3vN5QIcEZZ/xTC4V4V/CD8+UceB+v0uhW5hrPvlqrCYG9HtAw6HwQ/CH/4zmYjARIgARIggVgQoAAYC4rcBwmQAAmQAAlYiABqRpVpGhnqR7VrbcATWiNQs9os0yoKs+SGnfVy+337A9w271P3TYxpV2NlxGPpVAFwpRbTh4EKGwmQgLUIIC138GS035D+jeb7bExvLDzf0i/PHemXA10jsthXI0w4PqRGRe9cV7qkCGR/0lnpy1X0yzK+i9K1fiAbCZAACZAACcSSAAXAWNLkvkiABEiABEjAQgQceoG5RqNWyjTKBGnBwyEcLM04pPUr87Qm4Eb5RzUB8U/t+4+9rVKUnSHnrS+NqNvYBxyT4Z7MRgIkYA0CU+5pQV2/3rGpqKKZETX4SuugRvr1y2ttQ2GZg0D4u3xLhVzaUC7RiHWoWlCskc2I9kOEMxsJkAAJkAAJxIsABcB4keV+SYAESIAESMAiBOBO2VBVYNTJgggWbdpcooa9rbZYPvWedfLTp5p9h0S0zr89eUQKsh0SaSre4LjbYIAoQjYSIAFzEphRtb5fU3NR229k8lQZgEh765mZkTfbh42afi+2DAREE4faV4G6727XSL/zN5QKbkJE48Cb4dBI7Pws4wZMpoORx6GYczkJkAAJkEDsCFAAjB1L7okESIAESIAELE0AwleRXuB2Dk9K59BUWFEwyR7wDnXahHB390ttvq54VCD4wSOH5OarGmR1aa5veThPIIAWKgMW2w+HFtchgcQRGFfjom4tVwDxzzO9WGJu8H7NaG7woe5ReVbTe//S3B+WGVK2lgU4Z22JbNeoYtxUiPa7ATcnkOZbqt+30QiIwUfIpSRAAiRAAiQQmgAFwNBs+A4JkAAJkAAJpBwBOOHWFOcYNaiO909I35jL9Ayu2VYlAxMueXR/t6+vk5oa+L2HmuS2axplhRbTD7ch+hH1AGkIEi4xrkcC8SPgmZ4xvoMQ7TfunF7SgVAf8PjAhBHp95yaeYTznZaetkzOWFUs569fIdtqiwQu6tE0h+4P30NI80XENRsJkAAJkAAJJIMAf4GSQZ3HJAESIAESIAGTE0BK2sbyfCkvdGt9wPElX3wnYpiIovn4eWtkWIv/v6CpfN4GU4A7HmySW67aInlZ4Z/yQADExXp2BtPyvCz5lwQSSQD1SHtV9OvXGxD+NT4j6QOiBZ+Dg686hMPle7GGWnyNGuGH9N6z15TERKjLzUwzov3wfRJt5OBi/ef7JEACJEACJLAYgfDPhhfbE98nARIgARIgARKwHYGCrHTZWl2o9bac0qZRNO4lpt7FGwwiFz974QYZfuCAHNQUP2/Dhf8/PtIk37iiIewoHggOx1T0RF1ENhIggcQQcHlm1MxDDT30u2bStbRovyGNBP5L84AKf31yuGcsrI5vLMvT9N4V6uJbEhMTDv0qktI8mHpkSb5+f7KRAAmQAAmQgFkIUAA0y0ywHyRAAiRAAiRgUgKIsPPWrGobnDTqcGlWneka0vS+fHmd3LJ7X0DEz6HuMfnx44fli5dsEgiF4TREIPWpGBFJ+nA4++U6JEACpwggPXdII3Vxg2FQxbulfK9MuDwCEw/U9XurczisfVQXZWuk3wqjrh++22LRMtOXG9+TcAeOxhU4Fn3hPkiABEiABEggGAEKgMGocBkJkAAJkAAJkMA8Ao605bJ2Ra5e5GYaEXLROHDO23mMFuRlOuRru+rlWyoCDqhZgLe91Doov3zumHzy/LVhF95v1RqIxTkZTN3zQuRfEogRgSmt0YlIPwh/iPyLtGGb19qG5FmN9Hv1+GBYkckw3YCRB4S/VSU5YX8PLNQ3pA0X5aRLhYqIMA+iqcdCtPgeCZAACZBAsglQAEz2DPD4JEACJEACJGAxAihiDzfMfo2Qa9W0YBhnmKmVar2tG3fWyy179smEXyrhYwd6DEHvfWfWhNVdiAztgxMROwmHtXOuRAIpRmBGc+th1tMz4hRE2EbapnX7fRrhh7p+LxwbEBj9LNZwQ+Cd6yD6lcomrWm6HIpdDFqWRvuV5mZKmd4MyVKXYDYSIAESIAESsAIBCoBWmCX2kQRIgARIgARMSABCGyLkUGcPxhlLLdYfj6HBxffLl9XJdx88EBAddM/L7UafL6wvC+uwXcNThiMynTvDwsWVSGAeAaToQvRDSn2kNUSRInxEa/lB9Nvb3B+WcJippQBg4oFov601heJYHp2Dr3dAEBOLc9OlRCMJ+X3gpcK/JEACJEACViJAAdBKs8W+kgAJkAAJkIDJCKCmHsS2lVr36rhGA8K10yxtc2WBYQzyo8cOi3/Jwl/8uVkKNW3vzFXFi3YVNcmae8elUY1Q2EiABMIjgGg9RAgjxXd0yhPeRn5rIfIWNf1g5oF9LNbgsLuttkjOV9HvzNXFAhfzaBuCBWGCBMEPwl8s9hltn7g9CZAACZAACURDgAJgNPS4LQmQAAmQAAmQgEEAaXBIsRvOd0tL/3hA6m0yEZ27tlQ+tt0tv3quxdcNRCpCFPzmezfLhrJ83/JQTyBg9IxOSVl+bMwCQh2Hy0nA6gRGp2YNPXAjACJgJA0Rgoj0e+5In1FaYLFtkcwLkX+7pveeu6ZU8rKiv6yBkIiafohsLta/qHvKRgIkQAIkQAJ2IRD9L6VdSHAcJEACJEACJEACURNAZN1p2YXqFOyUNo3i8UxHJgJE3YEgO7h8S4VhCLL79U7fu67pGfneQwfltqu3SKU6gi7W2jS6sURFAQoCi5Hi+6lGwK3/SzD0wMO/5mY4HEZUMHxeU3sR7XewezScTQwjovPXr5DzNNoP0XnRtgwHRL8M4/8bRh7hOoVHe1xuTwIkQAIkQAKJJkABMNHEeTwSIAESIAESsDkBOGFWFGZJaV6GQDhDCh9SaZPZrju7VgbVgOCZw32+bow5PVojsEluu2aLIQD43gjyxOV5WwXNSUN8CPI2F5FAyhEYgqGH/m8Pqtt2JMF+cACGK/ezGun3ZvuwTIfx5QCXXRh5bFfhryoMwX6xyYCJx2xqb4bka20/uvcuRozvkwAJkAAJ2IEABUA7zCLHQAIkQAIkQAImJJCu6XPrVuZJuV68H+sbX1ItsFgNCxf4n3rPOsNE4A0VHbytV9MOv/dQk6YDNyxa2L97ZNYQBGYAbCSQigScnmnD0AP/N5G4f3s0SvC19iFN7+2Xl1X8QwTuYg0puOep4Ie6fmtX5EYt0uVrijDSe2nisRh5vk8CJEACJGBXAjyDtevMclwkQAIkQAIkYBICuSqYwUQDKYIwCnF5Fr/4j0fX4Qb6xUs2yW337TcESe8xWvon5IdaE/Crl9ctmOKLQKUWFTK3VBVELUZ4j82/JGB2AnDiHdAoP0T7DU+6w47mndGwwKYTI/Ks1vV7/li/jDunFx1qbkaanKN1OxHtt7miIKp0XJh4IKUXgh+EP5p4LIqfK5AACZAACdicAAVAm08wh0cCJEACJEACZiEAp2BcjHdoKm3X8GREaYOxGgPMSm5Qoe+WPfuMOoXe/b7VMSw/fbpZrr9gvSyHchCiwRCka3jKiGqEYQAbCdiVwKRLo/3U/AbmHEiBD6dBLES0L0S/vergOzjhXnSzDI0UPkude2HmcXpNkSByeKnNkab1/FT0K4bop39Zs3OpJLkdCZAACZCAHQlQALTjrHJMJEACJEACJGBSAhDNVpXmSFlBprRq5B0iixLdUPD/xp2b5ebdb8mICnrehppkSDv86LmrvYuC/kW/8cjUOmI5GrGUk+6QnEz9q8+zVWBkPbGg2LjQAgTg3Ns/7jTSfCF2h9u6hiYN0e85Ff0gkC/WoJ2fpmLfdk3vfcfqEsnW/52lNph4wLUXNxcKsmjisVSO3I4ESIAESMD+BCgA2n+OOUISIAESIAESMB0BROLVVeQLjASQgotoo0Q2mJTcsLNebtd0YKdfSvJ9b3QZYsIVWysX7Q5qoOExKKeinBA8CBHQEAMhDmaoOKh/MV42EjArARji9GiNy34V5MN17oZ4v1cj/Z5V0Q9Rf+G0uvJ8I733XE3zLdAIvaU2CIZw5S7OTZd8Ff3YSIAESIAESIAEFidAAXBxRlyDBEiABEiABEggTgQQjXe6CgGIGurQKKJwxYdYdGe9GpR8QWsCfv/hgwFOpP/xl1YjEhAGBJE21AmcUDETD/+GyEcIgxAuIAjmqjCI5xmOpac7+u+fz0kgUgIw5ugbQ22/qbDq82H/EApfODZgOPge6BqRcBKDV5fkGJF++H9CGYClNph4ILUXwl80EYNLPT63IwESIAESIAGrE6AAaPUZZP9JgARIgARIwOIEkDJbVZQtK/IyDZMQmIUkqm2rLZK/U3fgnz51NOCQ//bkUSNCaUtVYcDypb5AaiXEEzz8W7rWLJsVBWcjBY3nKhSydpk/JT6PJQEYefSq6Nev4p9+LBdtcP59RZ17UdfvtbYhwWd5sVamQt92FfyQ4lurAuBSGtKEESWI1F6k+FIsXwpFbkMCJEACJEACpwhQADzFgs9IgARIgARIgASSSAAX+BvK8gTpuXDbjaQGWTTd3rFppZoVuOTuF9t8u/GoyPGDRw7JzVc1yOrSXN/yWD9xT78t7kmPjOjDv4EFIgW9qcRGxKAKg8uhirCRQIQE4Lzdq2YeSPOd0rT1xZpnZkZgjPPskX55sWUgIE0+1LYQ685bpw6+Kvrh/3gptTBh4oE6nBD8EB1Mo51QtLmcBEiABEiABCInQAEwcmbcggRIgARIgARIII4E8jId0lhdaKQmtg1MhO1AGk2Xrjm9Sga1ptkj+7t9u5l0T8sdDzXJbVc3RpW66NthBE8g2OAx5OeiivqCqCVoiIIn/6LGYJaakSxFbImgO1zVggTgyAsXXqT44nOE9PSF2oyucKh71BD9nj/WH5YAj7T2c9aWGJF+iJZdimAHsRtRfkjtLch28LO80CTxPRIgARIgARKIggAFwCjgcVMSIAESIAESIIH4ESjLz5LS3ExpH5wwagQuJmBE0xMIaB87b40MaXokapx5G4STOx46ILdctSXpZgMYP8xS5hqmIChwNo0YNQY1lRjioLoSZzpoPOKdx1T6O6XCdc+I04j4g4i8UINIeFxF9uc0vRcOvqgJuFhD2voZtcWyfUOp8XcpqbkQsY3UXhX+IPizkQAJkAAJkAAJxJ8Af3Hjz5hHIAESIAESIAESWCIBRBQhBRdiYEv/eEBE3BJ3GXIzpNd+9oINmo57QJpOjPrW6xyaku8/clC+cUWDKeuQoSTbuHP6pJHDKQEH6ZReR2JECnpFwvQ0Go/4JtcmT2b0QwAHX0T7zU0nDzbEbk0F9op+7YOTwVYJWIbo00aN8DtfRb+z15RoFGpklxDYHkKfEemnoh9dsQPw8gUJkAAJkAAJJIRAZL/eCekSD0ICJEACJEACJEACgQQgXm2uLDDSdCEEhlPHLHAP4b1CNNOXLquTW/fs08jDU8LIoe4x+fHjhw3X4KWkOYZ39NiuBUfl0WnPyVTOU8YqGQ4Ig7OmI94agxB0rDKu2FKy9t7G1VSmR01z+rS+32IO2kNa5/IvzQNGpN/hnrGwBo5afqjp906t7YeafJE0RKYWaj0/pPZi26VECkZyPK5LAiRAAiRAAiSwMAEKgAvz4bskQAIkQAIkQAImIlCs0UOFajbQpRFMHSrQheNIGmn3Eal04856+dbufTKgUVXe9pI6of7y2WPyN+9aa+k6ZS7P21pf0C1wg/VvqCUIIfCUKKgpxZpOzPqC/pSS/xyfeQh+SPOd6yo9t3cTLo9h4vGcmnm82Tm8aB1AbF+tjtznb5h18C0vyJq7ywVf08RjQTx8kwRIgARIgASSSoACYFLx8+AkQAIkQAIkQAKREkCqLkSKFXkZApOQ3tFTIl2k+wq1fmlepiECIhJwXOvuedufmnqMNMb3nVnjXWSbv4iqnHK7VPQ8NSREcfmMRwxX4lmBkCmcpxgl6tnIlBp6qOgHUXoh4Rt1/15rG5Jntabfq8cHBU7Ti7VSFda3a6QfhL9VJTkRib6ZKhzDtZcmHotR5vskQAIkQAIkkFwCFACTy59HJwESIAESIAESWCIBmFxsKMuXsgK3tPZNLBoNFelhalUIQTrwdx88ECCi3PNyu6CO3rbaIkP4yFXDDbtGyaG+4IQKoHj4N6QL+0cK5mhKMdK0mebpTyn65+7pGRW4NdpPH3PNX/z3DkFwn0b4oa4fTGzgYL1YQ6QrUntR129Teb4sR6G+MBs+8xD9EJFLE48woXE1EiABEiABEkgygWXq/rX4bcEkd5KHtx+B9vZ2qa2tNQbW1tYmNTX2i6Sw36xxRCRAAiRgXgI4nYFQAkfTcCKeIhnJ88f65UePHZZQJ0xwRTXEEERBqSBSrHXPIIx4BRLjtb6XClFzYDFrNuKXSqxpxA4aj4T9kcNnGenZEP0Q7RfqTB3rHe0dk2c1vXdvc/+8lO5gB8zUGpfvUBMP1PXbWlMojuXhGcJAG8zPmjXxwOc6FT7LwfhxGQmQAAlYlQCvv606c7HtNyMAY8uTeyMBEiABEiABEkgCAUTglWm9MghwMO84oTUCQwknkXbv3LWl8rHtbvnVcy1BN4XgCLEGj4UaIuZmhUIVCE9GT83+nTVKgGhYpPUNrSyWgYV70jPPiRZpohi/N1LQiB5UYRDp3GyzBKY0as8b7Yc03lCtfXDCEP2e0xTfxT5z2AeiNRGtCtHvjFXFYYt3mBqYdxTnzn5e6R4daka4nARIgARIgASsQYACoDXmib0kARIgARIgARIIgwDEszUrclUMzJQWTQuea3QRxi6CrnL5lgoZVBfVe1/rDPp+OAtnU2knpWPolLtwsO0KNNLKF0GoAkzJSQHmlGiYLgUqFEaSshnsOIlc5tT6gnhoRTrfYRFV5qsvqGKgIRCqCQnMSOyaUu0b/MknM5q6i88VhDx8VkOJ1jD9QHrvc0f6pFWjXBdrkFXhmr1d03vPXVMqefqZCqchghOiH4R0mO3QGTocalyHBEiABEiABKxBILyzAWuMhb0kARIgARIgARIgAYMA3GwbqgqMFMqW/nFDfIoWzYfeUWvUO4MQ06+CzMiUJ9pdBt0e+8WjtT+00JOm6lmhphr7Uo4RqWVEa51MQT4p4kBUM6uYBrELde3m1rZD5BnmbzaVeFYYxHPUfLRLgzsvov3wCJWyDtOP55sHVPjrk6YTo2ENfa2K3+evXyHnabQfPhvhNERnwsADojPEZ7N+XsIZC9chARIgARIgARIITYACYGg2fIcESIAESIAESMDiBCCCIK22c3hSOoemFnRPXWyoEEbee1qV8cC6MGgYmnAbEVyDWqsNkVyDeK3PB4zn+nrcHZYhw2LHnvv+tKpnqA+Hx0ItQyMivSmcp6IKZ1M6Z8XD2RRPM4lrMB4Zc3rmmbo4UF/QL1IQomCuPqySMg2jDgjHiPYbDSEeIw34pdZBTfHtkzfbhwXzvFir0NR3GHlsV+GvSt2xw2leEw98BnLVDISNBEiABEiABEjA/gT4i2//OeYISYAESIAESCClCaDOXE1xjqzMz5TjGlXXN7awaBYuLNREwz7xWKhB1PEXCCHazYqFswKh93moSLCF9r3Yey4VKbtHnMZjoXUhpM3We5uNIPSJg0Yk4axgiIjDcE0jFjrWUt/zaH3B0WnPSfHsVL1FOA/Ppg+nnTIgMVF9wVGN5IPo16+fO4iAc5tH5+h1Ffue1Ui/l1sGBXO2WCvSuYDgt10j/dZp1N9iUXtIt6aJx2JU+T4JkAAJkAAJ2JsABUB7zy9HRwIkQAIkQAIkcJIAotw2ludrfUC3pteOy7hzOiFsUOeuUiOz8AjV4OiK/hiRgz6B0G1E+A2djCaEcDi0QJ24UPsOZ/m4puKOuxauT4i6cvkaTVkCl2NfuvFsBCFee0VDCE2JrE8Iwww8EI3pbRC84HiLVGLDcEQFTuOvzsViYpl3H9H8RXQo6vb1qPiK2o9z24zOd1PXiIp+/QKX6XA+ixBpz1FDGkT7ba4oWNRABfX7IBQacwXxlk7Mc6eBr0mABEiABEggpQhQAEyp6eZgSYAESIAESIAEYG6wtbrQiMpqU0OFeETeRUoZohSMGvBYVZITcnOYRgxrRJkvzVhTjCEQnooqnE1JDpViGnLHYbyB2LURFSDxaFmkPiGEJ68gaIhQmmrqrTNXfPI9pPPGS4xD5uyUmo5MucHm1OBQX9BbWzAb4qD2Aa8h0saiDasI2TM6ZczH3GA/iLzghvTevc39xjqLHRMp3GetLjYi/U5XJ9/FnHhh4oFUb7DG55wuy4sR5vskQAIkQAIkkDoEKACmzlxzpCRAAiRAAiRAAicJQHgq19ppEKnaByc1RXYqpAOrmaBB0JmN6MqQdQt0bLY+4WxNQp84aEQW+kcVxq8+Yb8eC4+FGiL0MBZ/sXA2kjAwwhApvrFqEOUQbTcbcXeqf4iW848UzElXcTAzbVHBDf1yeqZ9hh4QHee2LnV9RqQfzDy6hqfmvj3vNUTK02qKDNHvHatLDIFy3kp+C+CabIit+lnO13p+8RJV/Q7JpyRAAiRAAiRAAhYkQAHQgpPGLpMACZAACZAACcSGACKq4JxapnX84BY8MhkfZ9/Y9Db8vczWJ8zS+oRZC24EB15vDUIYmPiLhUip9b72zA1nW3Cv4b3p1LTdEyq84rFQ8xpWzIqDszUKvSLorLFJuuGIHE19QtTmQ9Tk3MjJDAeMR2bTiE8JhEhxFsPwBdF+4ISIQ/8GbntPin7NfX4hiP4rzXlep+np2zW9952a5lug0XsLtTwV+mDuAuEPac5sJEACJEACJEACJLAYAZ4xLEaI75MACZAACZAACdieAJxQt1QVGnXbjmtasDNIJJcdISD9NTsje0H3WKSuwpV3VgwM7Xo8HK/6hEbE3qQRqRlqDlSPM1JevYKgIRCqODYrFJ5KR0aKdST1CV2et7W+oFswNv+GiMG5hh5g9MKxASPF94DW95ujCfpv7nuOdO/z1cjjPDX0WMhMBjUNC7JOjkOFPzO5NvsGwyckQAIkQAIkQAKmJkAB0NTTw86RAAmQAAmQAAkkksCKvExDNOrUtE084hD4lsjhxORYSCnNV/EJj9WloXcJQQxCmRFRaKQbz0lB1kg51C6EUBbrBrENBil4HFtg5xDuUIPQXyA0TE18YuGsqcli9Qm94h/Sf19pHdT03n55tW1onigYrCuINoV7L1x8axeo90gTj2D0uIwESIAESIAESGCpBCgALpUctyMBEiABEiABErAlAQgvEGYQkYVowP6xU7XibDngGA0K3JCSioesDL3TWcfeWXEQYqE3zRgpyBAIvcuQIhzrBuGuT+cTj4Watz6hUVsPguEcgRDOvkjxfal1wDAbWWhfeA8pvf9/e/cBN0Vx/3F8QFSwYQOsFFGxQTQKdhEbihUURWPF3qJG/tao0dgQJXYsQSwRuyiKvUTsYktAFAuCDRWxY0Pd/3wHZrN3t3vtuf585uXj3W2ZnX3Pcbf32ykbrmRn8LWBv5XbL5I4Tp+6HC+uGZWZxCMXKesRQAABBBBAoEABAoAFgrE5AggggAACCDQPAc0Mu6odl+2bRTXr7WyjoA+p6QKa1KO9nYBFf9mSxif80gYIfVBw7uO8FoZh4HBOXq3ush0nbl2+4xPG7euXqRVhz85LmI1XXtp1L1eANC6pG/bcGZLntrKM24ZlCCCAAAIIIIBAUwUIADZVkP0RQAABBBBAoKEF2toWYD3atHWTVWjG4F9/y2d0t4YmqcjJKTC2vB2fcPnF2yQeT+MTfme7FP8vSDg3QJg+XqEbnzAxl9KtaGWDfH/suISbzGOdFZcwSTMYL2rHIlSrQgX+dJ4kBBBAAAEEEECg3AIEAMstTP4IIIAAAgggUPcCGgdv2bZtjMYI1NiAP9lJQn63wSf92f/mPZ/7qKCUxg783f5Pj6TyCaheNDmG/jottXDigaLjE4ZdjmfPa00YGa+wmPEJNUGHJpBR996enZc0mlAmPanxn7oBz+1SvEBiYDB9P14jgAACCCCAAAKlEsi8QilVzuSDAAIIIIAAAgg0mMD887XMGmiKO925gcB5QcF5QUMXIFTw0A5z5wOJWhYGD+dtZx9ig4sKaBF8jNOOXxYdn7BrjvEJ3SQmYdfj/wUJ53ZHnvt6zm+/m5XaLeKCfhvYsf00bl96ajWfHc/PBv3U0k+Prex7h4QAAggggAACCFRLgABgteQ5LgIIIIAAAgg0C4GWtvlXSxM//lu5AdKDjz6g+FsTgo8KPCoA6fMKg5l2gXvugpPlPrPy5K8uux3s2IT6S0oK0qphZ0s1/UtL2l+zDKuln1olqu5JCCCAAAIIIIBALQgQAKyFWqAMCCCAAAIIIIBAGQRqMfg4t8Wj7yI9N2iYb8tHt29aq8m5LSErF3xUt+NoWI9JPMrwxiVLBBBAAAEEECi5AAHAkpOSIQIIIIAAAggggEAtBx99wHFuS8Z5wUPXgjFLy0atnxd8VOM/dftlEg/e5wgggAACCCBQLwIEAOulpignAggggAACCCCAQF4C1Qw+5lVANkIAAQQQQAABBCoswGjEFQbncAgggAACCCCAAAIIIIAAAggggAACCFRSgABgJbU5FgIIIIAAAggggAACCCCAAAIIIIAAAhUWIABYYXAOhwACCCCAAAIIIIAAAggggAACCCCAQCUFCABWUptjIYAAAggggAACCCCAAAIIIIAAAgggUGEBAoAVBudwCCCAAAIIIIAAAggggAACCCCAAAIIVFKAAGAltTkWAggggAACCCCAAAIIIIAAAggggAACFRYgAFhhcA6HAAIIIIAAAggggAACCCCAAAIIIIBAJQUIAFZSm2MhgAACCCCAAAIIIIAAAggggAACCCBQYQECgBUG53AIIIAAAggggAACCCCAAAIIIIAAAghUUoAAYCW1ORYCCCCAAAIIIIAAAggggAACCCCAAAIVFiAAWGFwDocAAggggAACCCCAAAIIIIAAAggggEAlBQgAVlKbYyGAAAIIIIAAAggggAACCCCAAAIIIFBhAQKAFQbncAgggAACCCCAAAIIIIAAAggggAACCFRSgABgJbU5FgIIIIAAAggggAACCCCAAAIIIIAAAhUWIABYYXAOhwACCCCAAAIIIIAAAggggAACCCCAQCUFCABWUptjIYAAAggggAACCCCAAAIIIIAAAgggUGEBAoAVBudwCCCAAAIIIIAAAggggAACCCCAAAIIVFKAAGAltTkWAggggAACCCCAAAIIIIAAAggggAACFRYgAFhhcA6HAAIIIIAAAggggAACCCCAAAIIIIBAJQUIAFZSm2MhgAACCCCAAAIIIIAAAggggAACCCBQYQECgBUG53AIIIAAAggggAACCCCAAAIIIIAAAghUUoAAYCW1ORYCCCCAAAIIIIAAAggggAACCCCAAAIVFiAAWGFwDocAAggggAACCCCAAAIIIIAAAggggEAlBQgAVlKbYyGAAAIIIIAAAggggAACCCCAAAIIIFBhAQKAFQbncAgggAACCCCAAAIIIIAAAggggAACCFRSgABgJbU5FgIIIIAAAggggAACCCCAAAIIIIAAAhUWIABYYXAOhwACCCCAAAIIIIAAAggggAACCCCAQCUFCABWUptjIYAAAggggAACCCCAAAIIIIAAAgggUGEBAoAVBudwCCCAAAIIIIAAAggggAACCCCAAAIIVFKAAGAltTkWAggggAACCCCAAAIIIIAAAggggAACFRZoVeHjcTgEnMCvv/4aSsyYMSN8zhMEEEAAAQQQQAABBBBAAAEEECidQPQ3d/S3eOmOQE71IEAAsB5qqQHLOHPmzPCsevXqFT7nCQIIIIAAAggggAACCCCAAAIIlEdAv8U7d+5cnszJtaYF6AJc09VD4RBAAAEEEEAAAQQQQAABBBBAAAEEEGiaQIvApqZlwd4IFC7w008/mYkTJ7od27VrZ1q1ojFq4YrV2UPNx32rzZdeesksu+yy1SkIR62aAO+BqtHXxIGp/5qohqoVgvqvGn1NHJj6r4lqqFohqP+q0dfMgXkP1ExVFFwQdfv1vfC6d+9uWrduXXAe7FD/AkRd6r8O6/IM9IHTs2fPuiw7hf6fgIJ/K6ywwv8W8KzZCfAeaHZVnnLC1H8KR7N7Qf03uypPOWHqP4Wj2b2g/ptdlWecMO+BDJKaX0C335qvorIXkC7AZSfmAAgggAACCCCAAAIIIIAAAggggAACCFRPgABg9ew5MgIIIIAAAggggAACCCCAAAIIIIAAAmUXIABYdmIOgAACCCCAAAIIIIAAAggggAACCCCAQPUECABWz54jI4AAAggggAACCCCAAAIIIIAAAgggUHYBAoBlJ+YACCCAAAIIIIAAAggggAACCCCAAAIIVE+AAGD17DkyAggggAACCCCAAAIIIIAAAggggAACZRcgAFh2Yg6AAAIIIIAAAggggAACCCCAAAIIIIBA9QRaBDZV7/AcGQEEEEAAAQQQQAABBBBAAAEEEEAAAQTKKUALwHLqkjcCCCCAAAIIIIAAAggggAACCCCAAAJVFiAAWOUK4PAIIIAAAggggAACCCCAAAIIIIAAAgiUU4AAYDl1yRsBBBBAAAEEEEAAAQQQQAABBBBAAIEqCxAArHIFcHgEEEAAAQQQQAABBBBAAAEEEEAAAQTKKUAAsJy65I0AAggggAACCCCAAAIIIIAAAggggECVBQgAVrkCODwCCCCAAAIIIIAAAggggAACCCCAAALlFCAAWE5d8kYAAQQQQAABBBBAAAEEEEAAAQQQQKDKAgQAq1wBHB4BBBBAAAEEEEAAAQQQQAABBBBAAIFyChAALKcueSOAAAIIIIAAAggggAACCCCAAAIIIFBlAQKAVa4ADo8AAggggAACCCCAAAIIIIAAAggggEA5BQgAllOXvBGoEYFx48aZv/3tb2b77bc3q6++ull66aXN/PPPb5ZYYgmz7rrrmuOPP95MmTIl79JOnz7d7bPaaquZhRde2Cy55JKmZ8+eZtiwYeaHH37IO5/nnnvO7L333qZTp06mdevWZpllljF9+/Y1t9xyS955aENtv80227j9lY/yU77PP/98Qfk08sbTpk0zl112mdl1113NKqusYhZaaCFnvsIKK5hddtnF3HrrrebXX3/Nm2DSpEnm0EMPNV27djVt2rQx7dq1M5tuuqm56qqrCsrnwQcfNP379zcqx4ILLuge9VrL800qt46r46scKo/KpfK98cYb+WbT0Nt9//33Zvz48ebCCy80u+++u+nSpYtp0aKF++vcuXPB5079F0zWEDuU6rO/ITCqfBKff/65uf/++83pp59utttuO/e97v9N77///gWXrpY+i7/44gt3Xj169DCLLbaY+9NzneusWbMKPrdG3OHll182Z511lrv28d+fiyyyiFl11VXNAQccYJ555pmCTpv6L4irqht/++237ppN1+69e/c2K6+8smnbtq1ZYIEFTPv27c3mm29uLrjggrz/rdTStbh+Q6js+k2h3xb6jaHfGjpXff+QEECgBAIBCQEEGlpgzpw5gf2oyPlnA4LBeeedl9Ni7Nixgb0gT8zPXnwG77zzTs58zjjjjKBly5aJ+dhgZfDjjz9mzcdeKAT9+vVLzEP528Bn1jyaw8q//vWvgf1hmOjk3x/2giuwF1g5Sa655prAXmgm5terV69g5syZWfP57bffggMPPDAxD5XpoIMOCrRdtqTjqNz+HNIfbVAxuPbaa7Nl0SzW2R8EiUY2YF6QAfVfEFfDbFyqz/6GAanyiaR/1kVf77fffnmXrtY+i1944YXA3gxM/LxadtllgxdffDHv82vEDe3NrkSf6Ptg3333DX7++eesBNR/Vp6aXPnoo4/mVf/2Zn/w0EMPZT2HWroW128He4M68dz02+O+++7Lej6sRACB3AIm9yZsgQAC9SygAKC9MxjsvPPOwbnnnhvYll7BU089FUyYMCG49957g+OOO86t9xeNI0aMSDzdV199NbCtq9yXs73THJxzzjmBvXMYPP7448HBBx8cfmkrCGjvUCbmY1trhdvallrByJEjg5deeim45557gj59+oTr9txzz8Q8tGLQoEHhttpP+ysf5ad8/TldffXVWfNp9JU+0GbvpAa2ZWQwatSowLYOCGwLguCmm25KCaDp4uu7775LJLGtScPAbYcOHYJLL73U/RizrQeCAQMGhOabbLJJYFvmJeZz0kknhduus846gW3F6epOj3rt6+7kk09OzEP56zh+Wx1f5dCPQ5XL3gl36xQIfuCBBxLzaQ4rbCuB0MneVQ9si9lA/4ZlV0gAkPpvDu+WzHMs1Wd/Zs4sKVbAf+7psWPHju7ftF9WSACwlj6LP/jgg8C24nafS61atQpOOOGEwLZcdn96rmU6R322f/jhh8XS1f1+/vpmueWWC4455pjgzjvvdN+fttdDMHz48GD55ZcPP+9zXUdR//X3dlAAcMUVVwwU4L3kkkuCu+++O1DdP/vss8Ftt90WDBw4MJhvvvnce0A3a19//fXYk6yla3H9ZtBvB/8Zpt8U+m2h3xj6reGvV2zvleC1116LPR8WIoBAfgIEAPNzYisE6logWyBGJzZ16tTAdgd2X7y6+E7a3t911kW4vpTTk222H355665iXLLdd8KAo360pLcU07F33HHHMJ8nn3wyLht3YeAvFLR9epmVr/LXNosvvnjw5ZdfxubTHBbqh9PQoUMTg7Kys91CQ/MzzzwzluWXX34JVlppJbed7sS+++67GdsdccQRYT6jbKAxLtnu5uEPufXWWy9QS85omj17dqDlqju915JalCrQ698DOm560n6+tartIhMoGN5ck4Lgo0ePTrFU4E9++QYAqf/m+u4JglJ89jdfvfKcue0O61rDfPrpp+4A77//fvh5mG8AsNY+i/fZZ5/wHG6//fYMOAU3/Gd+vueYkUkDLFAPCVmkX/f4U9P1TzSYopu+cYn6j1Op/WVJ9R4t+ZgxY8J/K3ZYlegq97zWrsVPO+20sLz6LZGeFNz0NwB0Q5OEAALFCxAALN6OPRFoKAE7Xlr45WvH98o4N7Wq8hfe2jYuqSuJHWPQbaegmwIG6UmBKJ+PWnvFJd3Z93cv1cU3Ltkxj1w+uiBIagmg/P2x4i4o4vJtrsvsmEtht97u3bvHMkR/fCV1F1fwzgeT11hjjdh8Dj/88LBedNc6Lmm5r7u44J728e81tWjTceOSyunziftBGbdPc1lWaACQ+m8u74zU8yzVZ39qrrwqtUAxAcBa+iyeMWNG2LrcjgWcyKN1+kxXy27tQ4oXUFdJ/9139NFHx25E/ceyNMzCbt26ufeAugKnp1q6FtdvBfVU0vtV13VJQ79Ef6eotw8JAQSKEyAAWJwbeyHQcAJDhgwJLxbVNTQ9qSumv5jUGD1JKRpwefjhhzM223DDDV0+apmVbWwaf5GvMdzSuxPrtR+Dbtttt804hl+g/H0LMB2XlF3At7pTF4u4pK5E/j2Q7YdX9CJNLQyi6ffffw/UbUn52IGdo6synvuLV3Vn0n7RpHx9WQ477LDoqpTnKqffLldXqJQdm8GLQgOA1H8zeFPEnGKpPvtjsmZRCQUKDQDW2mexWin7z2oNVZKUojf2mvvwHklGWm4nfgo9426kUv/Z9Bpjnb+mU/fZ9FRL1+L6reD/7Z9//vnpRQ1fR28MZxseJtyBJwggECvALMD2E4eEQHMXsJNtGDseoGOwd9XdLHLpJn5GOc3IpZmDk5Jtmh+usk32w+d6Yu/yGXvXzi2zFx9uxrKUDSIvfD42iGc021002fELXV5a5reLrvfPNSPaBhts4F5qH9sF1K/iMUZA1kq29WXMWhPOKmgDc27G5diN7MJonaS/B+yPVPPJJ5+4XaPbxeXl13/88cdGsxhHk38/apnfLrreP9fM0rYrlHuZXha/DY/5CXhz6j8/r0bZytd7Uz77G8Wikc6j1j6L/ftMxtk+06Pr+ExPfkf673NtEfedTv0n2zXCGnuT1Nix/9ypaBbdaKq1a/F8/+3bgKaxN6jdqfBvP1qjPEegMAECgIV5sTUCDSOgYJgdcNvYO+1mo402Mna8NHdugwcPNosuumjGeb755ptumR1Lzdhutxnr/YLohYbfx697++23jW3a715Gt/Pro4/R9en5TJ48Odw0ul24MPLEr7djpoTnGFnN03kCn3/+ufHOtgtGhottTWBsV2u33JtmbDRvQXS9z9NvW0zdad9S5KPy267Cvig8FiBA/ReA1WCb+n97TfnsbzCShjidWvss9uWxXQGz3mCyswAb27Lf1YF/bzZEhZT4JOy4f2GOcd/p3lsbRb+zw50iT6Lr082LySfuu9jnQ/1H4At8asdTdte5diIYF0TXda/Ssccem5JTrV2L+7pXIaPvtZRC2xf67aHvIaX096FbyP8QQCAvAQKAeTGxEQKNIaBWVC1atHB/ah1nuwAa260vvEtou92aiy66KONkf/rpJ2PHiHPLV1hhhYz10QV2/DejliJKPmDk13/00Uf+qcmVj53hLNy2XPmEB+CJGTZsmPEXi3ZCkAyRWqu7Yspj28Gb6H4ZJ8mCRIGoWy382y2mPNR/YvUmrijVZ3/iAVhRNYFi/g2psKX4Po77t+jLk+vzRWXw1wcCBP/qAAAjs0lEQVTpZdE6kjG2e6+xXSlDilr7Tqf+w6pp8pPrr78+vK7Xtbd6PBx//PHms88+c3nbWZ7NXnvtlXIc/29NC3P9e/P/1rRt+r+3Uuej8tvxw3WoxOTLYye6MdFWrok7sAIBBDIECABmkLAAgeYnYAcINnaAfzNu3LjwznpU4bvvvgtf2rFEwudJT3wAUK2GoqmQfHwe2r9c+UTL1pyf20H+zcUXX+wIdDFoBwbP4Ki1uitVeTJOlAWxAqXyrrV8Yk+WhaFAIfWlnfzndvpndpghT2pGoJC69fWqwqfXbanzaco1Rs3gVrkg//jHP8LhVgYMGBA7bEup602nnKvu8nkf5cpDx/H5pL8XtY40V2Dttdd27wE7LrcLEEZdGqHudT7Uf7RWeY5A/gLJ/fjyz4MtEUCgTgTsZApm4sSJrrRq7aWx1R566CEzcuRIYydSMO+9956xA+tmnI1agfikloO5kp24w22isQWjqZB8fB7av1z5RMvWXJ/rLvFuu+3mWv+pdegNN9wQjrESNam1uitVeaLnyPNkgVJ511o+yWfMGgkUUl/a3n9up39max2ptgQKqVtfrzqD9LotdT5NucaoLeHqlEZdf9XqS6l9+/ZmxIgRsQUpdb3pILnqLp/3Ua48dByfT/p7UeuaW9pll12MxsZTkoeu42+//XYzZswY18NHN3d32GGHFJZGqHt/viknxgsEEMhLgBaAeTGxEQLlF/Bdc5vyqK4A2dL8889v1lprLfenu4Pbb7+9ueyyy4yd1dfdITzllFOMxgBMT61btw4XafDgXMk3y2/Tpk3KpoXk4/NQBuXKJ6VwNfCiKXXv9831Hoiepu4C6z3gu3Goy9AWW2wR3SR8Xmt1V6ryhCdYA098HTblsZD6L+SUS+Vda/kUYtActy2kvuTjP7fTP7Obo12tn3MhdevrVeeUXrelzqcp1xi1bl7u8r3xxhumf//+7oae6uWOO+5wQcC445a63nSMXHWXz/soVx46js8n/b2odc0tqcusv67v2bOnGTRokLn77rvNjTfeaKZOnWp23nlnk35d0Ah1r3qm/pvbu53zLZUAAcBSSZIPAnUs0KNHD3P22We7Mxg1apR55JFHUs4mOilIPk3u/UQL6V05CsnH56GClCuflJNsZi90B1gXhq+88oo78yFDhpgTTjghUaHW6q5U5Uk8YVakCJTKu9bySTlJXmQIFFJf2tl/bqd/ZmdkzIKqCxRSt75eVej0ui11Pk25xqg6ahULoFl9t9lmG/PVV1+5WX81wdtmm22WWKJS15sOlKvu8nkf5cpDx/H5pL8XtY40V2CfffYxAwcOdONBHnXUUebLL78MaRqh7nUy1H9YpTxBoCABugAXxMXGCJRPoBQzWml2vGKTgkFHHHGE2/3OO+90F5I+L90tXGqppcysWbPC1mJ+XfqjLj79xZkfrNdvEx1s2Lc68+vSH6ODDefKx3d/SM9Dr7PlE7d9NZdV6j2g7t8aFPzJJ590p3vQQQe5SUCynbu6j/tUyrrzecY9Zqu79PeSxrFMSj4ftayL7pe0fbWWV6r+izk/6r8Ytfrfp1Sf/fUv0XhnEP0sLOVnerGfxSqPhqTIVRbVhP9MT782aLxayu+MPvnkE7PVVlsZPep77rrrrnM3+LLtTf1n02mMdbquV3dgXZNruB8/GUi56r7Ya3GVR2NRq5xff/111olA/L/9du3ahV3BG6O2OAsEKidAALBy1hwJgawCq622Wtb15V6pL1Ofpk+f7p+Gj2ussYZ5+umnzbvvvuu6l7RqFf/x8dZbb4X7rL766uFzPdHsZPPNN5/57bffTHS7lI3mvYiuT89HZfEpup1fFn3061XeVVZZJbqq5p5X4j2g2QF1Z/i+++5z57/HHnuYq6++OqeF7hjrx5Yuvrxp0k7R9aWoOx0nVz7q0p6UfHlUfj94eNK21Vxeifov9vyo/2Ll6n+/Unz2179C451BMd+jUijXZ7HKoxbp33zzjfn000/NMsssE4s+Y8YM8+2337p16WWJ3aHBF37xxRdm6623dt09daoa1mXffffNedbUf06iut8g6bq+1q7F9V686667nLeu1zbYYINYe9281hiHSvzbjyViIQJ5CdAFOC8mNkKg8QU0IYhPcc3qN9lkE7dad+h8t1G/ffRRA1D7tPHGG/un7lGDO/fq1cs9f/7557OOF+Pz0WDP6XcVNc6JHyjab5dyoHkvNJaMxjdU0j4aA7G5p0MPPdSoa5DSjjvuaP71r3+Zli3z+yrw74EpU6a4H2hJltE6SX8PdOnSxSy33HJu1+h2cXmNHz/eLVbrs86dO6ds4suihdny0Q/Jt99+2+2bXpaUDHmRU8CbU/85qRpqA1/vTfnsbyiQBjmZWvss9u8z8Wb7TI+ua+6f6QqW9u3b10yePNm9KzWO75FHHpnXO5T6z4uprjdKuq6vtWvxfP/tv/zyy2EPo+b+b7+u35gUvuoC+f3qq3oxKQACCJRbQINF+9S9e3f/NHzUTGM+aZzAuKTWZRp4WEkDE/fp0ydjM5+P7uBroOK4pC5Ajz32mFu15ZZbmuh4JVqo11qupO2Sugwpf99SQANjN/f0l7/8xfzzn/90DPJTnSe15Iyz8nWndemDSvvtf/jhB9flRK91V1d3mqNJ3ZPULUVJd3p9gDa6jZ5ruW+5p+21XzQpX38HWF1cdNy4FC0n74E4ofyXUf/5WzXSltF6b8pnfyOZNMK51Npn8U477RTejEp6n8ndf6brxpX2aa5J33maxOvVV191BKeeeqo58cQT8+ag/vOmqtsNs13X+8/1WrgW33zzzU3btm2d8w033GCCIIg19//2tZLruVgiFiKQn4D9R0ZCAIEGFhgzZkxgx4XJeob2jnpgW/3pGzewAaHABl5it990003DbZ577rmMbS644AK3XvmcccYZGeu1wI4jGNgverddp06dAtt9JWU728Q/sC3TwnzsOHUp6/2Lxx9/PNzG/ggItF80zZw5M+jYsaPbxgYjAzsAcnR1s3uu+lC96G+jjTYK7EDbBRvYFpXBSiut5PJYbLHFAtsdPCMPO45keBz7Iy5jvRbYFmSB7QrutrOtOwP7QyZlO73Wcv9+tC34Utb7FyNHjgyPZVs9+MXho8qnciqflVdeOZgzZ064jidBoH9/stFjPon6z0epMbcpxWd/Y8rUzlnZSSDCz8P99tsvr4LV2mexHZ4iPAcbvMg4B3uzJ1yf7zlmZNIAC+wsuIGd8CO0OOaYY4o6K+q/KLaq76Rrqx9//DFrOYYPHx6+P2xrz4xr5Fq7Fj/ttNPC8uq3RHrSbw79PtE1S+/evdNX8xoBBAoQUJSdhAACDSygi2Tb3D+wd8uCyy+/PFBA7bXXXgtsC6vg5ptvDgYNGhTYO+nhF+9ZZ52VqGHvNAdt2rRx2ypgeO655wa2K2/wxBNPBIccckiYh22dFdi7ion5XHXVVeG2Xbt2DeyA1cGECROCe++9N7CtBsN1e+65Z2IeWqGy62JAf9pP+ysf5ad8/To7xl3WfBp95aWXXhpa2O60wTPPPBNMnDgx65+CPXFp3Lhx4fulQ4cOgR1vKLCDNwd2gOlg1113DY9ju3RkXHBG8zvppJPCbddZZ53Adkt2dadHvfZ1d/LJJ0d3S3muoK/tBhJuq+OrHCqPytW+fXu3Tu/vBx54IGXf5vbinXfeCfSjIfpnJ/ZxPnqMLtdzO85WLBH1H8vS8AtL9dnf8FAVPEE7Jm/Kv9thw4aFn4X6XEz/N51UtFr6LP7ggw8CO26ZOw/92Lct2gKdp/703AcAtI0djzbplBp++YABA8K63mKLLYL//ve/Wb/PFehLStR/kkztLtdNuyWXXDI4+OCDA9tizl3Tvf766+7fyZVXXplyXaTr/0cffTT2ZGrpWly/GfTbwV/76TeFflvoN4Z+a/hGCvoNot8wJAQQKF6AAGDxduyJQF0IKADov1CzPepL9aKLLsp5TmPHjg1bVcXlpy9wBRtypdNPPz2wXVASy9avX7+cdzjVUkzbxZVDyxT4SWqJmKt8jbRed0uTjJKWqzVJUrrmmmtcUDlpXzvOY6AWmNmSnQgmGDx4cNZyHXjggYG2y5Z0HDu+Y2I+dgzJ4Nprr82WRbNYp2BAUn3FLdeNgqRE/SfJNPbyUn32N7ZS5c4u3+92/+87qWS19lmsm5N2ApDEzyut0zbNOfk6zfcxWytv6r/+3kmqz3zq3s6uGzzyyCNZT7CWrsX128FO1pd4burRYSewy3o+rEQAgdwCBABzG7EFAnUt8Nlnn7kWcfvvv7/rVmlnQg0UFFHAT63B1I3EDhyds5twFGHatGnBcccd5+7WLbTQQoG62KrL5tChQwM7UHx006zPn3322WCvvfYKVCbdpVSLLTubXTB69Ois+6WvVEtG7af9lY/yU75x3ZTT920Or0sdAJSZWhDq7rO6BLdu3TpQKzK1+hsxYkRBXW3VosyO8RfYiUFc3elRrwtpsaeuvbrrreOrHCqPyqXyTZo0qTlUcc5zLGUAkPrPyd2wG5Tqs79hgSp4YqUKAPoi19JnsW7s/PWvfw3WWmst1/JHrX/s2MRuWfqwIb78zekxn+BPdJtsAUDvRv17idp/1DA9umGvlqA9evQI1BtDrWPt+Niu94t6Q+g7P9/r8Vq6FtfwNPotod8U+m2h3xjdunVzvzn0/UNCAIGmC7RQFvZLgoQAAggggAACCCCAAAIIIIAAAggggAACDSjALMANWKmcEgIIIIAAAggggAACCCCAAAIIIIAAAl6AAKCX4BEBBBBAAAEEEEAAAQQQQAABBBBAAIEGFCAA2ICVyikhgAACCCCAAAIIIIAAAggggAACCCDgBQgAegkeEUAAAQQQQAABBBBAAAEEEEAAAQQQaEABAoANWKmcEgIIIIAAAggggAACCCCAAAIIIIAAAl6AAKCX4BEBBBBAAAEEEEAAAQQQQAABBBBAAIEGFCAA2ICVyikhgAACCCCAAAIIIIAAAggggAACCCDgBQgAegkeEUAAAQQQQAABBBBAAAEEEEAAAQQQaEABAoANWKmcEgIIIIAAAggggAACCCCAAAIIIIAAAl6AAKCX4BEBBBBAAAEEEEAAAQQQQAABBBBAAIEGFCAA2ICVyikhgAACCCCAAAIIIIAAAggggAACCCDgBQgAegkeEUAAAQQQQAABBBBAAAEEEEAAAQQQaEABAoANWKmcEgIIIIAAAggggAACCCCAAAIIIIAAAl6AAKCX4BEBBBBAAAEEEEAAAQQQQAABBBBAAIEGFCAA2ICVyikhgAACCCCAAAIIIIAAAggggAACCCDgBQgAegkeEUAAAQQQQAABBBBAAAEEEEAAAQQQaEABAoANWKmcEgIIIIAAAgg0XaBz586mRYsWZv/99296ZlXOYdasWWbJJZd05zNhwoSSleb66693ecpp2rRpJcu3kTMKgsB0797duY0aNaqRT5VzQwABBBBAAIEaEiAAWEOVQVEQQAABBBBAAIFyCJx++unmq6++Mv369TM9e/YsxyHIM08BBUtPPfVUt7UeZ8+eneeebIYAAggggAACCBQvQACweDv2RAABBBBAAAEEal5g+vTp5tprr3XlVCCQVH2B3Xff3XTr1s3MmDHDXHHFFdUvECVAAAEEEEAAgYYXIADY8FXMCSKAAAIIIIBAMQLq0qrumurmWs9p6NChZs6cOWbjjTc266+/fj2fSsOUvWXLlua4445z53PhhRean376qWHOjRNBAAEEEEAAgdoUIABYm/VCqRBAAAEEEEAAgSYLfP311+bGG290+ey9995Nzo8MSicwcOBAM//885uZM2eaW2+9tXQZkxMCCCCAAAIIIBAjQAAwBoVFCCCAAAIIIIBAIwgosKQx5hRoUsCJVDsCmpRl2223dQUaOXJk7RSMkiCAAAIIIIBAQwoQAGzIauWkEEAAAQQQQMALfPLJJ+akk04yf/zjH03btm1dMKxDhw5uJtY999zTdfH99ttv/ebhY9IswOoarIkc8v3bfPPNwzzTnzz55JNmv/32MyuttJJZaKGFzGKLLebK9X//939G5W5quv32210WKsNSSy2VNbsnnnjCyKNLly6mTZs2rjydOnUyG2ywgRkyZIjR+lzp999/N9dcc43ZaKONzBJLLGEWXnhh06NHD3POOeeYH374IXF37af8dRx1VV566aVdPS2++OJm7bXXdss/+OCDxP21QueoOtGj0jvvvGOOOuoos8oqq7hz0bq4mYrfffdd1x1XM/Pq/aFzV31o9ueXX37Z5ZX0P3XdvfTSS90x27Vr58qswJ7G99tuu+3M8OHDY4/p89t1113d02effdZ8+OGHfjGPCCCAAAIIIIBA6QXs2DYkBBBAAAEEEECgIQXGjx8f2KBaYK+gsv7dd999Gedvg19uHxugS1n3/vvvZ80r/Vi9e/dO2V8vfvzxx2DQoEFZ87HBs2Ds2LEZ++a7wAanggUXXNAd47TTTsu627HHHpu1LDonG0DMyGPUqFHhfm+88Uaw5ZZbhq/THXr16hV8//33GXlowRlnnJG4n8/HBkiDu+++O3Z/LZSzttXjPffcE8jP7+sfVXfRNGzYsMC2jszYzm9vg4ZBkp0N0AZrrLFG4r4+j+OPPz56yJTnb731Vri/DZymrOMFAggggAACCCBQSoFW9uKEhAACCCCAAAIINJzAzz//bGyQzah136KLLmoOP/xw06dPH9O+fXvzyy+/GBsMMs8995wZM2ZMQee+/PLLm4kTJ2bdRy3v/v73v7tt1IoumuyFnNltt93MuHHj3OIdd9zRaFZYtTrT5BAvvfSSueiii4xavGk7tQ5bb731olnk9XzChAlGBko9e/ZM3Of+++83F198sVuv1npyWn311V1rOI0haAN75rHHHnPlSszErjj44IPNCy+84Fo06nyWWWYZdw4XXHCBef75593+Z599tjnvvPMysvn111/Nsssua/r372823HBDZ9G6dWvXKk51dOWVVxobPDR77bWXefXVV135MjKZt0BuGu9QLSpt8M5suummZr755jPyWGSRRcLdbPDPnHDCCe61P2+1FlSrwylTppjLL7/clVv1qBaJf/7zn8N99eToo482kydPdst0vAEDBpjlllvOHUuz+6r14L333puyT/qLVVdd1R1Pzk899ZQzTN+G1wgggAACCCCAQEkEShlNJC8EEEAAAQQQQKBWBB5//PGwdVVcCz9fTjtDbvDNN9/4l+FjUgvAcIOEJzbQFNhupO7YNpCWkbdaetmLONfy7MEHH4zN5csvvwzWXHNNt53tEhu7Ta6Fdvbf8Pxt99LEzffZZx+3nc73u+++S9xu1qxZGeuiLQB1TjfddFPGNmqJuNZaa7ljqBWhvNOTWubZoGz64vC1ym8Dry4PG2wLl0ef+BaAKocNxAXTp0+Prk55rtaKvuWfWh/aLsgp6/Xit99+C3Qs5WcDh4HqxCe14PT7Z2vhp+3j3Hw+erRBaXeM1VZbLbqY5wgggAACCCCAQEkFWtqLGhICCCCAAAIIINBwAp9++ml4Tptttln4PP1Jq1at3Nh76cuLea1x+3beeWdjA0RGY8HZwGNK3vYqztjAnMtaLcr8JBDpx9L4eWqhpqQWgBrPrtD00Ucfhbuo1WNS8k4aIzHaQi59e51PtqQWcGoJl55sN2Q3Fp+W22BY2Gouup3GW9REJUlphRVWMBoXUcl2izZyzJbOP/9807Fjx8RN1MLSBiJdy0obAHRjB6ZvrNaYl112mVH51frwzjvvDDexwUC3vxZke29pfS43XzdqkZrrvJQfCQEEEEAAAQQQKEaAAGAxauyDAAIIIIAAAjUvoC6lPtmWav5p2R4V9Ntll13c5B0KKipg1LVr15Tjqcvoe++955ape2+2FA0sqQttoWnmzJluF3WFXWCBBRJ39052vMSwbIkbZ1nxpz/9KXHtuuuuG66bOnVq+DzpibptKyCm7seTJk1yfzoPJb8uaV+da64ZjxWYVdIkHJocJCmpO7AmB1GK1oEmVPGmttWjURfmYpMPEKq7troCkxBAAAEEEEAAgXIIEAAshyp5IoAAAggggEDVBTbZZBM3lpwKYie5MHYSCjf+nFrUaQzAUqfBgwe7ceaUr2aG1XiD6Sk6q6zGulPwKekv2hrPt9JLzy/ba7VSU1Jrwmxp3333davVOs921XXjJipgqtlxC0m2C2vi5j7IpQ1sN+PY7WyXXTeunloDajZejYmo8igAp79DDjkk3O+LL74In6c/0Th+Gj8wKek4Pjh68sknJ/r7evF1Fq0DtQrcY4893CEU6F155ZXdeIIPPPBAwUG8aP3Mnj07qdgsRwABBBBAAAEEmiRAALBJfOyMAAIIIIAAArUqoC6laumlCS2UNAnEKaecYhQYVMsudb8dPXq0sWO9NfkUNFHErbfe6vI54ogj3EQacZl+/vnncYtzLvvhhx9ybpO+gQ+CqWVitmRn7nUTXthxC40dr8/cdtttRsFMBdLU9fawww4z//nPf7Jl4db5FnpxG6o7rU9x3nYsRGNn1HXlUIAuV8p2TtGAWlw+paoDTRKiCVyUVGZ12d5+++2NWgdq0hW9tmNLxhUhZVn0XLJ1g07ZiRcIIIAAAggggECBAswCXCAYmyOAAAIIIIBA/QgoqKQZexUI1J+6uaplm4IuDz/8sPsbPny4UcstPxZboWd31113GY0jp6Rg2iWXXJKYRTT4pfKotVs+qZiytWvXzmWtbqUaWy5bV9cjjzzSdZtVQPTRRx914w4qePXxxx+bq6++2tiJS1zwVLP4ljqpNZ9m91WQU60ehwwZYvr27eu6T6sloO9q+8QTTzhfHT/bWHma8TdbitbB6aefnrO7sM9r4YUX9k/d42KLLebGI9SszZr1+d///rd5/fXXXUBZrQb1d+GFF5p77rnHzWycsnPkhW+pqUU6XxICCCCAAAIIIFAOAQKA5VAlTwQQQAABBBCoGQEFhDQ2n/6UZsyYYR566CFzxRVXmFdeecX9HXrooWbMmDEFl/m1114z6kKrgJS6gSoQpPH/kpJah/mkVojq4lqu5AOAdoZb1xJNx8uWFGRUV2n9aR8Fs2Silm4KIp5zzjmuZZsmOSllUhdaP/adjrfVVlvFZh8NlMVukOfCaB2oxV1T60Bdy/WnpO7NCgRef/315u677zZqbahxBjXuo1pYxqWvvvrKLZa/b7UZtx3LEEAAAQQQQACBpgj8rz9GU3JhXwQQQAABBBBAoE4ENOnFAQcc4CZ10My3Svfff79rFVjIKWhMOAXD1HJNLbfUoi861l1cXuuss064WGMRljP5ySt0jLfffrugQ6nLrmzUtfnxxx8P91WAs9RJE30oyS4p+Kf1fiw+PW9K0tiCvqVdqetg0UUXdd2C1SpUszwrKeD8zDPPJBbZ182aa66ZuA0rEEAAAQQQQACBpgoQAGyqIPsjgAACCCCAQF0KqPVX7969Xdk1i6tvhZbPyWisPLUo/PDDD41aGGr8v2yTYPg8FVTTuHpK6larfMqVNt100zBrjX9YbFKZ/bh62SbfKDZ/P4OuLNTyMC4pyKrZdkuRVF/9+vVzWT3yyCPmzTffLEW2GXmoO7hPSW6a0XjKlClus/XXX99vziMCCCCAAAIIIFByAQKAJSclQwQQQAABBBCoBYGnn34660y2mgn4qaeeckXV2HO+y2w+ZT/ooIPMiy++6DbVZA+aUCSfpJZ1mohEaerUqa778M8//5y4qwJE6oJbTFpxxRVNp06d3K4apy4padKP6EQU6dup5Z3vptqlS5f01U1+rclGlBTki2thqDH75P3JJ580+Vg+A83+q0CgAo677bab+eijj/yqjEcd/+abb07ZRnXn3zsZO8xboOCiT0lusvXjGW6zzTZ+cx4RQAABBBBAAIGSCyQPUlPyQ5EhAggggAACCCBQOQF1XVUXVrWE0+ysPXr0cEE+BbvU7fKqq64yr776qivQgQcemHXsvmipr7vuOhcQ0rItttjCbL311mbSpEnRTVKea/KIaABIs+pqog2Nd3fHHXe4MmgMQo0jp66pCvq99dZbbiy5sWPHunHhjjrqqJQ8832hLsqXXnqpefLJJxMnAjnxxBPdTL/adrPNNjOrrrqqUZlnzZrluq5edtll7nAKmCkQV+q0++67u6CoAqHqmq2xB2UqC3UP1vE1VuPGG2/sJicpxfHVPVoTdBx33HFm8uTJbhzAQw45xNVnhw4dXMvMadOmuW7iGqNQ3Xg1mYxvvfnBBx+YPn36uJmL+/fvb9Zbbz2z/PLLu6KpVaiCqj6Yufbaa5uk1n2+e/XSSy/tZqcuxbmRBwIIIIAAAgggECdAADBOhWUIIIAAAggg0BACauGlllrZWmsp8HXeeeflfb4K/vikmWmjY+355dFHdTPWxBA+aTZeBYiOOeYYF4TUBBEnnHCCX53xWMwMwD6Tgw8+2AUAFZRSi0gF+OKSuj/fcMMN7i9u/YILLujKqkBXqZOCaiNGjHDBRXUDHjp0qPuLHmePPfYwOpdsYwRGt8/nuSY7UaBTj5rxWC059ReXNBNx3AQdCh7qLympW7gmA0magfmWW25xu+r81CWdhAACCCCAAAIIlEuAAGC5ZMkXAQQQQAABBKoqMGTIENfq77HHHjOarVddSDUrq9IyyyzjWtxpBl+1Dqx0UrDnyiuvNIcffri59tprXYBQgcXvv//eqDuyWgyuu+66ZrvttjM77LBD0cXTDLcbbriha8k2evTo2ACgWgdqApPx48e7lpGa3ERdfhdaaCHTtWtXo7HsVE5NnlGupJZ/3bp1cwE4TcyhgKRaxf3hD39wrQLVSjAaRC1VORRU3GmnnczVV19t1GVX4/Hp2Ap4qkWfgrtqjaiZfFUen9SqVOV5+OGHzQsvvODGgvzss89cy0FNZqJyDxgwwOy///4uL79f9PH5558377//vlskXxICCCCAAAIIIFBOgRZ23JGgnAcgbwQQQAABBBBAAIHqCagrqlqYaSIPBRkVYCRVX0DdqUeOHGn69u1rHnrooeoXiBIggAACCCCAQEMLMAlIQ1cvJ4cAAggggAACzV1g4MCBrjWhWvUVO6FIczcs9fkrEHvjjTe6bM8888xSZ09+CCCAAAIIIIBAhgABwAwSFiCAAAIIIIAAAo0joPHnNK6e0vDhw83s2bMb5+Tq9Ew05uScOXOMgrNJE4TU6alRbAQQQAABBBCoUQG6ANdoxVAsBBBAAAEEEECglAKaTVcz+2o8vTXWWKOUWZNXAQIafUcBWU14MnjwYNOxY8cC9mZTBBBAAAEEEECgOAECgMW5sRcCCCCAAAIIIIAAAggggAACCCCAAAJ1IUAX4LqoJgqJAAIIIIAAAggggAACCCCAAAIIIIBAcQIEAItzYy8EEEAAAQQQQAABBBBAAAEEEEAAAQTqQoAAYF1UE4VEAAEEEEAAAQQQQAABBBBAAAEEEECgOAECgMW5sRcCCCCAAAIIIIAAAggggAACCCCAAAJ1IUAAsC6qiUIigAACCCCAAAIIIIAAAggggAACCCBQnAABwOLc2AsBBBBAAAEEEEAAAQQQQAABBBBAAIG6ECAAWBfVRCERQAABBBBAAAEEEEAAAQQQQAABBBAoToAAYHFu7IUAAggggAACCCCAAAIIIIAAAggggEBdCBAArItqopAIIIAAAggggAACCCCAAAIIIIAAAggUJ0AAsDg39kIAAQQQQAABBBBAAAEEEEAAAQQQQKAuBAgA1kU1UUgEEEAAAQQQQAABBBBAAAEEEEAAAQSKEyAAWJwbeyGAAAIIIIAAAggggAACCCCAAAIIIFAXAgQA66KaKCQCCCCAAAIIIIAAAggggAACCCCAAALFCRAALM6NvRBAAAEEEEAAAQQQQAABBBBAAAEEEKgLAQKAdVFNFBIBBBBAAAEEEEAAAQQQQAABBBBAAIHiBAgAFufGXggggAACCCCAAAIIIIAAAggggAACCNSFAAHAuqgmCokAAggggAACCCCAAAIIIIAAAggggEBxAgQAi3NjLwQQQAABBBBAAAEEEEAAAQQQQAABBOpCgABgXVQThUQAAQQQQAABBBBAAAEEEEAAAQQQQKA4AQKAxbmxFwIIIIAAAggggAACCCCAAAIIIIAAAnUh8P/tjQO4JMZp0AAAAABJRU5ErkJggg==\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div id='79cd5743-2a0d-4666-b540-d155340c048f'></div>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div id='9a53d1ca-baa5-4250-be04-a732c455b9a8'></div>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAAXNSR0IArs4c6QAAQABJREFUeAHs3QmYHFW58PF3MpNZksmeAMlkB9Gwh0AIRNlkkT2somyC6KeiF3dRXK7oVbkKLlz1KqvsAiqbsqqXPYQQ9hAIJJnMZCbLZJbMPtMz/Z23tIqqnu6e7p7uqu7q/3ke7K7qqnNO/U5P4rx5zzklUVOEggACCCCAAAIIIIAAAggggAACCCCAAAKhFBgVyqfioRBAAAEEEEAAAQQQQAABBBBAAAEEEEDAEiAAyBcBAQQQQAABBBBAAAEEEEAAAQQQQACBEAsQAAzx4PJoCCCAAAIIIIAAAggggAACCCCAAAIIEADkO4AAAggggAACCCCAAAIIIIAAAggggECIBQgAhnhweTQEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIdQAABBBBAAAEEEEAAAQQQQAABBBBAIMQCBABDPLg8GgIIIIAAAggggAACCCCAAAIIIIAAAgQA+Q4ggAACCCCAAAIIIIAAAggggAACCCAQYgECgCEeXB4NAQQQQAABBBBAAAEEEEAAAQQQQAABAoB8BxBAAAEEEEAAAQQQQAABBBBAAAEEEAixAAHAEA8uj4YAAggggAACCCCAAAIIIIAAAggggAABQL4DCCCAAAIIIIAAAggggAACCCCAAAIIhFiAAGCIB5dHQwABBBBAAAEEEEAAAQQQQAABBBBAgAAg3wEEEEAAAQQQQAABBBBAAAEEEEAAAQRCLEAAMMSDy6MhgAACCCCAAAIIIIAAAggggAACCCBAAJDvAAIIIIAAAggggAACCCCAAAIIIIAAAiEWIAAY4sHl0RBAAAEEEEAAAQQQQAABBBBAAAEEECAAyHcAAQQQQAABBBBAAAEEEEAAAQQQQACBEAsQAAzx4PJoCCCAAAIIIIAAAggggAACCCCAAAIIEADkO4AAAggggAACCCCAAAIIIIAAAggggECIBQgAhnhweTQEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIdQAABBBBAAAEEEEAAAQQQQAABBBBAIMQCBABDPLg8GgIIIIAAAggggAACCCCAAAIIIIAAAgQA+Q4ggAACCCCAAAIIIIAAAggggAACCCAQYgECgCEeXB4NAQQQQAABBBBAAAEEEEAAAQQQQAABAoB8BxBAAAEEEEAAAQQQQAABBBBAAAEEEAixAAHAEA8uj4YAAggggAACCCCAAAIIIIAAAggggAABQL4DCCCAAAIIIIAAAggggAACCCCAAAIIhFiAAGCIB5dHQwABBBBAAAEEEEAAAQQQQAABBBBAgAAg3wEEEEAAAQQQQAABBBBAAAEEEEAAAQRCLEAAMMSDy6MhgAACCCCAAAIIIIAAAggggAACCCBAAJDvAAIIIIAAAggggAACCCCAAAIIIIAAAiEWIAAY4sHl0RBAAAEEEEAAAQQQQAABBBBAAAEEECAAyHcAAQQQQAABBBBAAAEEEEAAAQQQQACBEAsQAAzx4PJoCCCAAAIIIIAAAggggAACCCCAAAIIEADkO4AAAggggAACCCCAAAIIIIAAAggggECIBQgAhnhweTQEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIdQAABBBBAAAEEEEAAAQQQQAABBBBAIMQCBABDPLg8GgIIIIAAAggggAACCCCAAAIIIIAAAgQA+Q4ggAACCCCAAAIIIIAAAggggAACCCAQYgECgCEeXB4NAQQQQAABBBBAAAEEEEAAAQQQQAABAoB8BxBAAAEEEEAAAQQQQAABBBBAAAEEEAixAAHAEA8uj4YAAggggAACCCCAAAIIIIAAAggggAABQL4DCCCAAAIIIIAAAggggAACCCCAAAIIhFiAAGCIB5dHQwABBBBAAAEEEEAAAQQQQAABBBBAgAAg3wEEEEAAAQQQQAABBBBAAAEEEEAAAQRCLEAAMMSDy6MhgAACCCCAAAIIIIAAAggggAACCCBAAJDvAAIIIIAAAggggAACCCCAAAIIIIAAAiEWIAAY4sHl0RBAAAEEEEAAAQQQQAABBBBAAAEEECAAyHcAAQQQQAABBBBAAAEEEEAAAQQQQACBEAsQAAzx4PJoCCCAAAIIIIAAAggggAACCCCAAAIIEADkO4AAAggggAACCCCAAAIIIIAAAggggECIBQgAhnhweTQEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIdQAABBBBAAAEEEEAAAQQQQAABBBBAIMQCBABDPLg8GgIIIIAAAggggAACCCCAAAIIIIAAAgQA+Q4ggAACCCCAAAIIIIAAAggggAACCCAQYgECgCEeXB4NAQQQQAABBBBAAAEEEEAAAQQQQAABAoB8BxBAAAEEEEAAAQQQQAABBBBAAAEEEAixAAHAEA8uj4YAAggggAACCCCAAAIIIIAAAggggAABQL4DCCCAAAIIIIAAAggggAACCCCAAAIIhFiAAGCIB5dHQwABBBBAAAEEEEAAAQQQQAABBBBAgAAg3wEEEEAAAQQQQAABBBBAAAEEEEAAAQRCLEAAMMSDy6MhgAACCCCAAAIIIIAAAggggAACCCBAAJDvAAIIIIAAAggggAACCCCAAAIIIIAAAiEWIAAY4sHl0RBAAAEEEEAAAQQQQAABBBBAAAEEECAAyHcAAQQQQAABBBBAAAEEEEAAAQQQQACBEAsQAAzx4PJoCCCAAAIIIIAAAggggAACCCCAAAIIEADkO4AAAggggAACCCCAAAIIIIAAAggggECIBQgAhnhweTQEEEAAAQQQQAABBBBAAAEEEEAAAQQIAPIdQAABBBBAAAEEEEAAAQQQQAABBBBAIMQCBABDPLg8GgIIIIAAAggggAACCCCAAAIIIIAAAgQA+Q4ggAACCCCAAAIIIIAAAggggAACCCAQYgECgCEeXB4NAQQQQAABBIYKLFu2TEpKSmT69OnS2dk59IKQnNFn/MQnPpH201x22WWWT2Vlpbz77rtp388NCCCAAAIIIIAAAvknQAAw/8aEHiGAAAIIFImABp/+/Oc/y+c//3k54IADZPbs2TJ27FjRwMsuu+wi++67r5x//vlyzTXXyMaNG1NSmTt3rhW80eBP7H9jxoyxgl7vf//75YgjjpCvfvWrcscdd8jWrVtTqlsvam9vl1mzZjl1f/azn03rXn1Gu1+f+cxnhtz7f//3f87nel22y8MPPyz33XefVe1//ud/Wt7x2mhoaJC77rpLvv71r8txxx0nH/jAB2TChAlSVlYm1dXVMmfOHDnppJPkV7/6lWzfvj1eFUPO7dixQ7T973//+3LGGWdY4ztlyhQpLy93xvzwww+Xb3/727J27doh9/t1QgOAkyZNkt7eXvnSl76U02aj0aj85S9/sTx23XVXqaqqkmnTplk/D+qU6ve+v79fnnvuObn66qvlggsukIMOOsj6rmt9o0ePlqlTp1p1fu5zn5Onnnoq5WcaHByUZ555xhqz448/XvTnS39GKyoqrJ/RI488Un7wgx+Ifl9SKbHfb/tnIdXXm266KWEzLS0tcvfdd4s+4yGHHCI77bST9d0aP368qO3ZZ58tt912m6hVJkXHQn9mFi1aZI2R2mq9+l3WMdSxTLX09fXJCy+8IL/97W/loosukr333tv62bIdMglc222//PLL8h//8R+yzz77WN9j/fNUf16PPfZYue6666Srq8u+lFcEEEAAAQSKS8D8ZU1BAAEEEEAAAR8FzC+g0SuvvDJqAh36G3PK/5lf6qMPPvhg0p6aX3RTrs9u2wSgoh/96Eejy5cvT1q3/aH2wb7X/MIefeKJJ+yPkr6agJ9z38yZM6NtbW1Drv/nP//pXKNtZLMMDAxE99xzT6t+E8SMmiBEwurt6+znTPZqAizR3/zmNwnrsj+45JJLPM+WrM5Ro0ZF9fru7m779rRftX4TDEv7Pr3hu9/9rtNXE7TKqI7hbtq0aVPUBNCcduJ5mGBr9MYbbxyuqqgJfCWtJ7buD3/4w9Ha2tqk9V511VVRE4hPqV4TZIya4Fg0EokkrTP2+x3br+GOH3rooSH1m6B89MQTT4zqz/Fw9+vnJoiZ8s+s3ZgJnEVN4DNp/UcddVS0sbHRviXhq/kHjWH7msn3Vv9c1fuGM9htt92izz77bML+8QECCCCAAAJhFSgzf0lSEEAAAQQQQMAnARN0kFNOOUVeeeUVT4ua9bT//vtbmUqaqdfU1CQmQCKrVq0SE1SwrjW/tIr5Rd/KckolM8sEOazMNbshEwCT1tZW0UyhV199Vcwv69ZHmo3zxz/+0coe+trXviZXXHGFlTlk3xf7esIJJ8g555xjZROZ/4MkF198sfU8mhGUqGjm0+9+9zvn4//93/8VzUzys2i24xtvvGE1+ZWvfMXKDEulfZ0qvGDBAjFBS9Gx0SzINWvWWGOjz6+ZfZp1tXnzZitTLJU6NZtQ65w3b57loGOwfv16MUFY6enpEc08+/Wvf221o1mDmnnoZ9EMqp/97GdWttTll18uTz/9dFabVzPNyHr99dedehcvXiwm8ComMCz/+Mc/rO9qR0eHXHjhhWIColY2rHNxkjeaUam2JtAjkydPtjLTdGzUVn+utPz973+XD37wg9ZzaVZqvPLoo49aY2p/pll/Bx54oJVNpt+DDRs2WPebIK2VVafZcW+//bbccsstVn/t+9yvNTU1YgK77lNJ32sf7GzQnXfeWUyQbcj1amSC8p7zeq1mFWsmsWb8aVac/sxr0X7rnw2atad/ngxXbrjhButn3L5u4sSJopmP+h3W8dNMPi2PP/64NaaaMalZsomKjoF+37NZtL6jjz7ayta0650xY4Y1xtpPzV588sknRcfqnXfesa7VP5PUiIIAAggggEDRCIQ1sslzIYAAAgggkG8CZj21qJmW52SoaPbcmWeeGV25cmXUBHzidtcESqLmF/WoZiyZ/3Ni/WemHMa9Vk+6MwCHy5xat25d1AR3omYaqlO3tmGmtkY1Wy5ZMb/EezIYTeAw4eWamWOmCjptmOBhwmtjM6QSXpjBB2aaodUHE7yJm33orvJb3/pW9Oabb46awIH7tOe9jqdmPdnjoll7zz//vOca98H1118fNVNUoyZoknC8NSvSBHedOrXun/70p+5q4r43AZDorbfeGj355JOjml1ppj06dejz6vfimGOOif7Xf/2Xlek5XKaaNuLOpjIBwLjtZnryvPPOc/pngnRRE5DzVGWCWlH9nti2mt1mAmGea9wH2j8zdTqqryaA6v7IeW8CYVEdAxOoduo1wWzn89g3JkAZ1Z9RM/U3aqbqx83G3LZtW/RjH/uYU5/2N5Vs0Ni24h3rGLkzEL/85S/Hu8zKutN2zbTt6KWXXho1wb6415mpz1ETcHb6qpmrJjAa91r7pAl0RzW70R4HHRMdG3fRsdO27WtMwNb98ZD33/ve96xrNQv3tNNOi/74xz+2xt9MJXbq0O9eOsX9M6M/h/ozE/sd12fVsbT7aYKxUbMMQzrNcC0CCCCAAAIFLZDduTUFTUHnEUAAAQQQyJ2ABsH2228/55dPDcrce++9aTWowSUNYmUrAGg3rr8Ym7XnnL7pL8hm7Tv744Svd955p3NPaWlp1GQCxb1WAxf2L90aANXgYaKSqwCgyU5y+pBucCFRX/W8WScvatYHdOo22ZDJLk/5Mw2i2GY6ZTFZ0SClyR51rrfvS/YabyppbBsaMLLr0OBMtsprr70W1SCNXbfJcIxbtQahddq7fZ0G2rJR/vCHPzh1aoCvrq4ubrUmAzJhMM19gwbvTVavU6fJGE0Y4HXfN9z7v/71r06damCyhuPeYtagtKZsx5tSH3uDyTKNauDPNjXrPcZe4jnWf6Cwr126dGnCfxj429/+5lynfxasXr3aU4/7wGQgxg08ugPO6fyM6lRyk53ptP+jH/3I3ZznvQbKTdafc+0Pf/hDz+ccIIAAAgggEGYBAoBhHl2eDQEEEEAgbwR0fTD7F2l91ay+TIoGnDSDLFFJJwPQXYdmTbmDSPpLvGa4DVc048x+LrPo/pB19XRdQXewR4OGyUquAoDuLC0NVmSzaEDWNjAbJGSlanfwTevWdd7iFf0+7LXXXk77eq2u8WY2j7HOmQ1foueee270Qx/6kGcNt1QCgBrYMtMorXo0CyxZ4DZe3xKdM9Olnf6aaZuJLrPOa0afbavfSc24G2nRIJA7C/CBBx4YaZVWwMvup76++OKLI67zrLPOcp594cKFI67PrkCD+3Zf9R8UEhX9hwH3z66Z2pvoUuu8OxvWTCFPem28DzMNAJqNRJzn0XVVE2WA2m1qwNl+fs2wHC7b2b6PVwQQQAABBApdgF2Azf8DoCCAAAIIIJBLAd3tV3eLtYsJRsmyZcvsw7RedX0zXSct20XXN7v99tudtct0vUCzUcmwzegunrrGlhZdY8xM53Pu0XW5dIdPXc9Oi659aDYbcT736436m2xLq7lx48ZZ659ls21dv9Euuj5gNoq7Tq0vUb0mm81ZR093u9VdTs3UbtHzWpYsWWKtSafrnzU3N4sJdonuZmsCO9bnyf7HZMc531NdR053RR5pMf/HWe6//36nGl3fL1kxWWfWWn56jX4n3fcmuy/ZZ+pkf2f1ukS2yeqI/UzXHNSdk+2i6+yNpOhane5nNcGxkVTnuVdN7ZKsn9q+/bO7++67WzsL2/fFe3Xv3Gv/vMW7LtvnTGa0U6Wubah/liUrun6h7gysxQQ5PesGJruPzxBAAAEEECh0geH/31+hPyH9RwABBBBAIGABs0OpFXyxu5HKBh72tX6+mmwx0Q0+7KL9tgMA9rnYV11oXzeLsItZY07M9D/r0GTGOe914wANFgZRdMMHXfxfiwY/NIiazWI/r9Zpsu+yUrW7Tt1wIjYgaDdy33332W/FrFson/zkJ0UDd/GKPrdu+mCmllqbIMS7JvacBkvsErvRhH0+nVfd0KK+vt65xUw9d94nenPEEUc4H+nmICMtugmFySR0qsnWmLndNVg5kqLBVt0MRosGLD/+8Y+PpDrPvan202TjOvelO0666YZutuFH2bJli9OMyYB23id6o57655Zd9M8HCgIIIIAAAsUgQACwGEaZZ0QAAQQQCFTA/Yu07vqqO4nmazFrfjld092C3bu0Oh/EvNFdgO1AkZ31p7sXuzMINUiou+kGUR577DGnWTMV1nmfjTe6q7DZWMKpyqyV57zP9I3ukKs7ytpFs0UT7QJs1nSzL/MEb52TCd64g0AJLrFOu71011R7R+pk9yT77M0333Q+1h1qU/lO6O7YdnHfb59L51UzEM0UWCubUO/THYCz8fPY0NDgCfKbDS7S6daQa+0MTv1AMzYTBYCH3JjCCbMGo3NVsn66rd1j4Nwc80aDarr7sF3c99vncvGqYzqSYu8MPpI6uBcBBBBAAIFCECAAWAijRB8RQAABBApawKzn5vT/oIMOct7n45vY/pk1/FLq5rXXXiuaqaZFp+Rp1pZOG9Vi1gazMtOsgwD+xz1F0KxTOOIemF1Q5aWXXhKzm6kcfPDBolOMtRx22GEy3JTWRI2btfysjKnf/e53YtZ7s6ZT67UaJPvJT36S6DbP+ZEG5zyV/fvAbNriBOnMRjYpBYTj1WOfe+utt+y3kkq2ll6sQTq7mF1p7bcpv6qLBujMTr7W99Lsjm3dq5lgmpWaKLiacgPmwptuusm5XLNdzUYTznG6bzRL8tlnn3Vuy+b0X83oveWWW5y69WczUQlirBL1Jdl5d3BUMw+HK/rnUmNjo3OZX4FKp0HeIIAAAgggEJAAAcCA4GkWAQQQQKB4BMwuo87D6lph+Vze9773edaHc0+vS9bv+fPni9lR07lEs9i0jB07Vn7/+9875/1+o9lB7owns2Nv2l249dZbrWm1mjWn/+k6gpoRdcUVV1jrx+k5Xf/skUceSTmYpNNg7fr0VdckU/vPfOYz1hp+2kkNxq5YsUKSZWnpPXa57bbb7LdZfXV/Z81OtCOq2+xY69zvzhZzTsZ5o0FQu2gQUoOlwxWzc7Ljq4G+mpoaOf300+WJJ56wbtXMQ80M1ey6kRYNLrqDtJ/+9KdT/h7Ea/vmm292Tk+ZMiWtzE7nxgRvfvOb34gdRNV1ID/72c/GvVKnzNvT5vWCTMZK15z0o5iNd5xmdIq4/Q8PzsmYN5rJ6n42v/oZ0w0OEUAAAQQQ8F2gzPcWaRABBBBAAIEiEtBAmDszS7ODhitml1rR/5IVDT5Nnjw52SUZfWYHuNra2qz7dRpwquXSSy8VDTC41/7SNQF12nNQRRf5t9dS0z7MnDkzq13R4JxmlOnmA9kqZodaK6BkdlIdtsqzzz5b7HUAf/3rX1uBxK997WspB2yGbcBcoMEzuyTbNMK+JtmrZk/aRZ8zlRJ7ndYx3EYPyerVdRJ/+ctfWsHpZNel8pmu9Wd2XHY2EtFA2WWXXZbKrXGv0YC1Bpztomv/ZWvNSp3q+s1vftOu2srKTbShkHuc9IbYMXAqiXnjvi62jphLs3ao61p++ctfttYr1X+w0LH96le/Grd+/bP4u9/9ruezbGwC46mQAwQQQAABBPJUgABgng4M3UIAAQQQCIdA7C+XmhE3XNGsLw3mJCv6C24uAoDaZnV1tdgBwNj+J+vT008/Le+++67nEg0oBlncGYw6Rdne/TOdPunmKJdccol1i06hVBvdpEOz4TS78+ijj5ZTTz3VGjN3tlqyNtTYrlOvU2ddz++FF16wspM0mKrTSnVKcLI16nRXZZ3SaQeMr7rqKvn5z38u++67r9W8jsdzzz1nTSvO5Nm1kqlTp1p16f9oQHUkxR2MTTWwFRvsc2dvJerLeeed52z0oW1qlp5OBddsL12zUb10jUq9biTlG9/4htibSOh3Xdfuc+8GnG7dmqHoDrJma/qv7iqsa0naQTnNHL366qsTds89TnpRJmOVyjgl7EAaH+y6666iO6vbGbAa5NT+6s9XaWmpU5Nu/PKpT31KYpc18KufTkd4gwACCCCAQEACBAADgqdZBBBAAIHiENDpou5irxfnPpdv791Bv/Hjx6fUPf0lWjcDiV2Q//LLL7cCD+513FKqMEsXub3tNQrTrVoDcPGCcLpW2xe/+EUrmKTry7344ouiQdBUsgw1E/R//ud/hnRFA1QamPrpT39qrTN46KGHyv33359w114NOv3pT3+yNrbQ9ew0w0mDlLpGoRbtzyGHHGKtz3jcccfJF77wBWutwiENJznhdnN7Jrkl4UfuIKRuGJNKiZ3y684yS3S/rs8YW7Q9DZZ+5StfsdaA08w9DbZ95zvfib00pWP11oCrXbTNY4891j7M6NW9+cdee+0l7umtGVVobtJg3imnnOJk5urP9D333GMF+hPV6R4nvSaTsUplnBK1n+75a665xgrwavax/gxoAP2///u/RTex0efVQL0GV3UKuQYFNWvQzpyN/TM63ba5HgEEEEAAgUIRYA3AQhkp+okAAgggUJAC+sune5MBzcQZrugOsBpIc//n3u11uPtH8rkGj9wBwFSzDDX4oQExLZoFZ08t1IyjROuMjaSfmdwbG5zMpA73PZpF9cADD1gBTj1fW1s74s1O1FsDgHZgSYM355xzjthrKrrbt99rsOZXv/qV5a/joOsTur9zep0GPjRQePjhh4tmDaYTyMumm2Y+2iXVzKvY69x12HWl8qpZYTr999FHHxVdF1CLej3zzDOp3O655s4775TPf/7zzjn9jmtdIyn2GNl1ZCP7T4NhOt5PPvmkVa1+VzSgPNxmOLHGsWNg9zH21X1dbB2x12bzWLMudRx1Ix67bNq0SXScdA3Shx56yPoZ0GCfBoE1sG6XVJZlsK/lFQEEEEAAgUIWIABYyKNH3xFAAAEECkLAnf2mU0fzubz99tueLL5UprSuXLnSM51Qpy/rNEvdZECLTre8/fbbA3ls95Rrd3AiW53RZ9Q1x+ypzhpcev3110dcvWYw2Rt86NRF98YQiSqfO3euaPBYMxHtgKEGenR6sjuL76677rKCQonqiT3vdnN7xl6XyrFuamEX9/Rs+1y8V/e0Y32O2CnB8e5Jdm7x4sXWun16jQY3dcp0OuXBBx+07tdguRZdpy9eNmc6deq1mkVqB981S00DvyMp2r9PmM1pNOCnRYPCd999tydIlqh+zd5zZ/BlMlap/uNBoj6ke153rNYNPv76179adroxkX5fNBCp/yCh07X1Z1OnCzc1NTnVJ9tkx7mINwgggAACCIRAgABgCAaRR0AAAQQQyG8BnYZmF13fL5+LrpPmLkuWLHEfDnmvO25qVpVuhqDltNNOs/7THWx1uqlddKqsewdY+3yuX90BTM2wcgezstW2Bnh1nUC7ZJJRZt9rv2pg0b2xSLp12sGbhQsXWoElXQPPveagBkkefvhhu7mkrxqAtIvb0z6XzqvbSTMmUykbN250LstkF2fnZtcbXbfRLunY6i6zZ555prPT7Mknn2yt+2cHu+06M3l1T/895phjRHcqHknRHaXtdfG0fxpE1qmvqZZ8GatU+2tfpzs760Yquv6lZrpqUFUDf7pTs/2PMbohil3iTe+3P+MVAQQQQACBMAkQAAzTaPIsCCCAAAJ5KXDEEUc4/dKpvPkcBNQMIbvo5g977LGHfRj39cc//rG8+uqr1mc6Dc+9eYnuADxnzhzrMw0iaRDQ76K7strBMG27vr4+J11wb/yQrUBnNuucMGGClaWmASu72Gug2ceJXnUqpV00y3AkZcGCBc7tmtnnzu5zPoh5s2rVKueM+37nZAZvMrHVQKH62RtkaIBWsyljp1tn0B3re6nBRbto5t5Iype+9CW59tprnSp0MxnNfEunuK3tNSWT3a9BZnemoPv+ZPf5/ZlmRrr/oUPXyKQggAACCCBQDAIEAIthlHlGBBBAAIFABc444wxxT338xS9+EWh/EjW+Zs0aa60s+/OzzjrLmdpqn3O/ahaNBvnsouvWuTPEdLqoBh7solk5jzzyiH3oy6tOzd17772dtt566y3nfTbfNDY2OtVla+pjLurU9eDs4t5t1j4X7/XNN990Ttu7Czsn0nyj05rdm6TolM3him7eYJcjjzzSfjui13RtdZq7ZpbZaydq0EgDqCOdjmw/hP5s2FOKdU06d6DWvibVV914x/1njE5x1g160i3uf7hId5w002633XZLt0lfrtdnsQOV+rN6wgkn+NIujSCAAAIIIBC0AAHAoEeA9hFAAAEEQi+ggTD3dNg77rhD7r333rx6bt1pVdccs4MQukmCrpmVqOh1OvXX3h1Up1ReeOGFQy7XXVHPO+885/z/+3//zwmiOCdz/EbXfLPLK6+8Yr/N2qsGQt3BtGxkPqmrridol2zUqXW5d3XWTTGGK1u3bnWy9HQ9Nd2ZdiRFA7Lu4NZNN92UtLrnnntOdF1KLbou3kknnZT0+lQ/1HX87DKc7WuvvWbt7muvq6ibrOi6liNdD9FuX1/d0381SBu7C6/72mTvNSD/ox/9yLnkiiuuyDjzVsfJntqsgfPly5c79cZ74x5L3XU4X8t3v/tdp2uaaZmptVMJbxBAAAEEECgQAQKABTJQdBMBBBBAoLAFNJim67HZRYNtqU7BtO/J1asGeT7ykY+Ie6rlZZdd5qyXFa9dzTCyp9FpIER32kxUNANp2rRp1se67ptmKPlZ3Ou9Pf3008M2nc4UXp0O+rnPfc6pUzci+OAHP+gc22/a2tqcdRLtc8lev/Od74hOqbSLrq0Yr7zwwgvxTic8p7sW22X33Xe33yZ8feqpp5zPDj/88KxMd9W16ezAkmaEPvbYY04b7jcaZP7617/unNK19+zvkXPSvNGdbtU31aLBO90R2S6nn366/XbIqwYf9fvT3NxsfaZT4rXPOqU6W0WXBNDsW7tkOv1XN6P59re/bVdj2en3KNOi0+fd3zsdi0Q7Qmuw2g5Ya6BWxzgfi26SY6/5qNnKbq987C99QgABBBBAIKsC5i9yCgIIIIAAAgj4ILBu3bqoCRBFzV/k1n8mGypqptlGza6tURPsiNsDs7lG9J///GfUBCGc+/R+s5Zg3OvNmnvOdTfeeGPca+yTWocJEETN9GTnHq3bTFlO2B+995133omabDDnHhPgs6tM+GqyHp3rTfAnarKJ4l6rz2r76Gs2SkdHR9SsA2jVO27cuKjJdkxarVmrMHrUUUdF77nnnqjZNCThtSaYGDXZhZ7+XnfddXGv/8tf/hI1mypEf/Ob30RNwDXuNXrSbFwQPffccz116nGiYtbki5osy6gJog0ZM/W74IILrFv1e2SCtlH9ztm+icbA3ZYJbjrXa9+zVUxWqFOvfv903N1Fx8x9jclWjK5du9Z9ifO+paUlatb0s77LJpDmnI99Y4J40R/84AdRM23XadtMU42aIG7spdaxCVZHzQ6xnmtNUDbutSM56TY2QdmMqjK7bnvG1mz4klE9sTeZ6d9Rkw3sGOiY6Ni4i1m70PNniMkEdn+c8nv9rtrfTft7m+rNJrM3qj+3Jlsz7i1mynfUBFad+vXPIPMPMHGv5SQCCCCAAAJhFSjRBzN/2VIQQAABBBBAwAcBzYDT6XGxU1E1s2nRokWiG29UV1db02R1wwrdYCM2I03X5tLsQRPMGtJj3aTB3l1VNylw75qqO/VqppQJmFj1ujPMtCLN3NHMP82SSbSxgf7fBq3XBGystnWXYM2osTO6hnTIdUJ3INXdZ7XoVFLNONSpxu6i63O51x7L1v9N+fjHPy469VqLZoAdd9xx7mY973WzEs2m0qJrvO25557Wema6Npsa6oYm2vfYDUUuvfRSz9pr7kp1yvepp55qnVIrXR9Ns8l0DTI10DHRqcTu3Un14qVLl1rrMsYba/3cPd4mWCWHHXaY7LPPPtb36KKLLhLdjfnggw+W+++/X0wAWm+xik7L1l1hkxW11zp1ExDto66b517LMtm9w32m02l1HT3382pf1UQ/0w0x1MQuOr3UBIXsQ89ra2uruDf1mDFjhmWg2Zg6bdkErMQEra0xs6esawWaAabtJJoCrFPH3RmWOv3Y3kXW04E4B/pzYQK3cT7xntL+6G6/doahTuH91re+5b1omCOdorzffvs50/c1I1etdLp1KkW/t7o2Y6JigoueNQTVWtdi1Onkq1evdjKB9X797mmWbaLvq92G9je26G7P9phrG/GsX3755djbrGOdnqzfcy268ZDWr+Ovu37r914/t5c30J+/G264IeH3yaqE/0EAAQQQQCCMAub/3FEQQAABBBBAwEcBs5FA1OyeGzXBPicjxfx/jKTvNXPrQx/6UNQEkpL21J0BOFyd9ueaEXX22WdHTbAjad36odnUw+mnZmWZAM6w99gXmF/wo5qBZ7dr1iezP3JeNZvI/lyzdLJVHn/8cafe4bKLzFRH51q7L8leNavTrOGWtKsm6JhWnWr7zW9+M9rV1ZW0XrO2pCcbM1k/7c9MMDRh1pu7Mc1wtO8x02TdH2XlvQksRk0gyWnDbsv9aoLhUROsSdqeCWpHTcA6aT3uOvW9mdoaNQHcpPVm8rNktzPcd8xu2ExFdvqt3/e6ujr7o5RfNXvSbjeT19jsy3gNmx2FoyawmLQd8w8D0VQzJDPpp96TqJi1IpP2zW7PBDqj+mcBBQEEEEAAgWIUIAPQ/D8CCgIIIIAAAkEI6I6iDz/8sPz973+3smh0LT7N9tNMFc0206xAzWQ58MADRbPn5s+fP2w33RlhsRdrNpuuXab/1dTUiG5mcMABB1hrnGnm4XBFM8HsDC299vvf/764F9Qf7n793EwjFTM90bpU+6MZPe4sxYceesjabVUv0AyjdNZ2sypN8j+anaTZUpoVptmPydZx0wxNzQ7T9dl0F1wTmJH29nYr01H7pZlxuqajrp2omWH6LMMVzRjUddI0G0n7YaZgi2avaVahZkxpxpLusqtZfLoRRKrZdtovXZfOBDYsT814UjfNLtMsMM0o1fHWDCndqMUEkofrqvW5XquZd1p0LcB4axtaH47gf8z/+RYzPVpuu+02K0NPswy1v5r9pa660Uy8TLDYJjVrUJ9fs1FfeuklK+tLMzV1cxvNiNOMMs3k1CxDzQZNZYfaZD9Lse3HHmsGnm0X+5n7WLOBNTtTi5l2nnA9RPc9se9js2ZjPx/uWLN5dX3H4Ypm6Gk2oK4jqe81s1KzF/XPEc12XLZsWcpZh6lmJ8b2Sb8v8Yp+1/XnVf8sffbZZ62sVf3zVDOZtY/6c6VrPWofU/lZjdcG5xBAAAEEECh0AQKAhT6C9B8BBBBAAIEQCdx6663OrsE6lW/Dhg1Ze7rbb7/d2ulYK9SNSXSqb5iLBllSDUTFOmggWgNvJgPRmqprb5wQex3HCCCAAAIIIIAAAoUhMKowukkvEUAAAQQQQKAYBDTbzi6J1mazP0/31UxztrLA9L6rr75a+vv7062iaK6/5pprrOCfPrCuS0dBAAEEEEAAAQQQKGwBAoCFPX70HgEEEEAAgdAI6PQ+3dzELvai/vbxSF918f+f/exnVjU6pVc3AqAMFdBpyRoA1KJTz1OZHjq0Fs4ggAACCCCAAAII5JMAAcB8Gg36ggACCCCAQJEK6LqH3/ve95xdYXX6qmbsZbvomn267poW3e1Y12GkeAWuvPJKa1daXSvtF7/4hfdDjhBAAAEEEEAAAQQKUoA1AAty2Og0AggggAAChS+wdu1a+eUvf2ltWKEL9+vmFXa5+OKLxew8ah/ymoHASNYAzKA5bkEAAQQQQAABBBDIYwECgHk8OHQNAQQQQACBMAsk2r1Up53eddddUlVVFebH59kQQAABBBBAAAEEEPBNoMy3lmgIAQQQQAABBBCII1BZWSnTpk2Tgw46SM4991xnim6cSzmFAAIIIIAAAggggAACGQiQAZgBGrcggAACCCCAAAIIIIAAAggggAACCCBQKAJsAlIoI0U/EUAAAQQQQAABBBBAAAEEEEAAAQQQyECAAGAGaNyCAAIIIIAAAggggAACCCCAAAIIIIBAoQgQACyUkaKfCCCAAAIIIIAAAggggAACCCCAAAIIZCBAADADNG5BAAEEEEAAAQQQQAABBBBAAAEEEECgUAQIABbKSNFPBBBAAAEEEEAAAQQQQAABBBBAAAEEMhAgAJgBGrcggAACCCCAAAIIIIAAAggggAACCCBQKAJlhdJR+hkugZ6eHnnttdesh5o2bZqUlfFVDNcI8zQIIIAAAggggAACCCCAAAL5IBCJRGTbtm1WV/bee2+prKzMh27RB58FiLr4DE5z/xLQ4N/ixYvhQAABBBBAAAEEEEAAAQQQQAABnwRWrFghBx54oE+t0Uw+CTAFOJ9Gg74ggAACCCCAAAIIIIAAAggggAACCCCQZQEyALMMSnWpCei0X7vov0BMnz7dPuQVAQQQQAABBBBAAAEEEEAAAQSyJNDY2OjMwHP/Lp6l6qmmQAQIABbIQIWtm+41/zT4N3PmzLA9Is+DAAIIIIAAAggggAACCCCAQF4JuH8Xz6uO0ZmcCzAFOOfENIAAAggggAACCCCAAAIIIIAAAggggEBwAgQAg7OnZQQQQAABBBBAAAEEEEAAAQQQQAABBHIuQAAw58Q0gAACCCCAAAIIIIAAAggggAACCCCAQHACBACDs6dlBBBAAAEEEEAAAQQQQAABBBBAAAEEci5AADDnxDSAAAIIIIAAAggggAACCCCAAAIIIIBAcAIEAIOzp2UEEEAAAQQQQAABBBBAAAEEEEAAAQRyLkAAMOfENIAAAggggAACCCCAAAIIIIAAAggggEBwAgQAg7OnZQQQQAABBBBAAAEEEEAAAQQQQAABBHIuQAAw58Q0gAACCCCAAAIIIIAAAggggAACCCCAQHACBACDs6dlBBBAAAEEEEAAAQQQQAABBBBAAAEEci5AADDnxDSAAAIIIIAAAggggAACCCCAAAIIIIBAcAIEAIOzp2UEEEAAAQQQQAABBBBAAAEEEEAAAQRyLkAAMOfENIAAAggggAACCCCAAAIIIIAAAggggEBwAgQAg7OnZQQQQAABBBBAAAEEEEAAAQQQQAABBHIuQAAw58Q0gAACCCCAAAIIIIAAAggggAACCCCAQHACBACDs6dlBBBAAAEEEEAAAQQQQAABBBBAAAEEci5AADDnxDSAAAIIIIAAAggggAACCCCAAAIIIIBAcAIEAIOzp2UEEEAAAQQQQAABBBBAAAEEEEAAAQRyLkAAMOfENIAAAggggAACCCCAAAIIIIAAAggggEBwAgQAg7OnZQQQQAABBBBAAAEEEEAAAQQQQAABBHIuQAAw58Q0gAACCCCAAAIIIIAAAggggAACCCCAQHACBACDs6dlBBBAAAEEEEAAAQQQQAABBBBAAAEEci5AADDnxDSAAAIIIIAAAggggAACCCCAAAIIIIBAcAIEAIOzp2UEEEAAAQQQQAABBBBAAAEEEEAAAQRyLkAAMOfENIAAAggggAACCCCAAAIIIIAAAggggEBwAgQAg7OnZQQQQAABBBBAAAEEEEAAAQQQQAABBHIuQAAw58Q0gAACCCCAAAIIIIAAAggggAACCCCAQHACBACDs6dlBBBAAAEEEEAAAQQQQAABBBBAAAEEci5AADDnxDSAAAIIIIAAAggggAACCCCAAAIIIIBAcAIEAIOzp2UEEEAAAQQQQAABBBBAAAEEEEAAAQRyLkAAMOfENIAAAggggAACCCCAAAIIIIAAAmERiEajYXkUnqOIBAgAFtFg86gIIIAAAggggAACCCCAAAIIIDAyga3tvSOrgLsRCECAAGAA6DSJAAIIIIAAAggggAACCCCAAAKFJ9DTPyCbWrsLr+P0uOgFCAAW/VcAAAQQQAABBBBAAAEEEEAAAQQQSEWgdnuXMAU4FSmuyTcBAoD5NiL0BwEEEEAAAQQQQAABBBBAAAEE8k6gratfmjv78q5fdAiBVAQIAKaixDUIIIAAAggggAACCCCAAAIIIFC0Apr1t357Z9E+Pw9e+AIEAAt/DHkCBBBAAAEEEEAAAQQQQAABBBDIocDmHT3S3TeQwxaoGoHcChAAzK0vtSOAAAIIIIAAAggggAACCCCAQAEL9EUGpb6FjT8KeAjpuhEgAMjXAAEEEEAAAQQQQAABBBBAAAEEEEggsLG5SyID0QSfchqBwhAgAFgY40QvEUAAAQQQQAABBBBAAAEEEEDAZ4H2nn7Z1t7rc6s0h0D2BQgAZt+UGhFAAAEEEEAAAQQQQAABBBBAIAQCtdu7QvAUPAICTAHmO4AAAggggAACCCCAAAIIIIAAAggMEWjt6pP2nsiQ85xAoBAFyAAsxFGjzwgggAACCCCAAAIIIIAAAgggkFOBumY2/sgpMJX7KkAA0FduGkMAAQQQQAABBBBAAAEEEEAAgXwXaO7sk45esv/yfZzoX+oCBABTt+JKBBBAAAEEEEAAAQQQQAABBBAIuUA0GpU6s/MvBYEwCRAADNNo8iwIIIAAAggggAACCCCAAAIIIDAigaaOPunqGxhRHdyMQL4JEADMtxGhPwgggAACCCCAAAIIIIAAAgggEIiAZv/Vt5D9Fwg+jeZUgABgTnmpHAEEEEAAAQQQQAABBBBAAAEECkVgW3uv9PQPFkp36ScCKQsQAEyZigsRQAABBBBAAAEEEEAAAQQQQCCsAoODJvuvlZ1/wzq+xf5cBACL/RvA8yOAAAIIIIAAAggggAACCCCAgGw12X+9ZP/xTQipAAHAkA4sj4UAAggggAACCCCAAAIIIIAAAqkJaPbfplbW/ktNi6sKUYAAYCGOGn1GAAEEEEAAAQQQQAABBBBAAIGsCTS0dUtfJJq1+qgIgXwTIACYbyNCfxBAAAEEEEAAAQQQQAABBBBAwDeB/oFBaWzr8a09GkIgCAECgEGo0yYCCCCAAAIIIIAAAggggAACCOSFwKaWbokMkP2XF4NBJ3ImQAAwZ7RUjAACCCCAAAIIIIAAAggggAAC+SzQ0z8gW3aQ/ZfPY0TfsiNAADA7jtSCAAIIIIAAAggggAACCCCAAAIFJlDX3CVm/w8KAqEXIAAY+iHmARFAAAEEEEAAAQQQQAABBBBAIFagozciTR19sac5RiCUAgQAQzmsPBQCCCCAAAIIIIAAAggggAACCCQTqN3emexjPkMgVAIEAEM1nDwMAggggAACCCCAAAIIIIAAAggMJ9DS2Sc7uiPDXcbnCIRGgABgaIaSB0EAAQQQQAABBBBAAAEEEEAAgeEEotGobDRr/1EQKCYBAoDFNNo8KwIIIIAAAggggAACCCCAAAJFLrCto1e6+gaKXIHHLzYBAoDFNuI8LwIIIIAAAggggAACCCCAAAJFKjBgtvyta+4u0qfnsYtZgABgMY8+z44AAggggAACCCCAAAIIIIBAEQk0tnVLX2SwiJ6YR0XgXwIEAPkmIIAAAggggAACCCCAAAIIIIBA6AU08NfQ2hP65+QBEYgnQAAwngrnEEAAAQQQQAABBBBAAAEEEEAgVAL1LV2iU4ApCBSjAAHAYhx1nhkBBBBAAAEEEEAAAQQQQACBIhLo6ovI1vbeInpiHhUBrwABQK8HRwgggAACCCCAAAIIIIAAAgggEDKB2u1dEiX5L2SjyuOkI0AAMB0trkUAAQQQQAABBBBAAAEEEEAAgYISaOvql1bzHwWBYhYgAFjMo8+zI4AAAggggAACCCCAAAIIIBBigahJ+6tt7gzxE/JoCKQmQAAwNSeuQgABBBBAAAEEEEAAAQQQQACBAhPY1tErnb0DBdZruotA9gUIAGbflBoRQAABBBBAAAEEEEAAAQQQQCBgAd3xt665O+Be0DwC+SFAADA/xoFeIIAAAggggAACCCCAAAIIIIBAFgUaWrulLzKYxRqpCoHCFSAAWLhjR88RQAABBBBAAAEEEEAAAQQQQCCOQG9kQBrbeuJ8wikEilOAAGBxjjtPjQACCCCAAAIIIIAAAggggEBoBeqau0SnAFMQQOBfAgQA+SYggAACCCCAAAIIIIAAAggggEBoBNp7+mVbe19onocHQSAbAgQAs6FIHQgggAACCCCAAAIIIIAAAgggkBcCtdu78qIfdAKBfBIgAJhPo0FfEEAAAQQQQAABBBBAAAEEEEAgY4Gmjl5p74lkfD83IhBWAQKAYR1ZngsBBBBAAAEEEEAAAQQQQACBIhIYNGv+bTRr/1EQQGCoAAHAoSacQQABBBBAAAEEEEAAAQQQQACBAhNoaOuW3v7BAus13UXAHwECgP440woCCCCAAAIIIIAAAggggAACCORIoDcyIA2tPTmqnWoRKHwBAoCFP4Y8AQIIIIAAAggggAACCCCAAAJFLVBnpv4OmCnAFAQQiC9AADC+C2cRQAABBBBAAAEEEEAAAQQQQKAABNq6+2Vbe18B9JQuIhCcAAHA4OxpGQEEEEAAAQQQQAABBBBAAAEERiAQjUZlfVPnCGrgVgSKQ4AAYHGMM0+JAAIIIIAAAggggAACCCCAQOgEGtp6pLtvIHTPxQMhkG0BAoDZFqU+BBBAAAEEEEAAAQQQQAABBBDIuUBP/4BsaunOeTs0gEAYBAgAhmEUeQYEEEAAAQQQQAABBBBAAAEEikygdjsbfxTZkPO4IxAgADgCPG5FAAEEEEAAAQQQQAABBBBAAAH/BVo6+6TZ/EdBAIHUBAgApubEVQgggAACCCCAAAIIIIAAAgggkAcCg4Nm44/twW38sXlHTx4o0AUE0hMgAJieF1cjgAACCCCAAAIIIIAAAggggECAAptau6W3fzCQHqyqbZEv3P6SXPHAaunojQTSBxpFIBMBAoCZqHEPAggggAACCCCAAAIIIIAAAgj4LtAbGZAGEwAMomjbNz27QUwCotzwzHr58FX/J4+v3hJEV2gTgbQFCACmTcYNCCCAAAIIIIAAAggggAACCCAQhEC92fVXA3BBlHtf2iTbOnqdprfs6JW27n7nmDcI5LMAAcB8Hh36hgACCCCAAAIIIIAAAggggAAClkBP/4Bsa38vAOcnyyYTeHzg1UZPk4vnTZbT9q/xnOMAgXwVIACYryNDvxBAAAEEEEAAAQQQQAABBBBAwBGob+mSaADZf1HTqE75HXClHpaNKpEfLttLSkpKnP7xBoF8FiAAmM+jQ98QQAABBBBAAAEEEEAAAQQQQEC6+wakqaMvEImn32mS1Y07PG1f/KH5svvO4zznOEAgnwUIAObz6NA3BBBAAAEEEEAAAQQQQAABBBCQuoCy/3Sn31uf3+gZgWnjKuQ/Pryb5xwHCOS7AAHAfB8h+ocAAggggAACCCCAAAIIIIBAEQt0miDc9oCy/+5aWSc7Yjb6+OTSuTKmvKyIR4RHL0QBAoCFOGr0GQEEEEAAAQQQQAABBBBAAIEiEdCdf4Mo727rkMdXb/E0vWjOJDnQbP5BQaDQBAgAFtqI0V8EEEAAAQQQQAABBBBAAAEEikRAp+A2d/q/9t+g2fDj+qfXi3vPkYqyUXLBwXOLRJ7HDJsAAcCwjSjPgwACCCCAAAIIIIAAAggggEBIBOqauwJ5kr+v2Srrmzo9bZ+2sEZ0/T8KAoUoQACwEEeNPiOAAAIIIIAAAggggAACCCAQcoEdPf3S2tXv+1P2Rgbkz6vqPe3WTKyS4/ee7jnHAQKFJEAAsJBGi74igAACCCCAAAIIIIAAAgggUCQCtU3BZP898vpmaY3Z+ONCs/FHWSkhlCL56oXyMfn2hnJYeSgEEEAAAQQQQAABBBBAAAEECldg644e0fX//C664/D9rzZ4mt135gTZc8YEzzkOECg0AQKAhTZi9BcBBBBAAAEEEEAAAQQQQACBEAtEBgalriWY7L8HX22Uzt4Bj+5HD5ztOeYAgUIUIABYiKNGnxFAAAEEEEAAAQQQQAABBBAIqcCm1m7pi7j33/XnQVu7+uSh1xs9jR00b7LMmzrWc44DBApRgABgIY4afUYAAQQQQAABBBBAAAEEEEAghALdfQPS2NYTyJPd93KD9EYGnbZLSkTOPGCWc8wbBApZgABgIY8efUcAAQQQQAABBBBAAAEEEEAgRAK1zZ0S9T/5T7a198rjb27xSB72vmmiu/9SEAiDAAHAMIwiz4AAAggggAACCCCAAAIIIIBAgQu0dPZJS2d/IE/xp1X1Ehl8L/JYNqpETl80M5C+0CgCuRAgAJgLVepEAAEEEEAAAQQQQAABBBBAAIGUBaIm7a+2OZiNP3TNwSfXbvP09ag9dpap1RWecxwgUMgCBAALefToOwIIIIAAAggggAACCCCAAAIhENB1/3T9vyDK3SvrPNOOK8pGybL9aoLoCm0ikDMBAoA5o6ViBBBAAAEEEEAAAQQQQAABBBAYTqB/YFA0Cy+I8s7Wdnl+fbOn6eP3ni4TqkZ7znGAQKELEAAs9BGk/wgggAACCCCAAAIIIIAAAggUsMCmlm6JDLy3/p5fj6LTjm9dvtHT3NiKUjnBBAApCIRNgABg2EaU50EAAQQQQAABBBBAAAEEEECgQAR6+gdky46eQHq7ckOLvLWl3dP2qfvNlLEVZZ5zHCAQBgECgGEYRZ4BAQQQQAABBBBAAAEEEEAAgQIUqG/pEtfmu749QWRwUG5f4c3+22lchRyz586+9YGGEPBTgACgn9q0hQACCCCAAAIIIIAAAggggAAClkBnb0SaOvoC0fjHm1tlc0zm4dkHzpLRpYRJAhkQGs25AN/snBPTAAIIIIAAAggggAACCCCAAAIIxApsbO7y7L4b+3mujrv6InLPqnpP9btOGytL5k/xnOMAgTAJEAAM02jyLAgggAACCCCAAAIIIIAAAggUgEBbd7+0dvUH0tP7X2mQ9hl2whEAAEAASURBVJ6Ip+1zD5ojJSUlnnMcIBAmAQKABTaaK1eulCuuuEKOOeYYmTlzplRUVEh1dbXsvvvucuGFF8rTTz+d1hM99NBDcuqppzp1aZ16rOcpCCCAAAIIIIAAAggggAACCORCYOP2rlxUO2yd2zt65W+vNXquO2DOJPnA9PGecxwgEDYBtrYpoBE99NBD5amnnhrS476+Plm7dq3130033STnn3++XHvttVJeXj7kWvvEoFnw9NOf/rRcf/319inrddOmTaL/3XvvvXLxxRfL7373Oxk1ijixB4kDBBBAAAEEEEAAAQQQQACBjAU0CNdh1v8LovxxZZ30D0SdpkeZpL+PLZ7tHPMGgbAKENkpoJFtaGiwejtjxgy59NJL5Z577pEVK1bIc889J1dffbXU1NRYn998883yiU98IumTXX755U7wb+HChXLHHXdYdemrHmu57rrr5Nvf/nbSevgQAQQQQAABBBBAAAEEEEAAgVQFotGo6Np/QZT1TZ3y9NomT9MfXrCzzJhY5TnHAQJhFCgxP3zvhb7D+IQheqYTTzzRyu47/fTTpbS0dMiTNTU1ydKlS+Xtt9+2PnviiSdEswZji36+5557SiQSkQMOOECefPJJqap67w+8rq4uOeyww0SnG5eVlcmbb74pu+22W2w1Izqur6+XWbNmWXXU1dVZU5BHVCE3I4AAAggggAACCCCAAAII5L3AFrPz7rptnb73U0MfVzy4WtZsbnfarhpdKj//6H4yoWq0cy6VN+VlJbJozuRULs2La/j9Oy+GIfBOkAEY+BCk3oEHH3xQzjrrrLjBP61l6tSpctVVVzkVaoZgvPKLX/zCCv7pZ9dcc40n+KfnxowZY53X9xok/PnPf65vKQgggAACCCCAAAIIIIAAAghkLDAwGJX6lmCy/5av2+4J/ulDnLzvjLSDfxk/PDciELAAAcCAByDbzR9xxBFOle+++67z3n6j/+px3333WYcf+MAHZMmSJfZHnlc9//73v986p9eTKOrh4QABBBBAAAEEEEAAAQQQQCBNgYbWbumL+D8JsTcyILc9v9HT22nVFXL83tM95zhAIMwCBABDNrq9vb3OE8WbJrx+/Xqx1xLUab7Jiv25bgqyYcOGZJfyGQIIIIAAAggggAACCCCAAAIJBTQI19jWk/DzXH7wwCsNsr2zz9PEOUtmS3kZIREPCgehFuDbHrLh1XX/7LJgwQL7rfO6evVq571mACYr7s91HUAKAggggAACCCCAAAIIIIAAApkI1Ld0i04B9rtsa++V+00A0F32mD5eFs8tnDX83H3nPQKZCpRleiP35Z/A4OCg/OQnP3E6pusFxhZd/NMuM2fOtN/GfbU36dAPdaOOdIq7nXj3NTY2xjvNOQQQQAABBBBAAAEEEEAAgZAJdPVFRANxQZTbnq+V/oH3Ao8lJSIXHDJXSvQNBYEiEiAAGKLB1s06VqxYYT3RaaedJosWLRrydO3t7+14VF1dPeRz94mxY8c6hx0dHc77VN64g4epXM81CCCAAAIIIIAAAggggAAC4RSo3d5l1pX3/9lWN7TJ8+ubPQ0fvWBnmT15jOccBwgUgwBTgEMyyjr197LLLrOeZqeddpLf/va3cZ+sp+e9NRfKy8vjXmOfrKiosN9Kd3e38543CCCAAAIIIIAAAggggAACCKQi0NbVL63mP7+LTjf+w3O1nmarK8rkzEWzPOc4QKBYBMgADMFIv/HGG3LqqadKJBKRyspKufvuu0WDgPGKfm6Xvj7vIqj2efvVvaFIVVWVfTql1+GmDOsU4MWLF6dUFxchgAACCCCAAAIIIIAAAggUnkDUpP3VNncG0vF/rNkiG5u7PG2fuWimVFcSBvGgcFA0AnzzC3yodVffY445RlpaWkR3/b3zzjvl0EMPTfhU48aNcz4bblpvZ+d7f1APN13YqfTfb4ZbXzD2eo4RQAABBBBAAAEEEEAAAQTCJbCto1c6ewd8f6j2nn65a2W9p91ZZtrvh830XwoCxSrAFOACHvmGhgY56qijRF91AdMbbrhBTjnllKRP5A7MDbdRhzuLjzX9krLyIQIIIIAAAggggAACCCCAgEtg0EzBrWsOZimpu1bWSUdvxNUbs/HHwXOkdBQbf3hQOCgqAQKABTrcTU1NcvTRR8u6deusJ7jmmmvk/PPPH/Zp9thjD+eaNWvWOO/jvXF/vmDBgniXcA4BBBBAAAEEEEAAAQQQQACBIQINbd3SFxkccj7XJ9Zt65C/v7nV08xB8ybLnjMmeM5xgECxCRAALMARb2trk2OPPVZWr15t9f4nP/mJXHLJJSk9ybx582TGjBnWtbpxSLLy5JNPWh/X1NTI3Llzk13KZwgggAACCCCAAAIIIIAAAghYAhr4a2h9bwNKv1gGzZqDNz67QdwbDleUjZJzl8zxqwu0g0DeChAAzNuhid+xrq4uOeGEE2TVqlXWBZdffrl84xvfiH9xnLM6VdieJqwZfsuXL49zlVjn7QxAvV7voyCAAAIIIIAAAggggAACCCAwnIBuvqG78Ppdnnx7m7yztcPT7LL9amRqdYXnHAcIFKMAAcACGnXdtVd3+33mmWesXl966aXywx/+MO0n+OIXv2htGKI3fuELX5Dubu+6DHqs57WUlZWJXk9BAAEEEEAAAQQQQAABBBBAYDiBTrP2XpPZ/MPvomv+3bFio6fZXcZXygn7TPec4wCBYhVgF+ACGvmPfexj8uijj1o9PvLII+WTn/ykvP766wmfoLy8XHbfffchn+u5r33ta6JTh1euXClLly61sgh33XVXeffdd+XKK6+Ul156ybpPr3vf+943pA5OIIAAAggggAACCCCAAAIIIBArsGF7p5iZuL6Xe16slx09MRt/HDJXRpeS9+T7YNBgXgqURE3Jy57RqSEC6U7DnTNnjmzYsGFIPXpicHBQPvWpT1k7B8e9wJzUAOPvf/97GTUq+39g6g7E9s7Cutuwe3fiRP3hPAIIIIAAAggggAACCCCAQP4KNHf2yVub233vYK0JOn7zL695Ao8HzJkkXznm/TnpS3lZiSyaMzkndeeiUn7/zoVq4dWZ/chO4RkUZY81qHf99dfLX//6V2tNQN0YRDMG9VXX/Pvb3/4m1113XU6Cf0UJzkMjgAACCCCAAAIIIIAAAiEWGDRr/mkgzu+iOU03PmM2/nClNo0uLZHzD57jd1doD4G8FmAKcF4Pj7dzuUjWPP7440X/oyCAAAIIIIAAAggggAACCCCQqcDmHT3S0z+Y6e0Z3/fk2m3y1hZv1uEpZuOPaeMqM66TGxEIowAZgGEcVZ4JAQQQQAABBBBAAAEEEEAAAZ8E+gcGZVOrd3NJP5pu7eqTW56r9TS107gKOWmfGZ5zHCCAgAgBQL4FCCCAAAIIIIAAAggggAACCCCQsUB9S7dEBlxzcDOuKb0bdepvZ9+A56YLzMYf5WWEOjwoHCBgBPip4GuAAAIIIIAAAggggAACCCCAAAIZCXSbANwWM/3X7/L8+u2yYkOzp9mlu06R/WdP8pzjAAEE/iVAAJBvAgIIIIAAAggggAACCCCAAAIIZCSwsbnLswFHRpWkeVNHT8Ta+MN927jKMrPxx1z3Kd4jgIBLgACgC4O3CCCAAAIIIIAAAggggAACCCCQmkBbd780d/aldnEWr7pl+QbRtt3lAhP8G1812n2K9wgg4BIgAOjC4C0CCCCAAAIIIIAAAggggAACCKQmsHF7V2oXZvGqV+pa5cm1TZ4addrvIWb6LwUBBBILEABMbMMnCCCAAAIIIIAAAggggAACCCAQR2Bre4909EbifJK7U7re4HVPr/M0UDW6VC5aOldKSko85zlAAAGvAAFArwdHCCCAAAIIIIAAAggggAACCCCQRGBgMCp1zd1JrsjNR3etrJOmDu+U43MOmi1Tqity0yC1IhAiAQKAIRpMHgUBBBBAAAEEEEAAAQQQQACBXAs0tnVLX2Qw18146tc2H1292XNuj+nj5YgP7OQ5xwECCMQXIAAY34WzCCCAAAIIIIAAAggggAACCCAQI6CBv4bWnpizuT+8+8V6MYmHThldWiKf+tB8GcXUX8eENwgkEyAAmEyHzxBAAAEEEEAAAQQQQAABBBBAwBGoa+kSnQLsZ9mwvVOee3e7p8mP7LmL7DKh0nOOAwQQSCxAADCxDZ8ggAACCCCAAAIIIIAAAggggMC/Bbr6IrKtvdd3jz++UOdpc0x5qZy8b43nHAcIIJBcgABgch8+RQABBBBAAAEEEEAAAQQQQAABI1C7vUui/ib/yZrGHfJyXavH/8R9Zkh1ZZnnnJ8HVaODa9vP56StcAkQAAzXePI0CCCAAAIIIIAAAggggAACCGRdoLWrT1q7+rNeb7IKoybaeGdM9t+EqtFy3F67JLstp5+NKhGZP21sTtugcgRyIUAAMBeq1IkAAggggAACCCCAAAIIIIBASAQ0EKfZf36Xlza2yltb2j3NnrqwRipHl3rO+Xkwc/KYQNv381lpK1wCBADDNZ48DQIIIIAAAggggAACCCCAAAJZFdB1/7r6BrJa53CVDWr230rv2n/Tqivkwx/Yabhbc/b52IpSmcHGIznzpeLcChAAzK0vtSOAAAIIIIAAAggggAACCCBQsAK646/u/Ot3edbs+lvX7G33zANmSllpMGGMEmvqb7WU6BsKAgUoEMxPTgFC0WUEEEAAAQQQQAABBBBAAAEEik2gobVb+iL+7vwRGRiUu2Oy/2ZNqpKlu04NjH+6yfyrrmDzj8AGgIZHLEAAcMSEVIAAAggggAACCCCAAAIIIIBA+AT6IoPS2Nbj+4M99Ppm2WqmHbvLWQfOklG6A0cApWL0KJk5aUwALdMkAtkTIACYPUtqKhKBbp/XvigSVh4TAQQQQAABBBBAAAEE8kxAp/7qFGA/i643+KdV9Z4m37dTtSyaPclzzs+D+VPHSmlAwUc/n5O2wi1AADDc48vT5UAgiPUvcvAYVIkAAggggAACCCCAAAIIJBTo6ouIBuP8LLrb8E3Prpdek3loF835O2/JnMDW3ps2rlwmjim3u8MrAgUrQACwYIeOjgcl0N7TLy2dfUE1T7sIIIAAAggggAACCCCAQM4Fard3iYnH+Vpe2NAiqza2etr88IKd5H07j/Oc8+tAk/5mTWbqr1/etJNbAQKAufWl9pAKkAUY0oHlsRBAAAEEEEAAAQQQQEBau/rMf/2+SuhSS394boOnzQlVo+XsA2d7zvl5MH1ClVSUlfrZJG0hkDMBAoA5o6XiMAt09g5IU4e/6fBh9uTZEEAAAQQQQAABBBBAID8EdBquZv/5Xe56sU6aY2Za6dTfsQHtvFtWWiIzJlb6zUB7CORMgABgzmipOOwCdc2aEu9zTnzYUXk+BBBAAAEEEEAAAQQQCFRAd9/t8nnjw/VNnfLIG5s9z713zQQ5ZNcpnnN+HsyYWCVlpYRM/DSnrdwK8G3OrS+1h1igp39wyNb0IX5cHg0BBBBAAAEEEEAAAQRCLqA7/tabnX/9LIOmzeueWudZb3C0yb67aOm8wDb+KC8bJdPHk/3n5/eAtnIvQAAw98a0EGKB+pZu0b+wKAgggAACCCCAAAIIIIBAoQs0tHZLX8Tf328eXb1F1pkMQHc5deFM2WVCcAG4WZOqZJTuAEJBIEQCBABDNJg8iv8CfWZ7+sYdPf43TIsIIIAAAggggAACCCCAQBYFeiMDogFAP0t7T7/cY9b+c5caM/X2pH2mu0/5+r6qvFSmjavwtU0aQ8APAQKAfijTRqgF9C/JyMBgqJ+Rh0MAAQQQQAABBBBAAIFwC+ga535PbvrzS5ukM2a9wU9+cF6ga+9p9l9JCdl/4f62F+fTEQAsznHnqbMoEBmImn8pIwswi6RUhQACCCCAAAIIIIAAAj4KdPRGZFt7n48tijSaRIrH3tjiafPg+VNkwfTxnnN+HlSbHYenVJP956c5bfknQADQP2taCrFAY1u39PQPhPgJeTQEEEAAAQQQQAABBBAIq0Dtdu8afH485+0rNspA9L31BnXjj48tnuVH0wnbmD15TMLP+ACBQhcgAFjoI0j/80JAU+U1ZZ6CAAIIIIAAAggggAACCBSSQGtXn+zojvja5dWNO2RlbYunzY/suYtZey+4jT8mjhktE8x/FATCKkAAMKwjy3P5LtDU0Se6iC0FAQQQQAABBBBAAAEEECgUgbpmfzf+GDRZf7cur/XwjKssk2ULazzn/DzQJf/mTCH7z09z2vJfgACg/+a0GGKB2u1kAYZ4eHk0BBBAAAEEEEAAAQRCJbC9o1d0/T8/yzPvNMn6Ju+U4zP2nyljysv87IanrZ3Mrr9Btu/pDAcI5EiAAGCOYKm2OAXaeyLSZP4SpSCAAAIIIIAAAggggAAC+SwQNZl4dS3+Zv/1RgbkzhfqPCwzJlTKkQt28pzz86B0VInMnET2n5/mtBWMAAHAYNxpNcQCG81agIO6KCAFAQQQQAABBBBAAAEEEMhTgW0mcaG7z9+NDP/22mZp7vTuNnzOQXOkbFRwoYmaSVVSXhZc+3n69aBbIRTgWx7CQeWRghXo7R+Uxh09wXaC1hFAAAEEEEAAAQQQQACBBAKasFDvc/afbjZy/yubPD3ac8Z4WTh7ouecnwcVo0fJ9PHBbTzi57PSFgIEAPkOIJADgYbWbumLDOagZqpEAAEEEEAAAQQQQAABBEYmsKW9RzRxwc9yx4qN0uNq0+y7IecumSMlugNHQGWWmfo7ykwBpiBQDAIEAIthlHlG3wUiA/ovamwI4js8DSKAAAIIIIAAAggggEBSgQGT/bfJ5+y/tVva5cm1TZ5+Hbr7NJk7ZaznnJ8HuvPwNLP5BwWBYhEgAFgsI81z+i6wtb1XOn3eUcv3h6RBBBBAAAEEEEAAAQQQKCiBxrZu6TcJC36VQbPZyE3PbvA0VzW6VM4+cJbnnN8Hs6ew8Yff5rQXrAABwGD9aT3EAubvOdmw3bu9fYgfl0dDAAEEEEAAAQQQQACBPBfoHzDrlbf5u175E29vk3VN3t+LTtu/RiaOKQ9Ma0p1uYyvHB1Y+zSMQBACBACDUKfNohHY0R2RJrO7FgUBBBBAAAEEEEAAAQQQCFpA1yrX5Yr8Kjoj6k6z9p+7zJhQKR/Zcxf3KV/fl5o1/2ZPJvvPV3QaywsBAoB5MQx0IswCtdu7RNfZoCCAAAIIIIAAAggggAACQQn09A/IZp+z//60ql529EQ8j3zBIXOlrDS4UETNpCqpNFOQKQgUm0BwP3XFJs3zFq2A7gbs9yK7RYvNgyOAAAIIIIAAAggggEBcgXqz8YefeQm6KeIjb2z29OWAOZNkn5kTPef8PKgqLxXNQKQgUIwCBACLcdR55owFeiMD8uCrjWlv7qEL7Xb3DWTcLjcigAACCCCAAAIIIIAAApkK6FRcP5cmipoF0f9gNv5wBxxHl5bIuUvmZPoIWblv3tSxUlJSkpW6qASBQhMoK7QO018EghDQv8D+9tpmufLhNbKxuUu2mR1+zzko9b+89C8+3RBkwfTxQXSfNhFAAAEEEEAAAQQQQKCIBfR3GN2k0K+yckOLvN6ww9PcifvMkJ3HB5d9N21cuUyoYuMPz6BwUFQCZAAW1XDzsJkKXP3Y23LJ7aus4J/W8fDrm2XrjvR2z2rt6pfmzr5Mu8B9CCCAAAIIIIAAAggggEDaAm3d/aK/i/hVdNbUzcs3eJqbMrZcTt53huecnwdlJvtw9uSxfjZJWwjknQABwLwbEjqUjwJnLJopmrJul4hJ6bvjhY32YcqvtSYLcNCdB5/ynVyIAAIIIIAAAggggAACCKQvUGey//ws9760yUw39iY+6OypIDfemDVpjJSXEf7w83tAW/knwE9A/o0JPcpDgTlTxsr5B8/19Gz5umZ5e0u759xwBz39g9Jg1gOkIIAAAggggAACCCCAAAK5Ftje0SvtMbvw5rLNxtZuecCsme4ue5hlkJbMn+w+5ev76ooyM/W4wtc2aQyBfBQgAJiPo0Kf8lLgC0fuNmTNiFuX15q1NNJbTKOhtUd0Z2AKAggggAACCCCAAAIIIJArAf09pc7s/OtX0fZuNBt/DLhmPJWaDTcuXDo3sI03dL+PuVPHBNa+X/a0g0AqAgQAU1HiGgSMwMQx5aJBQHdZu7VDnl/f7D417Hv9C7Guxd80/GE7xQUIIIAAAggggAACCCAQKoGtZuPC7r4B355Jfy96bVObp73j995FZprpt0EV3XRkXCUbfwTlT7v5JUAAML/Gg97kuYBOA94lJn38jhUbpX8gvYw+3UW4szeS509L9xBAAAEEEEAAAQQQQKAQBTTpoN7HpIOe/gG5xcyOcpfJZuOP0/af6T7l6/vyshKZNanK1zZpDIF8FiAAmM+jQ9/yTkAXjj13yRxPv/Rf1h55Y7Pn3HAHOmu4djtZgMM58TkCCCCAAAIIIIAAAgikL9Bg1uLri6S3VFH6rbx3x59W1Utzp3fjj/PN701Bbvwx16zjXlZKyOO9UeJdsQvw01Ds3wCeP20BXcD2/TuP89ynO1219/R7zg130NbdP+QvyeHu4XMEEEAAAQQQQAABBBBAIJlAb2RAGtt6kl2S1c800/Ch17wJEXvXTJDF84Lb+GPS2NEypZqNP7I60FRW8AIEAAt+CHkAvwVKzEqysVmAnWZtjT+v2pR2VzY2d6W9iUjajXADAggggAACCCCAAAIIFI1AXXO3ZyOOXD64tfHHM2bjD53i9O9SNsps/HFIcBt/lJr2NfuPggACXgECgF4PjhBISWC3narlkF2neK59bPUW0W3v0ym6KO/mHf7961w6feNaBBBAAAEEEEAAAQQQKCwBXWe8qaPXt04/++52Wd24w9PeifvMkOkTg1t7b6ZZ9y/IqcceDA4QyCMBAoB5NBh0pbAEzj5wlowuNfvK/7vov3r94bkNaWf0bWrplkiam4jYbfKKAAIIIIAAAggggAACCNgCus64KxnPPp2T166+iNz6vHfjj6nV5bJs4YyctJdKpWMrSmX6hMpULuUaBIpOgABg0Q05D5wtgWnjKuUje+7iqe6V+jZ5YUOL59xwB/0DukNXepmDw9XJ5wgggAACCCCAAAIIIFBcAq1dfaLrjPtV/vRivbR2edu74OC5UlFW6lcXPO2YlZpk/rRq0SWbKAggMFSAAOBQE84gkLLAsoU1MnHMaM/1mgXY0z/gOTfcgU4D1sV6KQgggAACCCCAAAIIIIBAugK6Fp9m//lVdC3zh9/wbvyx36yJsmjOJL+6MKSdXcZXSnVF2ZDznEAAgX8JEADkm4DACATGlJeJbm/vLs2dffKnVfXuU8O+1zT9hlbWAhwWigsQQAABBBBAAAEEEEBgiMC29l7pMuuL+1E02HjD0+tl8L19P6ylkT4R4MYfVeWlMnvyGD8enzYQKFgBAoAFO3R0PF8ElsyfInuZbe7d5aHXNkud+VexdMpWkwXYz1qA6ZBxLQIIIIAAAggggAACRS8wYCJxdT4uKfTU2iZ5a0u7x/3kfWfIziYDL4iiM351k8ZRZvdfCgIIJBYgAJjYhk8QSElA15i4yPxrl253bxfdEOSGZ9antSGI/gva5jayAG1DXhFAAAEEEEAAAQQQQGB4gYbWbumLDA5/YRau0F2Gb1ux0VPTTuMq5OR9azzn/DyoMTsOM/XXT3HaKlQBAoCFOnL0O68EdJv7k8y/ernLms3t8qT517F0iq4FyI7A6YhxLQIIIIAAAggggAACxSug64hrANCvctfKOtkRs9HIBSYZorwsmNDCuMoymTmpyq/Hpx0EClogmJ/Sgiaj8wjEF1i2X43ov365y+3P10pHT8R9Kun7iNkReItZv4OCAAIIIIAAAggggAACCAwnoMsOudfiG+76kXy+vqlTHntzi6eKA8ymH/vPDmbjj1IzA0un/rLrr2dIOEAgoQABwIQ0fIBAegL6r1667b277DDBvztf2Og+Nez7zW3dMujX3+LD9oYLEEAAAQQQQAABBBBAIB8F2nv6ZVt7ny9dGzRLHN1oLXH0XnPlpaPk/IO9GyK+92nu382ZMkYqR5fmviFaQCAkAgQAQzKQPEZ+COxv/gVM/xXMXf6xZqu8ZaYDp1r6IlHZShZgqlxchwACCCCAAAIIIIBAUQrUbk9v08GRID3x1jZZu7XDU8WyhTUybVwwG39MHDM6sE1HPAgcIFBAAgQAC2iw6GphCOgaGBWuNTDM3h5y7VPr0trht8FkAUbNv7JREEAAAQQQQAABBBBAAIFYgaaOXmlPY6mh2PvTOdYljW6P2fhjF7Pj74n7TE+nmqxdq1N/d51WnbX6qAiBYhEgAFgsI81z+iYwtbpCzlg009PeJrMw730vb/KcS3bQ2z8o28xf6hQEEEAAAQQQQAABBBBAwC2gywVtNGv/+VV0SaMOs/uvu3zCJD2MNlOAgyjTJ1QGtulIEM9LmwhkSyCYn9hs9Z56EMhTgeP2mi7zpo719O7elxtEF+lNtTS09qR6KdchgAACCCCAAAIIIIBAkQjobCFNGPCjvLutQ3RJI3dZPG+y7DtrovuUb+/Ly0pkxkR2/fUNnIZCJUAAMFTDycPki4CmpX/60PliXpwyYP6lTqcC6wK6qZTuvgHZThZgKlRcgwACCCCAAAIIIIBAUQj0RQbFr0QBzTS84en14v7tRZc6On/JnMCsZ04aI/q7FgUBBNIXIACYvhl3IJCSwNwpY826GDM81+rCuY+v3uI5l+ygroW1AJP58BkCCCCAAAIIIIAAAsUkUNfSJZpY4Ef5x1tbZV1Tp6ep08zGH1PMkkdBlKryUtlpXDBtB/G8tIlAtgUIAGZblPoQcAmcvv9MszuV9y+pO8waGqlm9mkW4JYdrAXoIuUtAggggAACCCCAAAJFKdDVF5Ft7f78brCju1907T93mTGxUo7fO5iNP7QfsyePkZISsv/cY8J7BNIRIACYjhbXIpCmQLlJkf/Uh+Z77uox63Xc8IxJpU9xKnC9+Ve+yIA/a3x4OsoBAggggAACCCCAAAII5I1A7fYu8zuEP93R4F9n74CnsQsPmSdlAW38Ma6yTCaPLff0hwMEEEhPgABgel5cjUDaAnvOmCBHvH+a575VG1tl+bpmz7lEB/0DUd/W+UjUB84jgAACCCCAAAIIIIBAcAJtXf3Sav7zo7y9pV3++dY2T1MH7zpF9qqZ4Dnn58HsKWP8bI62EAilAAHAUA4rD5VvAh8/aI5MqBrt6dYtyzeITvFNpTSanb56+lO7NpX6uAYBBBBAAAEEEEAAAQQKR6C22bsWX656bm38YWYruUvl6FFyrvl9JqiimX/jK72/SwXVF9pFoJAFCAAW8ujR90AEqivS/8unuqJMLjh4rqe/LeZf8P78Ur3nXKIDXee3rrkr0cecRwABBBBAAAEEEEAAgZAK6Lp/sdNxc/Woj725RXSqsbucsf+swKbf6pJ/uvYfBQEERi5AAHDkhtRQZAJzTPp5JjvPL5k/WfaOSZt/6LXNKQf2mjr6pKM3UmTaPC4CCCCAAAIIIIAAAsUroBl5G31KBGjt6pO7VtZ5sGdOqpJj99rZc87Pg53HV4ru/ktBAIGRCxAAHLkhNRSZQOXoUpkxsSrtp9Ydqy48ZK6UuaKHA2YV33Q2BNnQ5E/qf9oPxw0IIIAAAggggAACCCCQdYHGHT3SF/FnQ8DbV2yUrpglii5aajb+GBVM2ECnHpP9l/WvFBUWsUAwP8lFDM6jh0OgxgQAK8xfSOmW6ea+E/eZ4bltzeZ2eebd7Z5ziQ7aeyLS3NmX6GPOI4AAAggggAACCCCAQEgE+gcGzWaA3b48zZrGHfLU2iZPWx/cbaosmD7ec87Pg/nTqqXUlTzhZ9u0hUAYBdKPYIRRgWdCIE2BUeYvojkZrkWxbOEMmVrt3cL+1uW15l/bUpveW7u9U3QqAAUBBBBAAAEEEEAAAQTCK1Df0i2Rgdz///7I4KA1K8ktWWVmPZ1z0Gz3KV/f7zKhcsgmir52gMYQCKEAAcAQDiqP5I/AlOqKjP5SqigrlQvMVGB3aevul7tXprYhSE+/+ZdAsyswBQEEEEAAAQQQQAABBMIp0NM/IFvM9F8/yqNvbJE6E2x0l7MOmCkTx3iTFtyf5/I9U39zqUvdxSxAALCYR59nH7HAvKljRXemSrcsmj1JFs6a6LntkdWbzY5bqa3x19DaI/p/CigIIIAAAggggAACCCAQPoFNZuqvWS4850WXF7rnRW8igs50OnqPXXLedqIGmPqbSIbzCIxMgADgyPy4u8gFdEeq6SY9Pd2iG4JoFuDo0veih/oX/I3PbDB/0Q//N/2AmQJcu70r3Wa5HgEEEEAAAQQQQAABBPJcQP+hf1t7ry+9vNNs/NEdk1hw0QfnBbb2HlN/fRl2GilSAQKARTrwPHb2BHRDkPKy9wJ5qdasW9qfsl+N5/K3trTLGw07POcSHei/1rV2sSFIIh/OI4AAAggggAACCCBQiAJ+Zf+9u61DnnrHu/HHYbtPk913HhcIG1N/A2Gn0SISIABYRIPNo+ZGoKxUt6cfm1HlJ5kdgaeZtQTd5S8vbXIfJn2/vokNQZIC8SECCCCAAAIIIIAAAgUk4Ff2n846uuW5Wo+MbvzxscXBbfzB1F/PcHCAQNYFCABmnZQKi1Fg2rgKmTR2dNqPXl42Sk7eb4bnvtWNO2TN5tSyANkQxEPHAQIIIIAAAggggAACBS3gV/bfivXNorOP3GXZwpqMNjl015Hp+6nV5YG1nWmfuQ+BQhMgAFhoI0Z/81Zg/tRqKXOt6ZdqRzXNftL/Z+9OwOyqykTvv6l5nlNJpapSQyYMSZghBBJkFFAkIAqijCIOzW2v2j62bV/73qufn3zYAv15W2VQUERABBm0ZRICgRASAhlMyFhVqarUPM8jd70nVuWsSp199jl1zqnpv54n1tlrr7323r+Dya6113rfJHvw8I8BzAKsNhm7SAjiVpt2CCCAAAIIIIAAAghMTYFIzf4bGBqWR03sP++SayY0XHri5CT+iI6aIwuzk7wvh88IIBAGAQYAw4BKl7NTQGfzFQXxD1esWUJ8xUn2LMDtVW2iMTncFJMPRMpdZg920x9tEEAAAQQQQAABBBBAIPICkZr995ddtVI/JsmILv3V32cmoyzISJD4mOjJODXnRGBWCUzO/8NnFTE3O5sEclMTgloKfMEJuZKWEGNRBTILsKVrQFpMUhAKAggggAACCCCAAAIITD+BSM3+a+sZkLExx5eZpB9nlWRNClp8bJQsSE+clHNzUgRmmwADgLPtG+d+wy5QkpMc8FJgfeP18ZV51rVtrWiRw83dVp3Ths4CHNbpgBQEEEAAAQQQQAABBBCYVgKRmv335LtV0jMwZNnccHaRzJkzx6qL1EZRVpJEmSXAFAQQCL8AA4DhN+YMs0xAB/OCWQp80fJ5khxvT31/5n33GYE1IUhNe+8s0+Z2EUAAAQQQQAABBBCY3gKRmv1XaSYXvPJBnYV17uIcWTQ3xaqL1EZaYoxkp8RH6nScB4FZL8AA4Kz/TwCAcAgEsxQ4KS7GBN61ZwFuOtQkNa09ri9RE4L0Ddpv9FwfTEMEEEAAAQQQQAABBBCIuMAR87z/YZgX8nxoTvDI5grrPHEmFvl1ZxRG/H71hDrhUFdOURBAIHICDABGzpozzTKBYJYCa+atxNhjswD1QeCZ7Udcyw2ZJcD6Zo+CAAIIIIAAAggggAACU19AX943jEnIEY6r3na4VXaYRIPe5ROr8iZtBt68tATRCRAUBBCInAADgJGz5kyzTECXAhdnB/ZWK8UkArnYLAX2Lhv3N0p9AEt7Gzr6pb13wLsLPiOAAAIIIIAAAggggMAUFKhp7ZVwh/HuHxyWX28qt+4+IylWrjhpgVUXqY3Y6DlSkEnij0h5cx4ERgQYAByR4CcCYRCYmxovOSlxAfV8uUkGotPxR8qQmQb4uy2HRzZd/axo7DbT+8O8jsDVldAIAQQQQAABBBBAAAEExhPQgbm6AF70j9eHm7rndxyR+jGzDK8/c6EkeK08ctNPqNosNIk/Yr1+3wlVv/SDAALOAsdGGZzbsRcBBIIU0KXAcTHu/6+WnhgrF34k1zrb24eaZVe1PWXfajBmo7NvMCJLCcaclk0EEEAAAQQQQAABBBBwKVDbFv7Zf42dffLM+3ZIoWXzUkWTf0xGSTUrnnLN8l8KAghEXsD9qETkr40zIjAjBGLM263FAWbWuuqUfEmJt2NiPLypXAaHh12bHDaxAAeH3Ld33TENEUAAAQQQQAABBBBAYEICA+Y5vTYCs/8eebtC+r1+J9DkGzefU2yScJgPES5R5pSTlXE4wrfK6RCYkgIMAE7Jr4WLmmkC6SbGRl66+zddqQmx8pnTCyyGKpPh96XddVad08bA0Ieix1AQQAABBBBAAAEEEEBgagno7D9N4BfOoiuINpc1W6e48IR5AccptzqYwMaCjERJjDuW8HACXXEoAggEIcAAYBBoHIJAMAIa6yKQf/D0H+ei7CTrVL/fWiWt3f1WndOGvlXs6R9yasI+BBBAAAEEEEAAAQQQiKCArtIJ9+w/XTn00Fvl1l3pCqNrTy+06iK1kRAbJflmAJCCAAKTJ8AA4OTZc+ZZJhBl5rwvzk0x0+3d3bi2v2VNidW4Z2BIHttSadU5bWgekPKmLqcm7EMAAQQQQAABBBBAAIEICujg36BZrRPO8uLf6qS61V4NdO0ZhZJiYvBNRinNSRH9/YaCAAKTJ8AA4OTZc+ZZKKBv3QJJeb9s/vEBejfsa5AD9R2u9Vq7B6S5y/2sQdcd0xABBBBAAAEEEEAAAQQCEtBlv7r8N5xFVww9+W6VdQpNTHjBMjvRoNUgjBtzU+NFQyJREEBgcgUYAJxcf84+CwV06ntyvPvYF9eftVB0yrx30en8wzq9z2WpMLMAh8McY8TlpdAMAQQQQAABBBBAAIFZK1BnZv9prO5wlkc2HxZdOeRdbl5TPCkz8GKj5xwX1sj7uviMAAKRE7BHFSJ3Xs6EwKwV0Ixbmv3K7VLgzKQ4ufoUOyHIwYYueW1vg2vD3oFhqYlAljHXF0RDBBBAAAEEEEAAAQRmmYDO/qtps5flhppgR1WrvHmg0ep23ZIcWTov1aqL1MZCE9M8Npphh0h5cx4EnAT4f6KTDvsQCJNAslkKHEgQ3MtWzJcFY7II/+6dwwElBKk2GYH7B4fDdEd0iwACCCCAAAIIIIAAAk4CVS3d5nk8fLP/9Fn/V2+WW5eQbLLufvbMhVZdpDYyzLLf3NSESJ2O8yCAgB8BBgD9ALEbgXAJ6ACg26zAMeat2U1m2r536ewblIc3lXtXOX7WN46Hm7sd27ATAQQQQAABBBBAAAEEQi/Q0z9kZv+FN/bfM+9XH5dd+Doz+JdhVhRFukSbhB8ad5CCAAJTR4ABwKnzXXAls0xAs2AtmpvseinwqoIMWV2aZSm9fahZtpQ3W3VOGw0dfdLRO+DUhH0IIIAAAggggAACCCAQYoGyxi4JIIR3wGfXjL/PbD9iHbckN0UuOGFyEn8UmaW/CbHu455bF84GAgiERYABwLCw0ikC7gRSE2Jlfpr7afE3nV0smknYu/xyY5nobEC3paKJWYBurWiHAAIIIIAAAggggMBEBZo6+6StJ3wv4T80I4sPbjwkuuJnpESbgOO3rS2VKLeBx0cODMHPtMQYmRfA7zghOCVdIICACwEGAF0g0QSBcAoUZiVJ/Jgsv77Op9P3bzy7yNrdah4mfvt2hVXntNHROyj1HeFdfuB0fvYhgAACCCCAAAIIIDBbBHRQrjzML+Bf398ge2o6LNLLV86Xheb3jEgXXfqrCQ8pCCAw9QQYAJx63wlXNMsEPP9I5rj/R/LcxTlycmGGpfTavgbZWd1m1TltVJpYgINDJARxMmIfAggggAACCCCAAAITFQh3Ir52E97nkbcPW5c5NyVerj61wKqL1EZBZiJLfyOFzXkQCFCAAcAAwWiOQDgE0k2GLM2S5abM0en855aYf1jt//ve//oh6R0YctOFJ/sYCUFcUdEIAQQQQAABBBBAAIGgBPTZvKatJ6hj3R706ObDx4UDuuWc4kkZhEtNiJG8dPfhjdzeI+0QQCA0AvYIQmj6pBcEEAhCQN+WuS3Z5q3e9Sajl3dpMLFFHt9a6V3l+LneJAQJJHagY2fsRAABBBBAAAEEEEAAAUtAE394heWz9oViY5dZAbTBrATyLmeVZMkpCzO9qyLy2az8lVJPgkPzgYIAAlNSgAHAKfm1cFGzUUATgqQnupsFqD4XfmSenDA/1aJ6YVet7Kuz439YDbw2NAtZWYNmIzsWLNhrNx8RQAABBBBAAAEEEEAgSIGWrn5p7Q5f4o++wSG5/41D1tUlmqy7N5qkgZNR8s1khqQ4O1nhZFwH50QAAd8CDAD6tmEPAhEXKMhyPwtQM3rdvq5UYqOPvWXToTx9EHAb309nAOpMQAoCCCCAAAIIIIAAAgiERkBfsIc73M6T71Yd9xz/2TMLJSs5LjQ3EUAvKfExkp/h/veYALqmKQIIhFCAAcAQYtIVAhMVSDOzANMS3b85y0tPlGtOK7ROW9XSI89uP2LVOW3ow0n/IAlBnIzYhwACCCCAAAIIIICAW4HGzn7p7ncXm9ttn97tdGnxn3bWeFfJsnmpnhVCVmUENnTp76LcZNE45RQEEJjaAgwATu3vh6ubhQIFmUkB3fXHV+ZJcbZ9zNPvVcuRVncBhweHwv+GMqAbojECCCCAAAIIIIAAAtNUQGf/VbV0h+3qB4eH5RevHzRhfI6dIsaMwn3RrAzSFUKRLiz9jbQ450MgeAEGAIO340gEwiKgcQADmQUYrf/gry01b92OXc6giTasS4GHvZ8Mju0+7lODWQbc3hu+GCXHnZAKBBBAAAEEEEAAAQRmoICG1+kdCN/qmj/vqJGKJnuA8apT8idlCS5Lf2fgf8Dc0owWYABwRn+93Nx0FSjIsGf0+buP0rkpcvmKPKvZB7Ud8ureeqvOaaPcLCWgIIAAAggggAACCCCAQHACw+YlvIbjCVepaeuRJ7dVWd0XmuQbnzxpgVUXiQ2W/kZCmXMgEFoBBgBD60lvCIREID0pVlIT3McC1JNec1qBzE2Jt87/6ObD0tLdb9X52ujqG5KmThKC+PKhHgEEEEAAAQQQQAABJ4Ha9t6wxdbWlT26wmfAhO8ZKboASJMCxkRH/td6lv6OfAv8RGD6CET+b4rpY8OVIjCpAgXmbV4gJSE2Wm5bW2IdosGHH36r3Kpz2gjnG0un87IPAQQQQAABBBBAAIHpLDBkZv+5jcEdzH1u2Nsge2o6rEMvXTFfFuemWnWR2GDpbySUOQcCoRdgADD0pvSIQEgEMpLiAp4FuKogQ85dnGOdf3NZs2ytaLbqfG3ogCGzAH3pUI8AAggggAACCCCAwPgCOvjnPTtv/FbB1Wqs7kffOWwdnJMSJ585vdCqi8SGxh0vnUvW30hYcw4EQi3AAGCoRekPgRAKFJnsvt7JPdx0fcPqItG3ct7lV2+WS3f/oHeVz886C1Czl1EQQAABBBBAAAEEEEDAv8DA0LDo8t9wld+ZsD6dffaz/BfOLRFdARTpsiA9UZLH/K4R6WvgfAggEJwAA4DBuXEUAhERSE2IlXlpCQGdK81kEdZBQO/S3NUvj2+p9K7y+dkzC9C0pyCAAAIIIIAAAggggIB/gZrWXhn0is3n/wj3LT6obZfX9jVYB5xVkiUnF2ZadZHYSIyLlkDDFEXiujgHAgi4E2AA0J2Tz1bPPfec3HDDDXLZZZfJV7/6Vdm2bZvPtuxAIBgBzewVFxPY/1XXLsmRlfnp1ule2l0n++rsuCFWA6+NamYBemnwEQEEEEAAAQQQQACB8QX6B8M3+29weFh+ubHMOnFCbJTceHaxVRepjZKcZInS9L8UBBCYlgKBjSpMy1sM/qJfffVVyc3NlYULF0pra+txHf2P//E/ZP369fLoo4/Kiy++KL/4xS9k9erV8pvf/Oa4tlQgEKyAZvUqNkuBAylzzLphXRYQ55URTBf1auawQbNEwV9hFqA/IfYjgAACCCCAAAIIICCexB+aACQc5b921kqleTHvXT59WqFkJcd5V0Xkc25avKSblUYUBBCYvgIMADp8d3/+85+lsbFRzjjjDMnIyLBa7tixQ374wx96YqVpvDTdrz8HBwflS1/6kpSXl1vt2UBgIgLZKfEB/0OvS4evOa3AOq3G93t2+xGrztcGsQB9yVCPAAIIIIAAAggggICIzv6rC1Psv8bOPvnDtiqLWeODf+zE+VZdJDbiYuZIUVZgExIicV2cAwEEAhNgANDBa+PGjSYBwxy56KKLjmv1s5/9zDPgl5mZKe+++640NTXJO++8I1lZWdLX1yc///nPjzuGCgQmIlCckyTRAU65v3xl3nGzB59+r1qqTZYyf6VHMwITC9AfE/sRQAABBBBAAAEEZqmAPlOHafKfPPxWufSZAcaRogtvv3BOScC/D4wcP5GfxdnJoquSKAggML0F+H+xw/dXU1Pj2XviiSce1+r555/3DA7ecccdcsopp3j2n3766aLbOhPw5ZdfPu4YKhCYiEB8TLQUZiUG1IUOGH5xbamVSXjQPKU8YJYCD5v/Tv0VZgH6E2I/AggggAACCCCAwGwU6Bsckvowzf7bVtEiW80f73LBCbmyZF6qd1VEPutyY12NREEAgekvwACgw3fY0HA029LY5b8HDx6U6upqz5FXXXWV1cPatWs929qGgkCoBeabZb0p8TEBdVs6N0UuX5FnHfNBbYe8+kG9VTfehs4CbOjoG28XdQgggAACCCCAAAIIzFoBTZoXjtl/AyZe98Obyi3XtIQYue7MhVZdJDZioueIJv6gIIDAzBBgANDhe9SZfFra2tqsVm+88YZnOz09XU4++WRrX3Z2tme7u7vbqmcDgVAI6JL00rnJ1ow+N/1qLMC5Y97cPfrOYWl2scT3cHO3q8Qhbq6DNggggAACCCCAAAIITHeB3oHwvSR/eU+d1I95Af+5s4oCngQQCmON+xcXw5BBKCzpA4GpIMD/mx2+hfnzjwZY3bNnj9XqhRde8Gyfc845Vr1udHV1eeo0NiAFgXAIJJsZgAWZgS0FToiNltvWlliXo5l+f/VmmWfJurVjzMbA0IfHZR8b04RNBBBAAAEEEEAAAQRmjUC4Yv919w/KU9uOrjQbwVw6L0XWLskZ2YzYz4ykWMk1q48oCCAwcwQYAHT4LlevXu0ZHNGEHyMz+g4dOiTPPPOMJ/7fxRdffNzR+/bt89SNDB4e14AKBEIgkJ+RKKlmKUAgZVVBhqxdbD88aGyRd8qa/Xaj2c26+gb9tqMBAggggAACCCCAAAIzWSCcs/+e235EOsc8c+vsP10FFMmiccRZ+htJcc6FQGQEGAB0cL7ttts8e3fs2CErVqyQa665RnRQsLe3VxITE+X6668/7ujXX3/dU7d06dLj9lGBQKgE9CFgcW6KycYV2MPA588uEo0h4l1++Va5dPY6D+7paviyxqOzW72P5TMCCCCAAAIIIIAAArNJ4GiSvNDfsYbm+fPOWqvj04syZekkJP4oyk4SXUFEQQCBmSXAAKDD93nBBRfI1772Nc8swPLycnn66aelsbHRc8Rdd90lOTn2bCodGByZHbhu3TqHntmFwMQF9B/l4uzAgvKmJcTKTWuKrZO39wzII5srrLrxNjrMIGF9R+94u6hDAAEEEEAAAQQQQGDGC+jsv8bO8CTI+8O2Kuk3CUBGipmENymJP9ISY2QeS39HvgZ+IjCjBOypQDPq1kJzM3fffbdceOGF8vvf/15qa2slLy9PbrzxRtHBwbHl2WeflbS0NNHkIFdcccXY3WwjEHKBuanx0tbTbzL19rvu++zSbHnzQJNsO9wyesyGfQ2yZlG26DJhp1JpEoJkJcWZmYe8O3ByYh8CCCCAAAIIIIDAzBOoaes1k0NCf1+aUfjVvfVWx+cvyxUN+xPJokt/F81NieQpORcCCERQYI7JdBuGv8IieAecaloKVFVVSWFhoefaKysrpaCgYFrex1S46EHzpnBHdZv0DRx7Y+jvuprMm8tvPblDesxbzJGSkxInd11zkt/p/nnpCVKcE9jMw5Fz8BMBBBBAAAEEEEAAgekooM/c2w63ytBw6H99/vcX94rG5h4p8Sbz7t3XniyZ5sV7JIvG/ZtvnvUpM0+A379n3ncazB0xjScYtUk8pr6+Xp5//nn53ve+J5dddplnGbLGg9M/N998s6sre+ihhzztR45z+qltKVNbQGfj6Zu6QGIDZ6fEy+fOWmjdWGNnvzy+tdKqG2+jloQg47FQhwACCCCAAAIIIDCDBRrMC/RwDP7tre2wBv+U8LIVeREf/NMEgwz+zeD/gLk1BIwAS4CD+M9gcHBQWlqOvqHJzMyUmJjIMc6bNy+IK+aQmS6QnhgrC9ITpbq1x/Wtnn9Crrx5sFH21HSMHvPCrlrRJcJOwYZHEoKsyE8fPY4PCCCAAAIIIIAAAgjMVAFdNFdrlv+Gumi/vx0Ti1sH4q44KS/Up3LsT+MNsvTXkYidCMwIgciNXE1zrt27d8vPf/5zefnll2Xfvn2exCB6Szp7bsmSJXLRRRfJl770JU+24Ejd6sKFC+WEE06QF198MehTvvDCC7JgwQKfx7M01yfNlNtRkJkorSYeYFffsWW9ThcZZf7b/eLaUvn2H3bIwNDRpQz6vw+brMA/WL/C89+2r+NHEoLkprJEwJcR9QgggAACCCCAAAIzQ6C1e0B6Awi34/aut5a3yP76Tqv51afkS1JcZH9NX2BiDSbGkfXX+iLYQGAGCkT2b5ZpCDg8PCzf+ta35D/+4z9EP48Nmajbe/fu9QwK6gDhHXfcIf/+7/8uUVHhWV2tS3/POOMMzx+dDajZiUtKSoKWXbp0qRQXFwd9PAdOHYEo8+pucW6K7KxqE7ehSfLMrMFPn1Yoj75zePRGDjV2ybsmBsnpxVmjdeN9ONzU7VmaEEtCkPF4qEMAAQQQQAABBBCYIQKa/CPURZcTP7bl2DO49p9rEvxd9JHIrviKj42KeLKRUFvSHwIIuBNgANCP0/XXX+/JADwy8HfiiSfKmWeeKSNLcevq6mTLli2ya9cuGRoa8gwUHjlyRB5//HE/PQe3+3/9r/8V3IEcNSsE9G1hQVaS6OCc23L5yjxP1jHvB5vfv1slpxZlis4S9FV01qBmBS4lU5gvIuoRQAABBBBAAAEEprlAd/+gtPUMhPwuXjNZf4+MGVj8zOmFovG9I1mKs5PN5BXfz/yRvBbOhQAC4RVgANDB97HHHpMnnnjCsxTypJNOkvvuu88z8268Q3QQ8Mtf/rK899578uSTT4oee911143XlDoEwiqwwGTuaunqF12m66ZEm3/wP3Vqgfz01QOjzQ+bgb13yppltYkH6FTq2vtkrnlTmZoQ69SMfQgggAACCCCAAAIITEuBcMT+6x0YkifNC3fvohl4z17k/Ozt3T4UnzOTYyUrObKZhkNx3fSBAALBCUT29UJw1zhpR+mAnxZdJrtx40afg3/aRpflvv7667Js2TLPMuFf/OIXWk1BIOICGpdSlwLrwJ7book/8k3sD++iDyXDLtYSl5klwyMzZL2P5zMCCCCAAAIIIIAAAtNZYHBoWBo7+0N+C3/eWWNid9uzCq8/c6Hj6ptQX4T+qqCz/ygIIDB7BBgAdPiut2/f7pn99+1vf1uSk/3/5ahttK0WPXY6lFtuucWTBCQuLk5ycnJk9erV8q//+q9SXV09HS6ur8fqAABAAElEQVSfa/QhkBAbLUXZST72Hl+t0/4/fXqBtUMzCmuWYH9Fk47Utoc+Loq/87IfAQQQQAABBBBAAIFwCtR39InG6gtlaTcDf8/vqLG6XFWQLivy0626cG/kmwSC+jsDBQEEZo8AS4Advuv+/qNve1atWuXQyt410nZgwH6jY7eaOluvvfba6MU0NTWJ/tm8ebMnkck999zjyWw82iCAD1VV9pT2sYfW1Nj/6I3dz/bEBealmaXA3f1mObC7/xbPMEk/dNCwwit+4FPbqmXNohy/swmrWnokOzle4mJ4pzDxb44eEEAAAQQQQAABBCZbQFe4hOMl99PvVUuPWQI8UnTNjs7+i2TRjL8LTDJACgIIzC4BBgAdvu+ioiLZs2ePtLW1ObSyd7W3t3sq9NipXEpLS+Xqq6+Ws88+WwoLCz2XeujQIfnDH/7giWHY29vriWmoy0lvv/32gG9lpM+AD+SAkAqU5qTIjt5W0YQd/oom/NCMwD9+ce9oU33oeX1/g5y/LHe0brwPg6b/iqYuWTIvdbzd1CGAAAIIIIAAAgggMK0Emk1M7b6B4ZBec515tn5pT53V5zmLc8xLeP+rzayDJrhRQuKPCQpyOALTU4DpOg7f26c+9SlPbDMdFHNbNAGIDppdddVVbg+JeDu9tgMHDshdd93lGQTU+IX659prr/UkPXn22WclNvZoUoevf/3rUltbG/Fr5IShEdAZeYsCyNJ76sIM095+AHlqW5Vo/BN/ReOjtJoZhxQEEEAAAQQQQAABBKa7QM2YDL2huJ/Ht1ZaS4pjTBiez4wJwxOK8zj1MTc1TtKTSODnZMQ+BGaqAAOADt/sN77xDdGZcprQQ7MB+ys6+KdtS0pK5J/+6Z/8NZ+0/enp6Z5BSl8X8IlPfEK+973veXZ3d3fLgw8+6Kupz/rKykpx+vPOO+/4PJYdoRXINJm95qXFu+pUB68/c/rRGaEjB+jA3qt760c2HX8ebOiyHmocG7MTAQQQQAABBBBAAIEpKNBm4vR19A6G9MoONnTKpoNNVp+XnDhf5qYmWHXh3IiLmUPij3AC0zcCU1yAAUCHL0gHyl5++WU59dRT5bOf/aysX79e/vjHP3oSZGiMv8HBQc9nrdNZdTqDTtu+8sorosdO56LLfnUwSMuGDRsCvpWCggJx+pOXlxdwnxwQvIBm+EoysT7clJUmAPGyMUt5NVZJ3+CxWCW++ukfHPYsBfa1n3oEEEAAAQQQQAABBKa6QFVLd0gvcdgkEvnNpgqrT302X3/yAqsu3BslJjxQTDRDAOF2pn8EpqoAMQAdvpno6GMDJhoE9rnnnvP88XWIttm6datn1qCvNjqopgOHU73k5uZKdna2NDY2khF4qn9ZLq5Ps/wuzk2RXdVt4i+R2dFZgAXy/T/tGe25pXtAdBDwujP8Byiua++T7JR4SU9kacEoIB8QQAABBBBAAAEEpoWAzv5r7wnt72vPbj8ie+s6rPu/8qQFkpoQuefl7JQ4yTIrgygIIDB7BRj+d/judUBv5I82G/ns66ebNnrsdCkjMwCny/Vync4CyfExstBk+XVTli9IlxUL0qymz2+vkcpmd29DD5klDkP+Rhqt3tlAAAEEEEAAAQQQQGDyBapbekJ6Ebr098l3q6w+55qX5ZeuiNyKqNholv5aXwAbCMxSAWYAOnzx//Zv/+awd2bvamho8Mz+07tcsCCyU9Nntuzk3l1eeqJJ1DHg+ePvSm5aUyz//NTO0YG8ITN4/eDGMvneFctFMwY7lV6TMU0HC4tz7IQiTsewDwEEEEAAAQQQQACByRRo7x0QnQEYqtI7MCQ//esB0efokaKP0f9w/mLRZH2RKpplOJLni9R9cR4EEAhMgAFAB6/ZPAB43333eWY8Ks95553noMSu6SagWYF3VrdK/+CxB5Hx7qEgM0muWJUnf3z/yOhuXbrw2t4GueCE3NE6Xx9q23slyyw1SIvg0gZf10I9AggggAACCCCAAAL+BKqaQzv779cm7p8+E3uXq07Ol2XzU72rwvpZl/3OTXWXEDCsF0LnCCAw6QKRe+0w6bfKBahAeXm5vPfee44Yzz//vPzv//2/PW0SExPllltucWzPzukloG//NACwm3LVKQWSO+aB4dF3Kly9GdUXnYdMVmANekxBAAEEEEAAAQQQQGAqC4R69t87Zc3y6t5665Y1JvdVp+ZbdeHciNGlvznuQgCF8zroGwEEpoYAMwCnxvfg+io2btwoBw4cGG2vSTpGitY/9NBDI5uenzfffLO1rQOA559/vpx99tlyxRVXyEknnSSa8EPLoUOH5Mknn/T8GYlV+OMf/1jy8yP3j5R1sWyETUDfBOamxUu9SdjhVHSw8NZzSuRHf/lgtFlX35D89u0K+apZuuCv9PQPSaXJoqbLDigIIIAAAggggAACCExVgVDG/mvu6pf73zhk3WpCbJTcYZ6fY6IiNwenyMT/jo85ltjSuiA2EEBg1gkwABjAV67Ze7dt2yY7d+6U5uZmz5FZWVmyYsUKOfXUUyU2NvxZnB544AF5+OGHx73qN998U/SPdxk7ADiyb9OmTaJ/fJWkpCS5++675fbbb/fVhPppLlBsBuU0xkmfidfnVE4qzJCzF2XLpoNNo83eONAo65bOlRX56aN1vj7UtJmlwGbAMZJZznxdC/UIIIAAAggggAACCIwV6DCx/zROdijKsFkG87MNB6Wzz84kfPOaYpmXlhCKU7jqQ7P+5qZG7nyuLopGCCAwqQIMALrg7+rqku9///vy4IMPjg78jT0sMzNTvvCFL8i//uu/Smpq5GI6jL0Of9unnXaaPPLII57Bv61bt0pNTY0n2YcObuo9nHjiiXLhhRfKbbfdNjoz0F+f7J+eAtFRc0SXIew+0m7iPTrfw42ri2R7Zat0mxl9I0UTgtz5qVV+Awpr3wfNUuBVZrAwypyTggACCCCAAAIIIIDAVBKoCmHmX132u6u6zbq9s0qyZN2SuVZdODd0FU8pyfjCSUzfCExLgTlmqaefX/2n5X2F7KL37t0rl156qRw+fHg0KYavzueYlE6FhYXywgsvyLJly3w1o94IVFVVeawUo7KyUgoKCnCZJIGKpi450moHJx7vUl7aXSe/fLPM2nXVKfnymdMLrTpfG/kZibLQLEOgIIAAAggggAACCCAwVQR09t+u6vaQXI7GEfzmE9ut2X+6EubOq1dJSkLk5t4sz0uT9KTwr04LCRqdRESA378jwjzlTxK5AARTnuL4C2xra/PMhhsZ/NOlvnfddZds2LBBPvjgA88f/axx8lauXOkZINS2F110keixFASmg0ChyfabFOc/NsiFH8mVJWbGoHd51mQILjcDiG7KkbYe0QcsCgIIIIAAAggggAACU0WgMoSZfx9757A1+Kf3ePva0ogO/i3ISGDwb6r8x8V1IDDFBBgAdPhC7rzzTjly5IinhS4B3r59u3zzm9+UtWvXytKlSz1/9PM3vvENef/99+UHP/iBp60eo8dSEJgOArosV5cC+1udG2VmuH7h3BKJNj9HypCZQPxzE+NkcNg5jqC2H1kKTFbgET1+IoAAAggggAACCEymQJuJ+6cxsUNR9tV1mKy/DVZXq0uzRONpR6okx0eLvtynIIAAAuMJMAA4nsrf655++mnRZb2f+cxn5Lvf/a7ns6/m2u5f/uVf5Nprr/XMBNRjKQhMF4Hk+BgpyPL/sKDZfK88ZYF1WxVN3aIzAd0UzQocyhgrbs5JGwQQQAABBBBAAAEExhOobOkerzrguqHhD+WXJj62d9GsvzesLvauCutnfZm/JDeVmNthVaZzBKa3AAOADt9fRUWFZ6+vTLrjHTrSduTY8dpQh8BUFFiQniDx5kHFX7nq5HwpHDNY+NR71XK42d0DFEuB/QmzHwEEEEAAAQQQQCDcAi1d/SY8jZ2pN9hzvri7VirGPAtfc2qhaPy/SBV9UZ/oIqxPpK6H8yCAwNQT8P/b/tS75ohd0Ug239zcXNfnHGmbkmLHSnPdAQ0RmCQBncVakJno9+wx0VHy5XWl1pJhfeupS4H1p7/CUmB/QuxHAAEEEEAAAQQQCLdAqGb/tXT3y++3VlmXW2ieqT+2Yp5VF84NHWicb17mUxBAAAEnAQYAHXQ0sYeW/fv3O7Syd420HTnW3ssWAlNbYG5KvKuEIKVzU+SKk+ylwGWNXfLcDvdLgWva/WcentpaXB0CCCCAAAIIIIDAdBRo7OyTrr6hkFz6I29XSM+A3detJm52TFRkftWOi4mS0rnJIbkXOkEAgZktEJm/laap4Ze+9CVPPL977rlHhl0kOdA2d999tydW4O233z5N75rLns0CbmcBqtHVpxRIfoY9Y/AP71aZGH8ulwK39sjAkP/kIbP5++DeEUAAAQQQQAABBEIr8KFZjlI5ZrlusGfYVd0mbx1ssg4/b+lcOWF+mlUXrg3NzbdkXorEmhU6FAQQQMCfAH9TOAh9+tOflltuuUXefvttWb9+vdTW1vpsXVdXJ1dffbVs3rxZNA6gJgOhIDAdBbLNLMAUkxTEX9G3jV8+r9QMeB9rORjAUuDBoQ9JCHKMjk8IIIAAAggggAACERBo6OiT3oGJv4QeNJM/Hnqr3LriZBOD77NnLrTqwrmhcbnTEmLDeQr6RgCBGSTg/7f8GXSzvm7l17/+ta9dct5558muXbvk+eefl9LSUrnkkkvkjDPOEI31p7OldOBvy5Yt8uKLL0pfX59nnx6jfd54440++2UHAlNZoDArUfbUdPi9xMUm09jHV+bJ8ztqRtsebOiSp96rkk+fVjha5+tDnVkGPD8tgYDFvoCoRwABBBBAAAEEEAiZwLB5WV3Z0hOS/l78W51UmxUt3uXaMxZKemJkBuQykmKPW43jfS18RgABBMYKzDFToP1H7R971AzbjjLxGXQwz19RKl/txu7TdoODockq5e+6puP+qqoqKSw8OkBUWVkpBQUF0/E2ZvQ1/+1Im7T3+P9vuH9wWP75qR1S03Yspp/+3+l7H18uJ+T5X/6QmRwbsWUSM/oL4+YQQAABBBBAAAEEHAVq2nqkvNFduBqnjtp6BuTrj79vxf4ryUmWH1y5QqKi/P9e6dS3m31xMXNkZX6G6IocCgJuBPj9243SzG/D3xh//451AM/fH23qq814+/7eNT8QmJYCuqTATdEHj69+dLFEew2i62uF//PaARNc2f8AYkvXgLR1D7g5FW0QQAABBBBAAAEEEAhKYMjM/jsyZsZeUB2Zgx5757A1+Kf93LymOCKDf/rIvXhuKoN/wX55HIfALBZgCbD58svKymbxfwLcOgLjC2g8EZ2dpwN0/sri3BS55vQCeXxL5WjTxs5+eXBjmfy3Cxb7nDk70riiuUtWJqb7bTfSnp8IIIAAAggggAACCAQioIN//YMTX/x2oL5TXtvXYJ167eIcWTov1aoL14Ym4Us3y38pCCCAQKACDAAasaKiokDdaI/ArBAozEwyA4Btru71k6sWyM6qNtld0z7aftOhJjmpMEM0G5pT6eobEg3InGviAVIQQAABBBBAAAEEEAilwMDQsBWuJti+h80yl4c3lVuHJ8RGyXURSvyhifoKMhOt87OBAAIIuBVgCbBbKdohMAsFks1DxtzUeFd3rvFOvvrRRZIcH221f+itMqn1ig9o7fTaqGzpFl2aQUEAAQQQQAABBBBAIJQC1SbxRyieM9/Y3yg6A9C7XHVKgWQlx3lXheWzhhbUVTe+YtKH5aR0igACM0qAAcAZ9XVyMwiEXqAoO8nEGHEXzDg7JV6+uLbUuojegWH56av7ZXB42Kofu6FLMkIVl2Vs32wjgAACCCCAAAIIzE6B3oEhqWs/lqwuWIXu/kH5nYn9513mm9Url62Y710Vts8LzTN5Ypz9oj1sJ6NjBBCYkQIMAM7Ir5WbQiB0ArHRUVKcney6w7NKsuX8ZblW+4MNXfKHd6usuvE2dABQH9IoCCCAAAIIIIAAAgiEQqDKrDIJxSKTp9+rFs3+611uPLtI9Fk53CU9MVby0ln6G25n+kdgpguE/2+rmS7I/SEwCwR0Zl92ivulDfowlJdux/N75v0jsvuIczxBfTiraOqeBaLcIgIIIIAAAggggEC4BXTWniamm2jRGYT/tbPW6uYUE+f6lIWZVl04NmKi58iiXPcv48NxDfSJAAIzQ4ABwJnxPXIXCIRdQGcB6gOIm5IQGy13nL9YojVYyd+LRvf7P68dlM7ewZGqcX82d/WbxCMTf1Abt3MqEUAAAQQQQAABBGaNwOHmbjF5OyZcntt+RIa8OtJn3BvMC+9IFH0Gj49h6W8krDkHAjNdgAHAmf4Nc38IhEggLiZKNB6g21I6N0WuPb3Qaq6De/dvPGQexJyfxMqbumRYpwNSEEAAAQQQQAABBBAIQqC9d8C8VLaX7AbRjbR098uGfQ3WoR9bPi8iS3J1BY7bhHzWBbKBAAIIjCPAAOA4KFQhgMD4ArmpCaIxSNyWj6/KkxUL0qzm75Q1y2t77Ycoq4HZ0MQhR9p6xlazjQACCCCAAAIIIICAK4HDIQor85ddtSaZ3bEX0zFm9t/HVy1wdQ0TaRRrVt6U5LD0dyKGHIsAArYAA4C2B1sIIOBHoHRusrW016l51Jw58pWPLpaU+Bir2cObyv1m/D3S2it9gyQEseDYQAABBBBAAAEEEPAroKtOOvyEnfHbiWmgMQRf2l1nNV27ZK5kJbuPjW0dHMCGZv2NRIKRAC6JpgggMM0FGACc5l8gl49ApAU0vt/CLPdLgfUB6fZ1pdZl9g0Oy09fPSCDQ8NWvffGkHnTSkIQbxE+I4AAAggggAACCPgT0FAzGvsvFOVlM/jXM3DshbRGt77CrHAJd0lNiBFdeUNBAAEEQinAAGAoNekLgVkiMN9k+M1Icr8U+IziLLnwhFxLp6yxS57YWmnVjd1oMlnb2ronHrtlbL9sI4AAAggggAACCMxMgfqOPunpPzZoF+xd9psX1n82y3+9y5klWZKXkehdFfLPZgGN6IobCgIIIBBqAQYAHURLSkpk0aJFcuDAAYdW9q7Dhw9LaWmp5zh7D1sIzCyBRSbJR1zMsSy//u5OM6UtyLDfZD6/s8bM8utyPJSEII487EQAAQQQQAABBBD4u4CuIKlqCc3sv9f3N0hbj/0i+oqTwh/7b35agiTF2eFz+IIRQACBUAgwAOigWFFRIeXl5dLf3+/Qyt41MDDgOUaPoyAwkwU0K7AOArot8THR8t8uWCIaOHmkaDJgjQfolBW427zBrQzRg9zIefmJAAIIIIAAAgggMPMEakwSuf7BYwk7gr3DYTOQ+PyOI9bhK/LTA3r2tQ52uaHP14UBhNpx2S3NEEAAAY8AA4D8h4AAAkELZCTFSZ5ZDuy2FGcny/pT8q3me2o6RDMDOxVNCNLa7X4g3qkv9iGAAAIIIIAAAgjMPIEBE1tanxlDUTabZ9O69j6rq09GYPZfkUn8Ee31sty6ADYQQACBCQowADhBwLGHt7W1eaqSktwnSRjbB9sITCcBTQiSHB/t+pKvWLVAclLszGmPbK4wb2t9JwTRzg82dPpt4/oiaIgAAggggAACCCAwowSqWnpElwBPtOjKlGe3V1vdlOQky4oFaVZdqDfSE2PNM3J8qLulPwQQQGBUgAHAUYrQfHjkkUc8HRUVFYWmQ3pBYIoLRJm3lEtyU8Xty0pd2vD5s+z/fzSaZB9jl1mMvW1dzqGDgBQEEEAAAQQQQAABBLwFek2m3rr20Mz+21ndJuVNdhzBK83svzmanSNMRZ+jdZCRggACCIRTgOiiXroXXHCB19axj7fccoskJzv/hdzX1yeHDh2S+vp6zz8Ol1xyybEO+ITADBdIjIuWYvPQcqjBOaHHCINmUPtIXqro8t+R8sz7R+S8pXMl2+HNZ6vJCHyktcckEwlv9rWRa+InAggggAACCCCAwNQXONzcbWJKT/w6h00nv3+3yupIk3KcUZxl1YV6Q59t9XmaggACCIRTgAFAL93XXnvNM3jnnZBAP2/ZssWrlf+PmgX4O9/5jv+GtEBgBgnMMw9Hmimtyczm81f0DepNZxfLd57eOfqw1m/itjz6zmFPohCn4/UBL80skUiJ568vJyf2IYAAAggggAACs0Ggo9fd86cbizf2N8qBenvFiWb+1RUv4SrxsVG83A4XLv0igIAlwG/QXhzr1q2zpnZv2LDBs33aaac5zgDUwYyEhATJy8uTNWvWyHXXXefY3uuUfERgRgloVuDu/jbpMZl7/ZUikxDkwhNy5eU99aNN3zrYJBcvnycnzPcdY0Xf7u6v65BVBRkESR6V4wMCCCCAAAIIIDA7BfTlcChKd/+g/M68jPYuOvtv3ZIc76qQfy4y8bRJ/BFyVjpEAIFxBBgA9ELRGYDeJSrqaIjEhx56SJYvX+69i88IIDCOgD68LJuXKho7xU0Q5k+fXiibzKBfl9eA4a83VcgPrlzh+Ka1d2BYyhq7ZHFuyjhXQRUCCCCAAAIIIIDAbBBoM+Fh2nsGQ3KrT79X7VnN4t3ZTWuKJCY6fGHzM5JiHcPfeF8LnxFAAIGJCoTvb7OJXtkUOP7GG28U/ZOZmTkFroZLQGB6CGj8kkVznWNmjtxJWkKsXHNawcim56cO7G3Y12DVjbfR0NEnzV3+lxuPdyx1CCCAAAIIIIAAAtNfoLIlNLP/qk2M6f/aWWuBnLowQ04uDN/vgST+sLjZQACBCAgwA9ABWWf+URBAIHABTeSR1zsoNW3+s7FdZJb86jJgffAaKY9trZSzSrMkKc75r6hDJitwSnyGaGZhCgIIIIAAAggggMDsEdAXwR3meXOiRWO+/3pTuQxpnJm/lxgzOnfD6uKRzbD8zEtPlIRYEn+EBZdOEUBgXAF+ax6XhUoEEJioQFF2kqQmOA/g6TlizFL7G88usk7XbpKJPLWt2qobb2Ng6EM51GgHah6vHXUIIIAAAggggAACM0ugKkSz/9493CI7qtosnI+vypP56QlWXSg39OV1fmZiKLukLwQQQMCvAAOADkQ7d+4Uzei7ZMkSqa72PxihbRYvXiyLFi2Sffv2OfTMLgRmvoAmx1lq4gHGxfjPmqYJPU4rspdY/GVXrRzxmhXoS6yla0Dq2/3PNPR1PPUIIIAAAggggAAC00ugsbNPuvr8J53zd1f9g8PyGxN/2rtkmrh860/O964K+edi86KcxB8hZ6VDBBDwI8AAoAPQI488IuXl5Z5Bvfx8//8IaJulS5d6jtFjKQjMdgF9u7nEDAKasUC/5fNnmSDLGgzl70WXYfzmbfuBbGTf2J/lTd3SOzDxh8Cx/bKNAAIIIIAAAgggMLUEdMluVcux0DETubo/76yRehNX2rt8zjyThnNpbnoiiT+8vfmMAAKRE2AA0MF6w4YNZuBijnzyk590aGXvuvLKK0X/UXrllVfsHWwhMEsFNNGHLgf2V3SZxeUr86xm71e2yntmWYa/ohmHD9R3ev6/568t+xFAAAEEEEAAAQSmr0CDmf3X0z/xF786i/CP79urvJaZF9drFmWHDUdn/ZXkuEuWF7aLoGMEEJi1AgwAOnz1I8t4V61a5dDK3rVixQpPxd69e+0dbCEwiwU0yHFOSpxfAV1ukWHeinoXnQU4ODTsXTXuZw0C7SbpyLgHU4kAAggggAACCCAw5QWGzUvfUMz+0wkbv3qzXPrMEuCRoutQblpT7JkAMlIXyp+6IkYHGBPjSPwRSlf6QgAB9wIMADpYdXYeTS6QkpLi0MreNdK2vb3d3sEWArNcoHRuisnq6/zAow9E15250JLSQb2//K3WqvO1UdncLd39E88G56t/6hFAAAEEEEAAAQQmT0CX6/YNHBu0C/ZKtpa3yLYxq0wu/EhuWGfnlc5NlnQTX5CCAAIITJYAA4AO8pmZR5MS1Na6G3zQrkbapqamOvTMLgRmn4AueVg2P1Vioo/F+RtPYe2SHFlkHpC8i2YEbu3u964a97N5KSwH67tYCjyuDpUIIIAAAggggMD0FdDZf9Wt3RO+AV0+/NCmcqsfjct37Rn2S2irwQQ3CrMSJTc1fFmFJ3h5HI4AArNEgAFAhy9as/9q+ctf/uLQyt71X//1X54KzQRMQQABW0ADKi8yMwGdSpRZH3HT2cVWkx6T4OOxLZVWna+Nzr5BOWJmDVIQQAABBBBAAAEEZo5ATXuv9A+at70TLE+8WynNXfaL5RtWF0lKfMwEex7/8Ny0eCnI9B8Pe/yjqUUAAQRCJ8AAoIPlxz72Mc9Movvuu0/27Nnj0PLorr/97W9y//33e+JGXHrppX7b0wCB2SiQlRwn+RmJjreumYPXLs6x2mzY1yBvH2qy6nxtVLEU2BcN9QgggAACCCCAwLQT0HjQNa0Tz/x7qKFTXhgTWmZlfnrYEn9kmCW/pST9mHb/vXHBCMxUAQYAHb7Zr3zlK5KcnCy9vb1ywQUXyPPPP++z9bPPPisXXXSR9PT0SGJiovzDP/yDz7bsQGC2C+gyiNQE57esGgswPsb+K+q+1w+5evjTpcCHGlgKPNv/O+P+EUAAAQQQQGBmCGhM6IGhic3+0yXED2wsMxM8jpnEmtA0Xzi3JCyJP5Ljo2Wpeak9R7N/UBBAAIEpIOD8G/gUuMDJvIScnBz5+c9/LjfccIPU19fLlVdeKaWlpXLuuedKXl6e59JqamrkjTfekLIy/cfkQ89f8D/72c9k3rx5k3npnBuBKS2gD0Il5m3ozuo26yHM+6J1pqAuBb7vjUOj1boU+O5X9sv3rzzRDA46JxTRrMC6FNjfbMPRzvmAAAIIIIAAAgggMOUE+k2mXh0AnGh5cXetlDV2Wd1cdUqBzEsLfWw+jX2tg3/6k4IAAghMFQEGAP18E5/73OdkeHhYdDZgd3e3HDx4UA4dOjYgoYfrwJ8WnS2og3+f//znPdv8DwII+BZINnFW5psHLqcHuo8umysf1LbL6/sbRzvSTL+/erNcvnye/zibuhQ4KylONLswBQEEEEAAAQQQQGD6CVSbpb9DurxjAqWps08e32rHk9aXxFesOjqpYwJdj3tocU6SaOxrCgIIIDCVBOz1dVPpyqbQtegMwAMHDsg///M/y8qVKz1XpoN+IzP+Vq1aJd/97nc9bRj8m0JfHJcy5QUKs5IkbswyX++L1pmCt5plGdrOu2g8wFf31ntXjftZnxUPmlgvI4P04zaiEgEEEEAAAQQQQGBKCvSa1R/1JvnHRIo+Bz70Vrn0Dgxb3ejS35jo0P86PDc1joy/ljQbCCAwVQTmmL8QJ/Y6ZarcSQSvY3BwUJqbmz1nzMrKkpgYJlIGyl9VVSWFhYWewyorK6WgoCDQLmg/QwT0jey+uk7Huzli3vx+9487rQc3jdny/StXSFF2suOxulNjDpJ9zS8TDRBAAAEEEEAAgSklcKC+Uxo6+iZ0TZsONsp//PWA1cdHl86VL7lYTWId5GIjITZKVhVksPTXhRVNIivA79+R9Z6qZwv9K4+peqchvC4d8MvNzfX8YfAvhLB0NSsFslPiJTM51vHeF5glGrevtZf8aiDoe17eL939g47H6s6qlh5p6er3244GCCCAAAIIIIAAAlNDQJ/xGs2L4omU9t4Bz+w/7z7SEmPl+rMWeleF5LPm+lhC3L+QWNIJAgiER4ABwPC40isCCAQgUGxm8fmLkXz2omy5ZLmdXKfWLAm53yQJ8TeRWec5HzBLgXv6hwK4KpoigAACCCCAAAIITJaAvsCd6Fq1X5ulv+0mMZx3uWVNsaQmOL989m7v9vNCE7ImxcS4piCAAAJTVYABwKn6zXBdCMwiAQ2SnJ+Z6PeOP7+6SBbNtZf8vn2oWf76gf94gINmxuDeug4ZHLLjv/g9KQ0QQAABBBBAAAEEIirQ2TcoTZ0TW72xraJF3jzYZF33GcWZclZJllUXio2MpFjRFSsUBBBAYCoL8IrC5bfz6quvyh//+EfZvn27NDY2Sk+PvpHyHT5RkxdoxmAKAgi4E1iQnmiWefQ7ztKLNYGav3bhUvnOUzuky2s238Obyj1LLvTNq1PRGYD7TSyZE+aniv5/lIIAAggggAACCCAw9QSqWrondFG6fPjBN8usPpLjouWWc0pC/gyocakXzU2xzsUGAgggMBUFGAD0863U19fLddddJxs2bPC09DXop4MJ3vsYXPADy24ExghEmTXAJWYp8O6a9jF77M25qfGeoM0/eWnf6A6NB3jvK/vk/1m/UnQ2oVNp7R6QyuYeWZjtPFjo1Af7EEAAAQQQQAABBMIj0GVm/7V0DUyo899uPizNY+I/33B2kWQmxU2o3/EOLjWDf3ExLKwbz4Y6BBCYWgIMADp8HwMDA3LZZZfJ+++/7xncO/nkkyU/P1/+9Kc/ed4cff7zn/dkA962bZvU1NR46k499VRZsWKFQ6/sQgABXwLpZvmELqHQQTqnckZxllx64nz5y99qR5sdae31BHn+souMbtUmq3CieQusg4kUBBBAAAEEEEAAgakjcMQ8p02k7KpuOy48zKr8dFm3ZO5Euh33WH2WzEoO/aDiuCejEgEEEJigAK8qHAAfeughee+99zwtfvWrX4kO9P3oRz8aPeLhhx+W5557Tqqrq+Wpp56SvLw82b17t3ziE58QbU9BAIHABYrMzDw3q3M1e1tJjh0PcMO+Bnljf4Orkx4ySUF6B0gK4gqLRggggAACCCCAQAQE9NmsaczMvUBOq8drgjjvEm9m5922NvRLf+Njo6SYFSXe1HxGAIEpLsAAoMMX9Ic//MGz99JLL5WbbrrJoaXI+vXrPcuE4+Li5Oabb5b9+/c7tmcnAgiML5AUF+NqZp7GA/zHC5ZI4pglvw9uLBM3b46HTQjPQw1d418EtQgggAACCCCAAAIRF9BnOIcw636vR5f+1nf0We0+e+ZC82yZYNVNdENfVmvcvxjzPEpBAAEEposAf2M5fFOa8ENj+elS3/GKd8w/3b9o0SL52te+Jl1dXXLvvfeOdwh1CCDgQqDAZASONjEB/ZX56QmeN7re7foGh+U//rpfBlxk+23rGZD69l7vw/mMAAIIIIAAAgggMAkC/eYZrmHM4F0gl7GjqlVe3lNnHbJsXqpcvHyeVReKjTzzDJqeGBuKrugDAQQQiJgAA4AO1M3NzZ69JSUlo610ht9I6e4+PjvVhRde6Nn90ksvjTTjJwIIBCgQHxMt+mDlpqxZlCMXnJBrNa1o6pYn362y6nxtVDR3iz5wUhBAAAEEEEAAAQQmT6C2rVd0hUYwRROH/OL145f+amzoKDexZQI4aXJ8tBRmkkwuADKaIoDAFBFgANDhixgZ7Bv5qU3T0tJGj9DYf2NLQsLRQYvx9o1tyzYCCPgWWJCRaDKq+Z8FqD3caLK66axB7/Lc9iPygZ+Mwtp+0GQQLm9iKbC3HZ8RQAABBBBAAIFICgyalRt1HcGvynh4U/lxWX81XrSuFgll0QUquvQ3ysVKlVCel74QQACBUAgwAOiguHDhQs/eurpjU8nnzZsnqampnvrNmzcfd/SuXbs8dbp0mIIAAsEL6BLgApdvV3XG4B3nL7aWDesL5P987aB09w/6vYimzv7jHhr9HkQDBBBAAAEEEEAAgZAI1Jmlv/pSNpiypbzZJIFrtA5dabL+XvyRcCz9TZTk+BjrXGwggAAC00WAAUCHb+rUU0/17B3JBDzSdN26dSY47YeeOH99fceCzLa2tsqdd97piRu4fPnykeb8RACBIAVyU+MlKS7a1dFF2cnymdMLrbYNnX3y600VVp2vjbLGLvPgyVJgXz7UI4AAAggggAAC4RAYNut+a9t6guq63cRzfmBM1l9NEPeldaWe38mC6tTHQTHRcyQvI7QzCn2cimoEEEAgLAIMADqwajw/Hej705/+ZLX68pe/7NnWgcFVq1bJt771LfnqV78qK1eulH379nn23XjjjdYxbCCAQOACOpN2YZb7GCufWJknJ8w/OkN35Gwb9jXIO2VH43mO1I33U+MAHjbxACkIIIAAAggggAACkRPQF7b9g4HP/tPf0x7cWCbtvfZqj5vWFEt2SnzIb2B+WoLEkvU35K50iAACkRNgANDBev369aLLgKuqquTgwYOjLT/+8Y/Lrbfe6hkc3L9/v/zkJz+RX/ziFzIS9++SSy6Rr3zlK6Pt+YAAAsELZCbHSW6au4c4jcfy1Y8uEn3z613uN2+GW7v7vavG/VzX3mceIgfG3UclAggggAACCCCAQGgFdBDvSGtws/82HWqSd8zyX+9yelGmrFuS410Vks+e2X8hjicYkgujEwQQQCAAAQYAHbAyMjKkvLxcKioqZNGiRVbLBx54QO6//34566yzJDk5WeLj4z0zAO+66y557rnnTGBYaC0wNhCYgECJWd6bmuAu3src1AS52bz59S6df88Mpw+Z/kq5WQrspp2/ftiPAAIIIIAAAggg4CzQYGL/9Q4EHoJFs/4+PCbMS4qJzfeFc0tCvvRX7yDPDP7FMPvP+ctkLwIITHkBd79RT/nbmJwL/MIXviD6h4IAAuEV0Jl9S+alyK7qNldLRNaaN7/bDrfIZq+lv+9XtsozJjPw+pPzHS+2q29I6s3D6DyzzIOCAAIIIIAAAgggEB6BIRP7r7IluPArj2+tFI3/51108C8jKc67KiSfYzX2X3piSPqiEwQQQGAyBZimNpn6nBsBBFwLaKbfpfNSxYwF+i0aO/DoQ2Cs1faJLZXybkWLVTfeRqWJBUhCkPFkqEMAAQQQQAABBEIjUGMSfwQT++9gQ6e8vLvOuohTF2bIWSVZVl2oNhZkJEq0mwfQUJ2QfhBAAIEwCTAAGCZYukUAgdALpCbESklOsquOte1Xzlsk3uOFugD4p6/uFx3gcyoDQx9KVUtw8Wic+mUfAggggAACCCCAgJiBv2ET+683YArNGKyJP7yDusSZpbka/kVfAIe6xMXMYVVIqFHpDwEEJk2AJcAu6YeGhuSZZ56Rl19+WXbu3CnNzUcDzmZlZcmKFSvkoosukiuvvFJiYiB1SUozBIISyDVLczWmnybs8FdWFWTI9WctlN9uPjzaVOPM/PjFvfKD9StMXEF7huBoI/Ohtr3Xk3wkKY7/T3u78BkBBBBAAAEEEJioQLVJ/KFLgAMtL+2pkzITr9m7XH1qvmgM6HAUZv+FQ5U+EUBgsgT4zdaF/LPPPit33HHHaJZfPWQkSYC+aXrrrbfkvvvuk7y8PPnpT38qmj2YggAC4RPQWYDd/UPS0Tvo9yQfX5knh5u65Y0DjaNtNcbfPS/vl+9cfoLE+EjYo/lCyhu7ZfmCtNHj+IAAAggggAACCCAwMYEe8wxXZ160BlpauvvlcRPOxbvkm+W5+qwXjhIXEyXzwjSwGI7rpU8EEEDAnwBLgP0I3XvvvXLVVVd5Bv9GBv2Ki4tl9erVnj/6WYvuO3LkiHzqU5+Se+65x1PH/yCAQHgEdOBd4wHqg5m/om1vW1sqi+baS4d317TLb8ZkjxvbV5sJLt3c1T+2mm0EEEAAAQQQQACBIAU08Ye+aA20/ObtCukZGLIOu9Uk/ghXdt6CzETRRHQUBBBAYKYI+P/teabcaRD3sXnzZvnmN7/pGdxLTU2VO++8U+rq6uTgwYOeWX86808/a53uS09P97T91re+JXosBQEEwiegg39LTWZgN89l2vablyyTzCR7ye+LJoD0Kx/YQaTHXnFFU5dovBkKAggggAACCCCAwMQEOnoHpKkz8JerO6paZdPBJuvka5fkyPK88KzUSEuMkdzUeOt8bCCAAALTXYABQIdv8Cc/+Yn5xX/YM7Cng306sJeTk3PcEVqn+7SNDgLqMXosBQEEwisQSFKQzKQ4zyBgbLT9JvfXb1WYINS+E35ozMCaIJaphPfO6R0BBBBAAAEEEJh+AhUmLEugZWBoWH71Zrl1WHJ8tHzurCKrLlQb+qy4ODclLElFQnWN9IMAAggEI8AAoIPaG2+84fmL/9vf/rYsX77coeXRXR/5yEdE2+py4Ndff91vexoggMDEBTQpyLw0d29oF81NkS+tW2SdtN88VP7nawccA1FXm4zA3f3+4w1aHbOBAAIIIIAAAgggMCqgYVXcxG8ePeDvH/68s8aTnM27/rNnLJT0RHtlh/f+iXwuNc+L8THRE+mCYxFAAIEpKcAAoMPX0tLS4tl7/vnnO7Syd420bW1ttXewhQACYRPQpCCpCe5yGp2zOEcuWT7PupaDDV3yx/errTrvDc1St7+uk6XA3ih8RgABBBBAAAEEXApoOBUNqxJo0UHDp9+zn9F0dt75J+QG2pWr9rnmpXJWcpyrtjRCAAEEppsAA4AO35hm9Q22TOTYYM/JcQjMVoFAkoKo0fVnLZQF6QkW19PbquVgQ6dV572hWYfLgnhw9e6DzwgggAACCCCAwGwU0HAqGlYl0PLo5grpGzx2nAZyuXlNsYkBbYd0CbTf8donxkVLcbadNG68dtQhgAAC01WAAUCHb+6iiy7y7N2wYYNDK3vXa6+95qm44IIL7B1sIYBAWAVGkoK4eR7UZR1f+ehiK4HIkFm6r0uB+70eMsdecH17nzR29o2tZhsBBBBAAAEEEEDAh4A+W2k4lUDLBzXt8uaYxB8fXZYrGtIl1EWTyi0xMwuj3WSXC/XJ6Q8BBBCIkAADgA7QmgE4MTFRfvSjH8m+ffscWh7dpW00G3BycrInKYjfA2iAAAIhFdCkIAuzklz1qctH1p+Sb7U90torv9ty2Kobu3HILBfuHRgaW802AggggAACCCCAwDgCh5u7HWMtj3OIJ+zKQ2+VW7uSzQy9684otOpCtVFonh+T492FkwnVOekHAQQQiLQAA4AO4suWLZMnn3zS02L16tVyzz33SHNz83FHaKzAe++9V9asWePZ98QTT4geS0EAgcgLLMhIlMxkd0GhrzIDgBo/0Lv8ZVet7Kpu866yPms8wH11HcQDtFTYQAABBBBAAAEEjhfo6B2Qho7AV0+8/EGdVJiBQ+9yzWmFkhaGxB8ZSbGiz48UBBBAYKYLzDEZaz+c6TcZ7P2NLOOtrq6W/fv3ezICa6yxkpISyc3N9WzX1dVJWVmZJ/Ovnmfx4sWSn2/PKvI+vx7/yiuveFfNys9VVVVSWHj0DV5lZaUUFBTMSgduOjwCAyaz746qNsflvCNn1iUp33l6hwwMHfurMNsEf/7/rlklSXG+3wTPNzEExw4ejvTJTwQQQAABBBBAAAHxvFQNNPNvuxk0/MYT70tX37EVFzpD7/+9amXIl+jGx0bJyvx0iY1mXgz/vc5sAX7/ntnfr9u78/3brdseZnA7jeenA3YjRcdK9c/Bgwc9f0bqvX8eOHBA9M/YcVXtR+u8+/M+js8IIBA6AX2I0yW+e0zsGH+vOPIzE+WzZy6UX2+qGL2AJpNx7vdbq+QmE2TaV6lt6/VkHs5JiffVhHoEEEAAAQQQQGDWCujMv0AH/xTriS2V1uCf1mnij1DH59Nwf0vnpTL4p8AUBBCYFQIMADp8zevWrWPAzsGHXQhMZYF0s0Qk3yznqHIRdPpjJ86Xdyta5G9H2kdv6YXdtXLesrmO2eAO1ndKbFSUpJulIxQEEEAAAQQQQACBowIaMkVj/wVayhq75K8f1FuHnb0oW5bnpVl1odgoNmFgUoj7FwpK+kAAgWkiwACgwxc1ktHXoQm7EEBgCgsUmNl9bT0Dft8+R5kZul9cWyrfenL76FJgnTn4y41l8j8/eaLJFnxsJrD37ZpnW9lX3+F5KCVwtLcMnxFAAAEEEEBgNgtoiBXN/htoeeydw3IsKItIfEyUfM6s1Ah1mZsaJ/PSEkLdLf0hgAACU1qAYAdT+uvh4hBAYCICuuR+ybwUiYkefwDPu299CLzyZDt+534zw2/DvgbvZsd9HjSxAz+obScz8HEyVCCAAAIIIIDAbBTo7h+UmraegG9995E22TEmEdt682yWHeJwK0kmm3BJTkrA18cBCCCAwHQXYABwun+DXD8CCDgKxMdEyxITD9DHJD7r2CtWLTBvg+2Yfr8zb6I7ewetdmM3+gc/9MQbDOZN99i+2EYAAQQQQAABBKargMY8P2BeoOoqiUCKHvf41krrkEwTYuXylXlW3UQ39KWwxv0LdTzBiV4XxyOAAAKREGAAMBLKnAMBBCZVICMpThaa7HH+SpxZZnLzmhKrmQavfmzLYatuvI3egWHZW9shGvOGggACCCCAAAIIzEYBjb3snb3XrcH7la2yr67Tan7VKfmiz2ahLKUm7l+imQFIQQABBGajADEAXX7rw8PDsnv3bjl06JB0dJhf8oeOpaX31cWNN97oaxf1CCAQYYEFJiFIz8CQ1Lf3OZ755MIMObM4S94pbx5tp8GoP7os15NZeLRynA+dfYPm4bVDTpifSgKhcXyoQgABBBBAAIGZK9DROyDVrYEv/R0eZ/Zfbmq8nG+evUJZCrMSQ76cOJTXR18IIIBAuAUYAPQj3N3dLT/4wQ/kgQcekKamJj+tj+3W2GMMAB7z4BMCU0GgJDtZevqH/CYFufHsItle1Sp9fw9erXP6fvlmmfzgyhUSFeUcT7C1e0AqmrpFM8tREEAAAQQQQACB2SCgKyB06a8mUQu0bD7U7Hl28j7umtMKTAzn0M3+y0mJk4JM/6tBvK+BzwgggMBMEwjd36ozTcbcT2dnp5x33nly5513SmNjo/kH7cOA/sxAEm4JgWktoIN3y8zsvPhY57/6NNj01WbZiXcpa+ySl/bUeVf5/FzT1iv1Hb0+97MDAQQQQAABBBCYSQIVTV0mIVrgWX914PDJd+3Yf/lm1cY5i3JCxpOaECOL5pL0I2SgdIQAAtNWgBmADl+dzvx79913PS1Wr14tt99+u5x00kmSkZFhZgE5DyA4dMsuBBCYRIFY8zZZl+juqm53jNenQadf399oLWX57eYKzwBisZlJ6K+UNXRJYmy0pCbE+mvKfgQQQAABBBBAYNoKtHb3S52fECu+bu6N/Q1yxLw49S6fOb3Q74oL7/ZOnzWGoCb98LeCw6kP9iGAAAIzRYABQIdv8sknn/TE8br88svlmWeeYdDPwYpdCEwngaS4GE88P03a4avospNbzymW7/9pz2iTgaEP5e6X9skPr1opyfHOf31qLhANZr0yPz3kAaxHL4gPCCCAAAIIIIDAJAoMDA3LQfPSM5iix/5hW5V1aIkJoXJGcaZVF+yGZvrVl76hTiQS7PVwHAIIIDDZAkxjc/gGqqurPXv/8R//kcE/Byd2ITAdBbKS40SXmDiV5QvS5dIV860m9R198vMNBz3hAKwd42z0mxiCmhRkWEcDKQgggAACCCCAwAwT0KW/+rwTTNEka42d/dah15rZfxpLPRRl0dxkvy9sQ3Ee+kAAAQSmiwADgA7fVG7u0cxTOTmhi0HhcDp2IYBAhAU0G5zGhXEqnztzoSzJtePGbK1oked21DgdNrqvo3dQyszDMQUBBBBAAAEEEJhJAs1d/dLQYQ/gub2/rr5Beeq9o5MtRo7R2XqrCtJHNif0syg7iYy/ExLkYAQQmIkCDAA6fKtnnnmmZ+/evXsdWrELAQSmq4C+YV4yL0Vio32/adalwF+7cMlxA4WPbTksu2vaXd16vYmLU99ux7dxdSCNEEAAAQQQQACBKSigy3fLGjuDvrIntlZKe8+Adfy1Z4Rm9p+u8FjgZ5WHdWI2EEAAgVkiwACgwxf99a9/3bP3pz/9qavlfg5dsQsBBKaoQHxMtN/McJoV+I7zF4v3MKFJCi7/8cp+aTGBr90UzSLc3T/opiltEEAAAQQQQACBKS1Qbp5r+geDC3Giz0Qv7amz7u+0okwTry/NqgtmY25qvCw0s/8oCCCAAALHCzAAeLzJaM2aNWvkzjvvlLfeekuuu+46aW1tHd3HBwQQmDkCmSYeYF56guMNrSrIkE+dVmC1aTNvrnUQcNC8BfdXNAzggfpO4gH6g2I/AggggAACCExpAV36OzZ2n9sLHjZvUH/1ZpmZXHHsCF2JcePqomMVQX7KTI41L3WTgzyawxBAAIGZL+Ac/Grm37/fO/ynf/onWbRokXzxi1+UwsJCufjii2Xp0qWSlOT/zdL3vvc9v/3TAAEEpoaAxorpNPFoNGafr3LVKfmepB47qtpGm3xgMgn/p0kKojMEo/wEre7qG5KK5m7RDHcUBBBAAAEEEEBguglMdOnvhr0Nst+8EPUu60/Ol9w05xex3u3H+6wxnZfmpoYsgch456AOAQQQmO4CDAD6+Qbr6+vl6aeflra2NjNzZ1ieeeYZP0cc280A4DELPiEw1QU0HuBik+xjZ3WbmdHn9Vra68J1gO8fzEDfvzy1U5rM2++Rsulgk6TGx8jNa4r9PnjWtvVKemKsaBZiCgIIIIAAAgggMJ0EJrL0t6N3QB5957B1u/PNwN8VJy2w6gLdSIqLNsuHUyUqyjtYS6C90B4BBBCY+QIsAXb4jpuammTdunXy29/+VoaGhjxxAD8089Xd/nHoml0IIDAFBRJio0VnAjqVtIRY+frFSyU+xv7r88XddfL0mGx2vvo51NApfYNDvnZTjwACCCCAAAIITDmBiSz91Zt5fEulZ7WF943py9NYk3At2BJjlg8vM4N/mrSNggACCCDgLMDflA4+P/zhD2Xfvn2eAb9rrrlG/vrXv4oOCupgoM4G9PfHoWt2IYDAFBXITU3wzNBzurxFc1PkG2YQMHrMm+bfv1slr4wJaj1ePwNmhqHGA9SXCRQEEEAAAQQQQGCqCwybYMYTyfqrzz1//aDeus0zS7LkpMIMqy7QDX0m0xe4FAQQQAAB/wIMADoYPfvss57lfDfccIM88cQT8tGPflQyMzP9LvFz6JJdCCAwDQRKTQDpMWN7x121JgX5ynmLrMzA2uhBE9j6nbLm49qPrWjvGZTq1p6x1WwjgAACCCCAAAJTTqCuozforL86ePhLTfzhdVe6kmKiiT80gRshVbxQ+YgAAgj4EWAA0AGourras/fWW291aMUuBBCYaQL6Jrkgy3kpsN7zOYtz5Mazi63b10l9//9f98uemnarfryN6pYe6elnKfB4NtQhgAACCCCAwNQQ0AG8IxN4aalhUsoau6ybufrUAslOibfqAtnQpB/+wrYE0h9tEUAAgdkgwACgw7eck5Pj2ZuamurQil0IIDATBRaYt8rJ8f6XlFy6Yr5odmDvMmgelO99Zb+0dB9LFOK9f+SzaSaHGu1MeCP7+IkAAggggAACCEwFgfqOvqBn/9W398pjWw5bt5GfkSiXm+enYIvG/dPEbZrAjYIAAggg4F6AAUAHq7Vr13r27tq1y6EVuxBAYCYK6ENlqYkr4+bZ8tOnFciFJ+RaDG09A56ZgEM6yudQdClwvVlWQ0EAAQQQQAABBKaagM7+CzZkicY6vn9jmUl8Nmzd1q3nFE8oacdi4v5ZnmwggAACbgUYAHSQ+uY3vymxsbHy4x//WHp7+QXdgYpdCMxIgZT4GJmfluD33nSw8NZzSuSkgnSr7Z6aDnlia6VVN97G4aZuGRiyH47Ha0cdAggggAACCCAQSYGGTp39F9wzymt7G2RXdZt1ufrCdPkC+3nJauBnY0FGgmQmx/lpxW4EEEAAgfEEGAAcT+Xvdaeeeqo88MADnkzAl1xyieenQ3N2IYDADBQoNLEA42P9/1UZZbKG/MP5iyV7zEPps9uPyLaKFkcZzQp8uLnbsQ07EUAAAQQQQACBSAroDL5gZ/81d/XLb96usC5XE3Zcf9ZCqy6QDX0xu9BFjOZA+qQtAgggMJsEYmbTzQZ6ryPJP5YvXy4bc/fn0QAAQABJREFUN24U/blq1SpZunSpJCU5JwjQGUEPPvhgoKekPQIITDGBaDOwV5qTbJJ6dPi9stSEWPnvFy2R//ncbvFe+vufrx2QH161UnIdZhPWt/fJ3NR4STN9UBBAAAEEEEAAgckWaDCx//oGAp/9pwOHD5qlvz0DdqKz284tkaS44H79NI9jsig3mbh/k/0fBedHAIFpLTDH/AXtHKBqWt/exC4+KirK+kdGqdwEmx1pNzRk/6M3sauZWUdXVVVJYWGh56YqKyuloKBgZt0gdzPjBCrNDL0qk7XXTfnLrlp5eFO51VQHEf/nJ0+U2GjfswmT4qJllVlG7ObvGatzNhBAAAEEEEAAgRAK6O8z71W2BjUA+OaBRvnpqwesq1m7OEe+alZKBFsWZieJJg+hIIBAcAL8/h2c20w7KrhXMDNNwcf9LFy4kF/EfdhQjcBsEyjITJSu/kFp6Rrwe+sfO3GefFDbLpvLmkfbHmrskl9vqpAvmLffvkp3/5AcaevlAdcXEPUIIIAAAgggEBGBYGf/aRK0h94qt64xLTFWbji7yKoLZEOX/i5I9x+TOZA+aYsAAgjMRgEGAB2+9fLycoe97EIAgdkkoLPyNOvczv426fWzHEbb3r6u1BPXr8YM6I2Ul/fUyfK8NDl7UfZI1XE/q8xMw6ykOEk0swEpCCCAAAIIIIBApAV09l8wsf/0uAc3HpLOvkHrkm9dUywaJiWYokt/F+emMCkjGDyOQQABBMYI+F6LNqYhmwgggMBsF4gxy3eXzU8VjQvor2iMm/9+0VKJG7Pk9/43Dkld+7FBwbH9DJugDPvqOqwYgmPbsI0AAggggAACCIRLQDP/+nvZOd65n36vWraU24nPzizJkrNKfb/4HK8f77oCk/SDl6LeInxGAAEEghdgADB4O45EAIFZKKADe4vmJru6c81Ud+u5xVZbDYh97yv7ZWDId1BtXQp8sKHTOo4NBBBAAAEEEEAg3AKaxEzjHgdatpY3y+/frbIOS46PllvM7L9gS2oCS3+DteM4BBBAYDwBBgDHU6EOAQQQcBDITol3HafvvKW5snZJjtVbmYkH+NvNh626sRtNnf1BLb8Z2w/bCCCAAAIIIICAW4EjrT3SPxhYjkgdMPw/r9lJP0w0FLnj/CWSYcKaBFM8WX9N6BUSowWjxzEIIIDA+AIMAI7vQi0CCCDgKFCYlSiZye7i2dx6ToksyLCDV7/wt1rZ4pUkZLyT6QN1W7f/pCPjHUsdAggggAACCCAQiECvWaWgA4CBlM7eQfn3l/Yet2T4+jMXysmFGYF0ZbVl6a/FwQYCCCAQEgEGAA1jdHS0509MjJ0TZaQ+mJ9j+wrJt0UnCCAwZQT0jfSS3FQT1Nr+e2O8C0yIjZavXbhUYqPt2IG/eP2gNHT4jgdoYmnL/voO81A9NF631CGAAAIIIIAAAiET0BePGovYbdHlwvf+db+JbdxnHXLu4hz5+Mo8qy6QDY35R9bfQMRoiwACCLgTYADQOGnGqpE/3mwjdcH+9O6LzwggMPMENBnICSYpSJKLjL0aD/CmMXFwukysP40HOOgQD3Bg6ENPUpDhQJ7IZx41d4QAAggggAACYRTo6B2QRhN+JJDyyOYK2VXdZh2icZK/uLZ0Qkt3i7OTJnS8dUFsIIAAAgiMCvifujLadOZ++Ld/+7dxb85X/biNqUQAgVkpoJmBT8hLlb8daZe+Ad+JPRTngmW5nnabDjaNWh1s6JI/bKuSa89YOFo39kNX35AcMnEDF+emjN3FNgIIIIAAAgggMGGBiqbAEn+8trde/rKr1jpvRmKsfOPiZRIXE/wck6zkuKDjBloXwwYCCCCAwHECDAAaEl8Dfb7qj1OkAgEEZrVAfEy0LM9LM4N7bY6Bs3XZ8G3nlkiZGfSrbT+29Pe57TWyujRbirJ9Zxdu6OiTzKRY0QQkFAQQQAABBBBAIFQC+ozRYWL5uS376jrkwY1lVvMYsyriGxcvFR3AC7Zo4o8iM/uPggACCCAQHoHgX8+E53roFQEEEJiWAhrnb9n8NIkZE+dv7M0kxcXIP164RHT58EgZMmEI7n/jkPhb5qvZg/sHnWcZjvTJTwQQQAABBBBAwJ+AxvE7bGL/uS3NXf1y90v7ZHBMaJLb1pbIknmpbrsZt92CjETR5ykKAggggEB4BBgADI8rvSKAwCwUSImPkaUmMYiZ6OdYSnKS5ZMnLbDa6FLgF3fbS2msBmZD4wEebOgcW802AggggAACCCAQlIBm/XX7clHb/fuLe6W1Z8A612Ur5st5S3OtukA34mOjRAcAKQgggAAC4RNgADB8tvSMAAKzUCDdLNPNd/EAu/7k/OMy3D22pdIE4LYz6Y0lbO0eMNn2ji0fHrufbQQQQAABBBBAwI1A3+CQ1LS5e6bQpIi6WkFjEnuXlfnp8rmziryrgvpcZJKlea+OCKoTDkIAAQQQcBRgANCRh50IIIBA4AIFmYmSmuAcYlUDZN9msuR5lz7zZv2XJqaOPmQ7FQ3U3Tsw5NSEfQgggAACCCCAgKNAVUuP6BJgN+VPO2tk44FGq+m8tHj5xwvssCZWA5cbaYkxxDh2aUUzBBBAYCIC/5e9+wCvozoT///auurVltUtq1luGDAE08H0QEKAhNRNsgYCbAIbkt0kW/5kN2E3u79kk00jjRJaQsgmmw0sCaEsxRhiMN2AsS1bVrFlVav34v95r7nSnFvmjmTJat/zPH7uzJkzc2c+F+7VnDnnfekAPBI99kUAAQTCCGiyD83YGy0e4GqTOOS8VfaUmddq2+SFyrEswWEO7/9jfXdjV9SOwnD7UocAAggggAACCPQMDIkm//BStu1rk19trbGaJpgpu1++aKWkRHngae0UZkXDpmhoFAoCCCCAwNQL0AE49ca8AwIIzEMBDWLt5Q/aT5y8TNITYy2he7ZUS1eUbHyarU+f3FMQQAABBBBAAIHxCmjijygTDvyH1Lh/tz1babXVUMc3nrtcli468oy9GjZFE6RREEAAAQSmXoAOwKk35h0QQGCeCixJiZes1HjXq9fEIVedXmy16TDBtX/5YrVVF25lvwnc3W5iAlIQQAABBBBAAAGvAu3m74zWbm9/P2iCMs386ywfOalQTipa7Kya0HJyfIzpRCTxx4Tw2AkBBBCYgAAdgBNAYxcEEEDAq4COAkyMi3FtfkrJYjlx2SKrzaZdTfL0jkarLnhFn9zvbOiUzj5vf8QH7886AggggAACCMw/gRoTS9hL6e4fkgdf3281XWPCl1yxLt+qm8jKQjOMUMOlaNgUCgIIIIDA0RGgA/DoOPMuCCAwTwU0o53+gat/6EYq+sfvNWcUi8bTcZafP79X3jnQ4awKWdbg3TvrO0Vj+VAQQAABBBBAAAE3geaufukyHXteysPb6qS73046pqFLJqPTrsCM/GPqr5dPgTYIIIDA5AnYd5uTd1yOhAACCCDwroBO812W6R4nJ9NMF7769BLLTDv3vvvELmno6LPqg1cGhw+ZjsJOMgMHw7COAAIIIIAAAqMCh8zUgVoT+89L0Wm/f3qz3mqqMxb0oeaRllSTOERj/1EQQAABBI6uAB2ALt733Xef6L+ODvcROM5DdHV1+ffR/SgIIIBAQCAvPVEyU+ICq2Ffz16RJZcel2dt06f0335sZ9QRfhqkW0cL6isFAQQQQAABBBAIFqg3DxT7Br39nfA/r+6TgeGxtjqT4WMm9t+RFj1OWRZTf4/Ukf0RQACBiQjQAeiidtVVV8nVV18t+/btc2llb2poaBDd75prrrE3sIYAAvNeoNTEAwye5huM8on1y0w8wAyrWpN93PrUbhkxIwLdiv5Rv6O+Q4Ycf7C7tWcbAggggAACCMwPAf3bYH9rr6eLrTN/dzy9045DfO7KbMmbhFF7hYuTosZG9nSSNEIAAQQQGLcAHYDjJvO2gw6xpyCAAAJOAV/MQlmRk+oaD3CheTT+1+eWS2FQVrzXa9vk/q01zsOFXdZYPZoYhO+gsDxUIoAAAgggMC8F9jZ3i4YM8VJ+83KtOJ85xpm/Xz504lIvu7q20am/eekJrm3YiAACCCAwdQJ0AE6y7fDw4UC5Pp9vko/M4RBAYC4IJJt4gJoZ2K1o1uCvvHel6B/KzvLImwfkWZMdOFrp6B0yMX68PeWPdiy2I4AAAggggMDsFtARfc1dA54uYndjl7y496DV9uK1ubI42T2MibVDmBVfzOGkaJORQCTM4alCAAEEEPAgQAegB6TxNNm5c6e/+eLFi8ezG20RQGAeCWSnJUhWarzrFWelJsjfXrhCNIuws9xlMgPrlOBoRdtoAG8KAggggAACCMxfgfbeQanxmPhDZw88EDTbIDk+Ri47Pv+IAZebuH8JsTFHfBwOgAACCCAwcQF7eMnEjzMn9nz22WfDXsdLL70kzc3NYbcFKvv7+2XPnj3yne98R/TJ1rp16wKbeEUAAQRCBDQeYLdJ8NEzcHjUcEgDU7EqN02uO6tEfrapcnRzv0ny8YMnK+Qbl6+VOJ/7M5w9TV2SFJfOH9yjeiwggAACCCAwfwT6Boelwh8WxNs1v1LdKttNQjFnuWJdgejshSMp+RkJsugIRxAeyfuzLwIIIIDAYYEj+zafY4rnnHOOv/POeVn6JGw8CT20vXYA/tVf/ZXzMCwjgAACloDG+tN4gNv2tVlxdqxGZmXDimzzx3uXPLljLBh3rXmSf9+WKrn2rNLg5tb6kIn1s7O+U9YWpIeMJLQasoIAAggggAACc0pAE4fp3w9e4/71DAzJ3X+usgx02u9Fa3KtuvGuaDiTZSbxBwUBBBBAYPoF3IePTP/5HfUz0A68wL/AmwfWvbwuXbpUfvzjH8sVV1wR2J1XBBBAIKyAxvorCEr2Ea7hX55WLJo1z1m0Q3DLHveRydpeRxjube5y7soyAggggAACCMxxgUqT9KPLzDTwWn7z8r6Q0CEfX18YdbaB2/GJ++emwzYEEEDg6AswAtBh/vTTT4+uaWffeeed5x/N9/Of/1xKSkpGtwUv6Ii/hIQEycvLk8LCwuDNk7re2NgoW7du9f/Tqcn6r6Wlxf8eGzdulHvuuWdc7/enP/1Jbr/9dv9xmpqaJCsrS9avXy/XX3+9XHLJJeM6Fo0RQGD8AgUZif4/uDV7b6SiU32/cH653Pz7N0WnAAfKHZv3SqmJqZNjYgq6labOAUmJ75NcMu+5MbENAQQQQACBOSFQ394nTZ39nq9FE388/na91V5nD5y5fIlVN94V4v6NV4z2CCCAwNQK0AHo8N2wYYNjbWzx5JNPljVr1oxVTONSTk7OpLz7yMiIv5NPOzedZf/+/aL/HnzwQbn22mvltttuk4ULGSjqNGIZgckU0AcIZaYT78397Wb0ceQja0fhNWeUyE837Rlt1Gti+2g8wFsuO0ZiY9z/P61u6TZtFkhminvykdGDs4AAAggggAACs05Ap/Lqb77XMmTuCe7YXCnOP0H074VrzywJCY3k9Zjajrh/49GiLQIIIHB0BNzvGI/OOczYd9m7d69UVlbKihUrZuQ5Llu2TC666KIJndvNN98sgc6/E044QR544AH/qEJ91XUtd955p3z1q1+d0PHZCQEEvAtocG3t4ItWzl6RJWeX20/j95opPr98oTrarv44gxXmCX+dhwzCUQ9GAwQQQAABBBCYcQI6g2lPY7drbOHgk35k24GQLMFXnrg06uyC4OM41zNT4oj75wRhGQEEEJghAnQAunwQ2vlXVFQkPt/4B0recMMNLkee+KZ//ud/locffljq6+ulurraP0JvvEfbtWuXP1ux7nfSSSfJ888/Lx//+Mf9U3/19bnnnvPX6/Zvf/vbsnv3bl2kIIDAFApoB6DGBIxWrjajAPWpurM8vr1B/vTWAWdV2GUdYVjd0mNiAnb7Y52GbUQlAggggAACCMxKgToz9Xc8cf8aOvrkv1/dZ12rxhx+/3F5Vt14VpaYzr/y7JQjGj04nvejLQIIIICAdwE6AF2sNJHHK6+84tIi/CaNn6dTZ6ei3HLLLXLppZfKkUwF/v73vy9DQ4eDAt96662SmGiPPEpKShKt16Ltvve9703FpXBMBBBwCGhW4LKsZPMHs6MyzGJCbIyJB7jCP53XufkXW6rlhcrD8UCd9eGWNTbQzoZOGTYZAikIIIAAAgggMPsFdOrvvoM9ni9ERwv+/Lm9VpZg/RPk+rNKxDfB8D9ZqfGynM4/z58BDRFAAIGjLUAHoIt4Z2envO9975OdO3e6tLI3adw8nTo7U4v+2D/00EP+01u1apWceuqpYU9V61euXOnfpu11PwoCCEytQGpCrORGSeihZ7DMPJ2/7qxS62T0/9AfP71bth/osOojrbR2D8r2ug4ZcCQVidSWegQQQAABBBCYuQITmfr73O5mf/xh51VddEyu6cBLdVZ5Xs5Jo/PPMxYNEUAAgWkSoAPQBX758uWimXEvvPBCqa2tdWl5eNNVV10ld999t39Fp9LOxKJxDevq6vynFinpSeC8A9s1KUhVVVWgmlcEEJhCAZ1642Uq8FnlWfKxk+ys40NmRN93H98ptR5HAOg0oXdMh+HQ8Fhm4Sm8NA6NAAIIIIAAAlMgsN/E9x3P1F8dLfjLF2usM1mcHCcfPWmpVed1JTc9QUpNQjMKAggggMDMFqAD0OXzeeKJJ6SgoED27dvn7wRsbGwM21qfun3605+W++67zz9S7lOf+pT84he/CNt2uiu3b98+ego6AtCtOLe/8847bk3ZhgACkyQQY6YCr8pNlThflLnA5v0uX5cvF6y2M4N3DwzLNx/dIS1d/Z7OqMe039XQxShfT1o0QgABBBBAYGYJaGfe/tbecZ3U717ZJx29g9Y+V51eLElx4497rnGJS5YkW8diBQEEEEBgZgrQAejyuWgCkMcff1wyMzOloqJCLr74YunosKfXjYyMyCc/+Um5//77/UfauHGj3HvvvbJwgrEzXE5nUjZpZ2agLF3q/pSvsHBsdJGXEZCB4+qrvo/bvwMHoicscB6PZQTmk4DG+VuZmybaGehWFpiAgVebP9hPKlpkNTvYPSDfMp2AelPgpbSbm4BKkxiEggACCCCAAAKzR2AiU3/3tfbIY283WBd54rIMWV+82KrzsqIJzIoy6fzzYkUbBBBAYCYI0AEY5VNYvXq1PPLII5KcnCxvvPGGPwFHX1+ff6/h4WH5xCc+Ib/+9a/969dcc43cddddMzrrlcY1DJSUFPeh+nrNgdLV1RVY9PSqnYdu/04++WRPx6ERAvNVICXe924WPXcBTR7y1+ct97d1tqw1owE0JuCIx/idjR39ojcFFAQQQAABBBCYHQIHxpn1VzsM7/1zlQw7/jbwmb8jPn1q8bgveOmiRFmWmTTu/dgBAQQQQGD6BOgA9GC/fv16efDBByUuLk6ef/55ufLKK6W3t1c++tGPym9/+1v/Ea677jp/8g8dkTOTS6DzUs9Rr8etxMfHj27W66UggMDRFVhk4vGUephWE++Lka+8d6XkmRg8zvJqTZv87tWxUb/ObeGWaw/2SrPHqcPh9qcOAQQQQAABBI6OQP/QsHlwN76/z7dWHZS3TAIwZ7n0uDzRGH7jKdr5pzGLKQgggAACs0tg/IEeZtf1TdrZnnfeefLAAw/IRz7yEXn00UelpKTEnyBE3+Czn/2s/OQnP5m095rKAyUkjP3ADwwMuL5Vf/9YDLHExETXtsEbo00Z1inAjAIMVmMdgVCBbJMVuN9k6o32R75mEP6Hi1fJVx96Szr7xqb+/s+r+6XYTM/xOrVnT2OXiT+4UNLM8SgIIIAAAgggMDMFqlt6ZNgk//JatMPwly9UW8018cfl6wqsumgrhYsTZekiOv+iObEdAQQQmIkCjAAcx6dyxRVXyB133OHfQxOC6DD6G2+8cdZ0/umJp6amjl5xtGm93d1jMcGiTRcePei7Cxpf0O1fXl5e8C6sI4BABAF9yp6VOjYiN0Iz0c7CL55fLsGhA3/yzG7PAcL1XmJXfacMkhk4EjP1CCCAAAIITKtAW8+ASfbl/iA/+AT/9406M8rf3udTpywTjTvsteiUXzr/vGrRDgEEEJh5AowANJ9JTU2N509GRwLedNNN8oMf/EA+/OEPy1e+8pWI+y9btszzcY9WQ2fiD2dCkHDv7xzF50wIEq4tdQggMLUCOhW4b3DYGt0X7h3X5KfLp04tkvu2jD3l7xsckf98Yqd844q1njL8DQ4fkpqDPVKW5R4nNNz7U4cAAggggAACUycwYp7U7R1n4q7Gjj552HQAOsuavDQ5tTTTWeW6nJ4YK5r0g4IAAgggMHsF6AA0n51O5x1v0Vh/v/vd7/z/wu2r24eGxqbhhWszHXVr1qwZfdsdO3aMLodbcG7XZCgUBBCYPgFN9lGekyJv7W+XgSH3KT8XH5PrvznYXNE8esIaKFyTgnzpopVmhGD0WKWaFCTbjDrUqcUUBBBAAAEEEJgZAnXtveaB4Ijnk9EZS78wU3/14V6g6EyBjacXe05cqH82FC9h2m/Aj1cEEEBgtgowBdh8cvrDOBX/ZuJ/FNrZmZ+f7z+1TZs2uZ7is88+699eUFAgxcXFrm3ZiAACUy+gyT7Kc1LNH+zu76UPIK49s1RKghKIaFKQ+81NgNfMwFXNPf7vRvd3YysCCCCAAAIIHA0BnQmwf5yJPx56vU5erm61Tu/CNbmybBxJPHJMiJGkOMaNWIisIIAAArNQgG9y86Hdfffds/Cjm9gpa8fA5ZdfLj/96U9FR/i98MILcuqpp4YcTOsDIwC1ve5HQQCB6RfQ5Bya1CPa9B9N5PG3F66Qm3//pnQ4koI88la9tPYMymc3lPmTfbhdUVf/kDR1mpGA5g9/CgIIIIAAAghMr0BVS7d5iOf9HB5/u17+6+Vaa4fUBJ98+D1LrTq3ldiYBVJosv5SEEAAAQRmvwAdgOYz3Lhx4+z/JMdxBV/84hfl9ttvl+HhYfn85z8vOtLPmeW3t7fXX6+H9Pl8ou0pCCAwcwRy0xMk0DnndlZLUuLlCyYpyL898o51w7ClssUEAu+XL5vpwGkmpo9b0ViAmiXQF8OAcTcntiGAAAIIIDCVAge7B6S1e9DzW2yuaJK7/1wV0v7q00skJd77LaCOFORvgBBGKhBAAIFZKeD9239WXt7cO+nnnntOdu/ePXphzc1jMb60/p577hndpgtXXXWVta4rK1as8Ccv+eY3vykvv/yynHHGGfL3f//3UlZWJnv27JFvfetb8tprr/n30yQn5eXlIcegAgEEpldAk4L0Dgz7OwLdzkSTglx/dqnc/myl1QlY0dgl//TQW/J3710lBS5P9jVmUK2ZbhQ8ndjtPdmGAAIIIIAAApMnoIk/qs3oP6/lpaqD8rNNe0KabzytWE4r8574QzsKs0w8YAoCCCCAwNwQWGBi341jIPncuOjZfBXaoXfvvfd6voRIH+/IyIhcd911ctddd0U81mc+8xn/SMGFCyd/5I9mIA5kFtZsw87sxBFPiA0IIGAJ9A8Ne0oKoju9UdsmP3iyQnpN/CBnSYqLkb+5YIWsLUh3VlvLGgHgWLM9eRwjBqwDsIIAAggggAACExaoa+s1HYA9nvbXZGHfenSHDAXNFf6Imfb7oRO9T/3VN1tbkEYyME/qNEJg5gtw/z3zP6OjcYaT37NzNM6a9zhiAe3U+/nPfy5//OMf/TEBNTFIXFycP0GIxvx75JFH5M4775Sp6Pw74pPnAAgg4BfQpCArTFIQzeYXrRxfmCG3XHaMLEmJs5r2mFGE3zQ3Ctvr2q1654o+JooWc9DZnmUEEEAAAQQQmByBweER2W86AL2U3WZ0/3ce3xnS+ff+Y/PkgycUeDnEaJus1Dg6/0Y1WEAAAQTmhgAjAD18jkNDQ/6Oss2bN0tlZaV0dnb64+e57apJM5588km3JvN6G08g5vXHz8VPsoDG86to6PJ01LaeAf/NwZ4meyqRxgL89yvWSqaJGxip6DRgjT9IQQABBBBAAIGjI1DV3C0H2vuivpnGCLz5wTelzST6cpZzV2bLdWeVjCuhX4x5srjOPDjUhGIUBBCYGwLcf8+Nz/FIr4IYgFEEN23a5I+jV1NTM9oy0rRabaAdf7qdrLmjXCwggMAUC2iyjz4ztbf2YPQRAhlJcfJPl66RnzyzR7buPTh6Zh29g/4pwv9stkUK9q2jAEfM91t+BtkAR+FYQAABBBBAYIoE9Le9viN659/A0Ih894mdIZ1/p5YulmvPHF/nn15KUWYSnX9T9JlyWAQQQGA6BegAdNF//fXX5eKLL5aBgQF/p15CQoI/IUZGRgZTY13c2IQAAkdfYOmiJNMJOCJNnf1R31ynDmt24G8/tlNeN7EBA0UTg9z3QrVcc0ZJoCrkVWMQ6XSkoszkkG1UIIAAAggggMDkCdQc7DH3IO7H04EHP3+uUoJH9q/NT5Mbz1lu7lk8xAlxvMWi5FjJSWO0v4OERQQQQGDOCNAB6PJRfv3rX5f+/n6Jj4+X7373u3L11VeLdgJSEEAAgZkooJmBNTFIR+9Q1NNbaEYr33jucrn5929Ko6PT8IntDbI8K0XOXpEV8Rh1bX3+TsAy047RzhGZ2IAAAggggMCEBTr6BqWlayDq/n96q16erWi22mWbzL03mQd9kUb0W40dK3G+BaK/7RQEEEAAgbkpQGAHl8/1ueee89/c3nzzzfK5z32Ozj8XKzYhgMD0C+hTfk0KkhDr7as9xWT1/dsLV0hsjD064E4zkqCqxY4RGHx1TZ0DsqPexEMNyjIY3I51BBBAAAEEEBi/QI2HrL/b9rXJL1+stg6ufwN8+aKVE0rgoZ1/sTHe/oaw3pQVBBBAAIFZIcA3vMvH1Nd3OOaGTgOmIIAAArNBQP9wX5WbZp762516kc5dp/Jed1aptXlw+JB874ld0tXvPpJQA42/c6BDRugEtPxYQQABBBBA4EgENLlXZ5/7b3C9SQzyw6cqQqYI32Cm/RYuThr322uSL40TTEEAAQQQmLsCdAC6fLbFxcX+rYODdjYtl13YhAACCEy7QGJcjJRn6/Rcb6dyVnmWXLQmx2qs04Jv27THH//U2hC0ojco+9uiJx8J2o1VBBBAAAEEEAgjoA/VNPafW9GkH/9pkn509w9bzT78nqWyvnixVedlJcn83VA0gU5DL8emDQIIIIDAzBGgA9Dls7jiiiv8W5999lmXVmxCAAEEZp6APsXXLH5ey6dPLfJ3Gjrbv1zdKi9UjmUKdm5zLmsHYM+A+0gFZ3uWEUAAAQQQQCC8gP6m9pukXm7lodf3y75W++Hbyabj74MnFLjtFnab5ghZbh4ajjdZSNiDUYkAAgggMKMF6AB0+Xi+8IUvSF5ennznO9+Rqqoql5ZsQgABBGaeQF56omSnxXs6MQ0U/sULVkhaYqzV/t4tVVGnAmuGwj2N3VFHC1oHZgUBBBBAAAEELIHegWGpizKqfr/p+HvojTprv8JFifK5c8pEE3yNt+h04WQTE5iCAAIIIDD3BegAdPmMs7Ky5JFHHpHExEQ55ZRT5I477pD29naXPdiEAAIIzCwBzQyclujtD/vFyXFy9enF1gW09w7Kr4ICjFsN3l3ReIEHTDwiCgIIIIAAAghMTKCyuUvcwuqOmCdumqjLmYBLR/DdeO5ykwAsZtxvmprgkzwT+4+CAAIIIDA/BLzdFc4Pi7BXedxxx4lOAdYOwM9+9rP+bMBLliyRpCT3qXULzBO4PXv2hD0mlQgggMDREtDvIs0M/Ob+9qhTivScTilZLCcuWySv1rSOnuLTO5vkzOVLZE1++mhduIVaE7NIOxEnchMS7njUIYAAAgggMF8EGjv7pKPXPZzGJvN7vKO+0yJ5/7F5JuRHslXnZUUHC5ZmJZt4weMfNejl+LRBAAEEEJh5AnQARvlMfve738lnPvMZ6ezs9E9vO2SevDU2NkbZS/gxjSpEAwQQOFoChzMDp8r2ug7RDL9uRW8ErjmjWLYfaJc+RwyiOzbvlW9deZzE+SIPHNdRC3uauuSYKB2Fbu/PNgQQQAABBOabwODwiNS0uCf+0BH592+ttmiyUuLlSpP4YyKlICNRkuK4FZyIHfsggAACs1WAb32XT27Lli3y8Y9/XIaHD2fYKioqEh0RmJGRYQLlRr4JdjkkmxBAAIFpEdA/8tcWpPtHDmiMIbeSaW4oPr5+mdzz56rRZvUdffL71/bJx0y9W9HRCw2mbU4aU4rcnNiGAAIIIIBAQECz/kZ7QPeLF6pDsv5ec2aJxPvGP/U30WT91Q5ACgIIIIDA/BKgA9Dl8/7GN77h7/xLT0+X+++/X973vve5tGYTAgggMLMFdGru2vw02dXQJTqSwK1cuDpHnt/dLBWNXaPNHn7jgJxamhl1qpHeyGQkxU7opmT0zVhAAAEEEEBgHgjo73FjR7/rlW7b1+b/TXY2Oq0sU9YVZjirPC/r1F+y/nrmoiECCCAwZwQYxubyUb788sv+qby33HILnX8uTmxCAIHZI6DZflfnpZoReu7ZgfXG4LqzSiVGo4u/W4ZNCIQ7NtvBxwPbnK9DZprxbtNxqCETKAgggAACCCAQXmDExM7Y29wdfuO7tQNDI3LX83utNslmBN9fnlpk1XldyTVJP9ISYr02px0CCCCAwBwSoAPQ5cPs6Tkci+PMM890acUmBBBAYHYJaJy/0qwUKV6SZB5yRD73wsVJctnx+VaDPU3d8uNndlsZCK0G767oVGAdCUhBAAEEEEAAgfACB0zIjGhhOX736j4TWsMeIfiJk5eZkfZx4Q/qUqtxfJeZ33YKAggggMD8FKAD0OVzLykp8W8NdAS6NGUTAgggMOsE8tITpcx0BLqVK9YVSJ4ZLeAsW/a0yI+fjt4JWNfWJy1d9k2L8zgsI4AAAgggMF8Fhs3ovwNtva6Xr6PpH95WZ7VZmZMq567Ktuq8rpQuSbZG9nvdj3YIIIAAAnNDgA5Al8/xQx/6kH8K22OPPebSik0IIIDA7BXISo2XrNTIowh0tMBnN5RJbIw9VHBLZYv86OmKqCMBK83UpmijG2avHmeOAAIIIIDAxAQaO/tcE3/o1N+fbdpj7kXGjq9hOT5jEn8sdBu+P9bcWtKHeYuSI//eW41ZQQABBBCYkwJ0ALp8rF/60pekvLxcvv/974vGA6QggAACc1GgZEmKJMRG/jlYYUYbfOW9q0I6AV+oPCi3PlUhQyMjEVk0HuCuhs6oHYURD8AGBBBAAAEE5piAxsjVUfJuRaf+7g8aIXjliUtFw3OMp2hfoYb8KDaj/ygIIIAAAvNbIPId3/x28V99amqqPPnkk7J27Vo5++yz5eabb5Zt27ZJX5/7DzZ0CCCAwGwS0BEFy7NTXOMBHluQLn9nOgHjTBIRZ3lx70H50VO7XTsBewaGTZDzsWzCzv1ZRgABBBBAYL4JNJnwGDrCL1LZ3dgZMvW3xHTgfeD4vEi7hK3XPF7l5vddQ35QEEAAAQQQsO/k8LAEYmJipKioSLZu3erv9PvmN78pJ5xwgiQnm/gZZpvbP5/PZx2LFQQQQGAmC6SajIBLF7nfIKw1nYBfee/KsJ2Adz9f5Xp5TZ0DUt/OwxNXJDYigAACCMwLAbfRf4en/laGTP3VcBy+hd5v3TR0x5r8NMlMiZ8XplwkAggggEB0Ae+/ItGPNeda6PD8wD+9uMCy19c5B8IFIYDAnBYoyEiUtET3hxeROgGf2tEoL1cfdPWpaumWfa1kBnZFYiMCCCCAwJwWONg94BobN9LU3/Fk79WwHvp7rQ/3KAgggAACCAQE3O/0Aq3m6evXvva1eXrlXDYCCMxHgQUmUJBOBX5zX7trYHK9qfj7i1fKfzy2U/odU5ju2LxXVmSnmk7E8DccGsi89mCvdPYN+d8nNmg68Xw055oRQAABBOaXQF1QXD/n1Uea+nvZ8fnOZq7LyfExsjovzcTtZZyHKxQbEUAAgXkoQAegy4dOB6ALDpsQQGBOCsT7YqQ0K0V21ne6Xt+a/HS56vRiue3ZytF2Hb2DcudzlfI3F6ww8QTtrMGjjcxCW8+gbDOdjCtyUhid4IRhGQEEEEBgTgu0m99JfQgWrgwOa9bf8FN/NVavl5Ka4JNVuanio/PPCxdtEEAAgXknwKOhefeRc8EIIICAu8Di5DjJSo0eM2jDiiw5qWiRdbCXqlplc0WzVRduRWMcvV3XYbIg9obbTB0CCCCAAAJzTsDtN+/hN+rCZv31OvVXQ3joyD86/+bcfzZcEAIIIDBpAnQATholB0IAAQTmjkBxZpLEmxhCbkVH+V17VqmkmREHznLPn6uk2WQ4jFZ0SnB1S49UNXdHa8p2BBBAAAEEZrVAd/+QfwR8uIs40N4rD76+39qkWX+9Tv3NSIqV1blp4nWkoPVGrCCAAAIIzBsB97u7ecPAhSKAAAIIOAV0BEGZmQocraSbeH/XnllqNesdHJafPrNHRrSHz0M5YLID680PBQEEEEAAgbkqEOl3TpML3vXcXiv2rkbRuM48YPPSobcoOVZW5qTKQo/ThOeqL9eFAAIIIBBdgA7A6Ea0QAABBOalgHbu5aUnRL329SWL5ezyJVa77Qc65LG36606txUdCaiZESkIIIAAAgjMNYHOvkEzMj78b9zze1rkLRMSw1kuOSZXdARgtKLTfun8i6bEdgQQQACBgAAdgAEJXhFAAAEEQgQ09lBiXExIfXDFRpMQJNPEDnSWB7bWyF6P03t1sODuxi7pMlOkKAgggAACCMwVgZ6BIdlhEmuFGxTfZRKC/OKFautSNQ7vR04qtOrCrejoQB2p75Z0K9x+1CGAAAIIzF8BOgDn72fPlSOAAAJRBXRK0fJsvcFwb5oU55PPbiizGg0OH5LvPL7TxDwKP+rBamxWhkcOmezDHdJnphBTEEAAAQQQmO0C+nv2zoFOGTK/h+HKAy/VSIfJDOwsV5sHagmx0R+8FS5O9NTOeWyWEUAAAQTmtwAdgPP78+fqEUAAgagCKfE+KchIjNpubUG6XLI212qn03q/+8Qu0ay/XsrAkHYC6s2St/ZejkkbBBBAAAEEjraA/u69Y8JhRPr909+6p3Y0Wqd1UtEiOal4sVUXbiXVJN/KTYseoiPcvtQhgAACCMxfAToA5+9nz5UjgAACngWWLkoU7QiMVv7i5GWyKjfValZhpvbeubnSTH8KPwLCamxWegaGZVdDF52AwTCsI4AAAgjMCgF9iLXDP6I9/MOsoZER+flzlda1xPsWylVm9F+0ork+SrOSmfobDYrtCCCAAAIhAnQAhpBQgQACCCAQLKAxhlbkpkh8rPvPhmYP/psLVkhWSrx1iM27m+UP2w5YdW4r7WZKlAZF19hJFAQQQAABBGaLwIiGs2jolO7+yOEsHn2rXmpbe61L+qiJ+5cZ9NtpNXh3Jd+MyNewGxQEEEAAAQTGK+B+Jzfeo9EeAQQQQGDOCsT7YmRNXprE+dwDAqaZ7MFffu9KE5vI/onRpCCvVrd69uk1IwHf2t8hLV39nvehIQIIIIAAAtMpUGmSX3X0Rn54pQ+2Hnq9zjrFoswkea/J/ButJJmkXDoin4IAAggggMBEBOy7s4kcgX0QQAABBOaNgAYmX5WbJr4Y905AzR5847nLxdlKJwDf+nSF7Gnq8uyliUF0OnBNS4/nKcSeD05DBBBAAAEEJlFAk141dbo/tHrkzfqQjPefOaNENKuvW9FkXEz9dRNiGwIIIIBANAE6AKMJsR0BBBBAwBJINrEAV+SkSpR7FTmpaLF8bH2htW/f4Ih8/X/flsffrh9Xh97+tl4TT4nkIBYmKwgggAACM0ZAp/7uNaP/3Epn36A88qYdDkMTf5Sb39RoJS89QVITYqM1YzsCCCCAAAIRBegAjEjDBgQQQACBSALpZpqv3rDoiAS3ctnx+XLG8iVWkyFzk3T3n6vk+09WmBhJkadJWTuZlbaeQakyIwEpCCCAAAIIzDSBfSamnz7kciv/+0ad9A6OxQbUn1CN/RetpCX6REfWUxBAAAEEEDgSAToAj0SPfRFAAIF5LLA4Oc4/HcmNQJOHXH9WqT92YHC7rXsPyj/+z5uy22QJ9lp0alW76QikIIAAAgggMFMENK5fXbud1CP43A52D5jR7w1W9enmAVlhlI49Tb6lo+7195SCAAIIIIDAkQjQAXgkeuyLAAIIzHOB7NQEyUmzM/4Gk8T5Fso/vm+VXHpcXvAmaTIJPnRK8GNmSrDXUtncJTrVioIAAggggMB0Cxw6dEgqm7pNWAv3M3nw9f0yMDw2QlDDaHz4xKWuO2lcwFW5qRIbwy2bKxQbEUAAAQQ8CfBr4omJRggggAACkQSKM5NFMxO6Fd/ChfLJU4rkKyY7cIqJIegsw+au6R4zJfjRt+y4SM42zmWdYqVTrSgIIIAAAghMt0BDR7909rmHs2jq7JOndjRap3ruymzJNXH9IhUd8FeenWJ+X+3fzEjtqUcAAQQQQCCaAB2A0YTYjgACCCDgKrDQjFDQ6UnRMhjqQU5ctki++aFjZWWYgOf3bqmWTbvsG6RIb6xTrcYTPzDScahHAAEEEEBgogL9Q8NS2xo9Nu3vXt0vmtU+UGJjFsgHTygIrIZ91Zh/i0yoDQoCCCCAAAKTJUAH4GRJchwEEEBgHgskmhGAxZneApRnpsTLP126RjRBSHC57dlKeXFvS3B1yLpOtTo85WrshiqkERUIIIAAAghMoUC1SUw1NOz+O7TfjFh/tqLJOosLV+eI/hZGKtkmtEZ+RmKkzdQjgAACCCAwIQE6ACfExk4IIIAAAsEC2WkJsiTF22gFHS34iZOXyceCsh9qx96tT+2WN2rbgg8fst5lMgjXd/SF1FOBAAIIIIDAVAvsb+uVlq4B17fReLW/2lpjxQeMN3FxL1sXefRfcnyMlJjQGhQEEEAAAQQmW4AOwMkW5XgIIIDAPBYoWZIsCSZjoddy+bp8+UBQchCdJvXdJ3bJzvrOqIepPdgrfYPDUdvRAAEEEEAAgckSOGDCUNSY0X9uZcgk/PjR07vl1ZpWq9kla/MkPTHWqgusaNy/0qwU0dAaFAQQQAABBCZbwPtd2mS/M8dDAAEEEJhzAj6TqbDcxPfTmxgvZYFpqCMBL1idbTXXTInfenSH7KjvsOqDV7SzkKnAwSqsI4AAAghMlUCDGXle1eze+acPpr792E7ZUmmHtNDRfZcGPfRynmeOGUkfnCjLuZ1lBBBAAAEEjkSADsAj0WNfBBBAAIEQAb150ZGAXot2Al59RomcUZZp7dJrbqC+8Yd3THbgejN9KnKMpfbeQbICW3KsIIAAAghMhUCjyearD53cSpfJCPzvj7wj2/a3W818ZlTfDRuWS7L5jQxX4szU4MJFxP0LZ0MdAggggMDkCNABODmOHAUBBBBAwCGgoxiWjuNGZqHpBPzsOWX+LMGOw8iw6fi7d0uV/PiZPaLZFiMVjcXU1uMeiynSvtQjgAACCCAQTaClqz9q59/B7gH5lz+8LRWNXdbhNO7f3128Sk4sWmTVO1c0kZaOoqcggAACCCAwVQL8ykyVLMdFAAEE5rlA4eIkyUqNnOUwmMe3cKF84fxyWVeYEbxJnt/dLP/80NuiU6/CFR0guNvccBEPMJwOdQgggAACRyKgD5i0U89lMLpo598tD78ttSbrr7PoqPivvn+NHFuQ7qy2lhclx7pmBbYas4IAAggggMAEBegAnCAcuyGAAAIIRBcoy0qWjKTwwc7D7a1ToL5y0Uq5wiQHCS41B3vk5t+/Kdv2hc8QPDh8yN8JqFkXKQgggAACCEyGgP6mVDZ3u3b+jZiewR+bhB+Nnf3WWy5OjpOvfWCNLM9OseqdKzFmanAxWX+dJCwjgAACCEyRAB2AUwTLYRFAAAEExCQDWSArTFIQDXzutWj2w4+tXyZ/e+EKSYy19+seGPZnCI40ErDTxF6qNh2FFAQQQAABBCZDoN6MPO8fHHE91B+2HZDtB+ykVbkmFMbXP3CMCYeR5LpvgQmXkRD0W+e6AxsRQAABBBCYoAAdgBOEYzcEEEAAAW8COrphVW6axMeO7ydnffFi+bcr1kpBhh0UvX9oRH79Uk3EN69v75NmE6uJggACCCCAwJEIDJmM9Bpj1q1UNnXJb16qtZpkvjvyL1oYjKS4GMlPT7D2ZQUBBBBAAIGpEhjf3dhUnQXHRQABBBCY0wI6tXdNXprozc54Sp7p/PuG6QQ8KShw+guVB2VXQ2fEQ2mWxl4zWpCCAAIIIIDARAW082/IhJeIVDTu7I/M1F9NWBUoC8zCDecuN+Ev4gJVEV9LTJgMHSlPQQABBBBA4GgI0AF4NJR5DwQQQAAB/xQnDYKek+Y9MYiy6dSovzq7LGQa8S9eqDYxmcZuupzEwyZm007TQaivFAQQQAABBMYroJ17OqLcrejv0IGgNpebGLb6wCtayUqNk7QE7zFyox2P7QgggAACCEQToAMwmhDbEUAAAQQmTUDj+5VmpZi4gCnii/E+6iElwSdXnrjUOg/N+rulssWqc67oCECdmkVBAAEEEEBgvAK1Jp6s2zOkl/YelKd2NFqH1cRXV77H/q2yGry7oqExChe7xwYMtx91CCCAAAIIHIkAHYBHose+CCCAAAITEshMiRcdDZhqOva8lgtX54gGVXeWB7bWyICJCRipNHcNRB3BEWlf6hFAAAEE5qdAV/+QiSU7EPHiD3YPyO2bK63t8SbUxV+fWy6+hdFvrzTxR7xvfCExrDdjBQEEEEAAgQkIRP+FmsBB2QUBBBBAAIFoAjq195j8NMn1GADdF7NQ/uKUZdZh9Qbt0bcOWHXBK1Ut3dLZNxhczToCCCCAAAJhBarN70akor8n331ip2gnobNcdXqxp9+zRBMLNy/oYZbzOCwjgAACCCAwVQJ0AE6VLMdFAAEEEIgqoMHPS5Ykh2T6jbSjJgNZlZtqbX7w9Tpp743cwadhAnc1dMmgyeZIQQABBBBAwE2g1Yzu6+i1O/cC7TXD/Ncfflv2mERTznJKyWLZsCLLWRVxuTgzSTQcBgUBBBBAAIGjLUAH4NEW5/0QQAABBEIElpkboqVmSlS0oh2Gnz61yGrWawK1//cr+6y64BWdJlxhOgEjJQ0Jbs86AggggMD8E9DfiBoT+y9c0ZiAX/vft6WuzU4MkpkcJ9eeVeopm+9i09ZLduBw708dAggggAACRypAB+CRCrI/AggggMCkCGhAdO0IjFY0ichZy5dYzZ7a0SD7WsPftAUa6ijB2oO9gVVeEUAAAQQQsASaOvulxySQCi67TFb5W/7wtmjsP2fRDr1/vGS1pMRHj2erg/6KPPzGOY/PMgIIIIAAApMpQAfgZGpyLAQQQACBIxIoyEiU4iXROwE/tr5Q4kxMwEDRTI0/27RHhqJM893f1ktSkAAarwgggAACowIj5oektjX0IdGr1a3yb398R7r77Y5B/b36l8uOEU3o4aXkm/Ya+5aCAAIIIIDAdAmM3T1N1xnwvggggAACCDgE8tITpSwr2UynclQGLWoW4fcfl2fVakymX5mswNHK3uZu0VEeFAQQQAABBAICBzr6QrLK727sMgk/dslA0MOl8uwU+foHjhH9LfJSNOO9dgBSEEAAAQQQmE4BOgCnU5/3RgABBBAIK5BtMiSuMRmC43yRewEvOz5f8oIyCP/prXp5qepg2GM6K/c0dYkGeqcggAACCCCgSaLqzAhxZxkx8QDvfn6vDGsmKUdZV5ghN7/fTPs1nXpeiv5Oacb7GBJ/eOGiDQIIIIDAFArQATiFuBwaAQQQQGDiAmkJsXJsQYakJYa/ydKpVF84v1xiY+xOwtvMVOBGM5LDrej9nMZ06uiLnD3YbX+2IYAAAgjMHYH9Zurv0LDd0be5olkqzYhxZ9H4s1+6aIXE+6JP5fWZ36YVOSkmrIWOaLd/p5zHZBkBBBBAAIGjJUAH4NGS5n0QQAABBMYtEOdbKGvy0szUqYSw+xZlJsvG04qtbd0mgPsPn6qIGg9Q4wburO80Ad+HrP1ZQQABBBCYPwJ9JpN8Q9BDI6379Ut2SIl8M5Lv+g2l4lsY/fYpOT7GPMBK9zxFeP5oc6UIIIAAAtMpEP0XbDrPjvdGAAEEEJj3AjpyQjv6dCRFuClU563KltPLMi0njQf4gId4gDri450DHaI3exQEEEAAgfknoBnk9YGQszz0ep209dgjxD91apGnzr+s1DhZm59Owg8nKMsIIIAAAjNCgA7AGfExcBIIIIAAAtEENNh6cMw/3Uc7CK89s1RyTdxAZ3nExAN82UM8wIGhQ/J2HZ2ATjuWEUAAgfkg0N0/ZJJC2fFgNUnUH9+ssy7/+KXpcsKyRVZduBUNWVGWlSILifcXjoc6BBBAAIFpFqADcJo/AN4eAQQQQMC7gHYAalyl4JIYZ+IBXhAaD/CnJh6gxnaKVgaGRugEjIbEdgQQQGCOCdQc7Am5ol9trZZBRzxA7cvT0X/RSkLsQjNSPZV4f9Gg2I4AAgggMG0CdABOGz1vjAACCCAwXgFfzMKQkX6BYxSbacJ/GRQPsMfEA/z24zs8JfugEzAgySsCCCAw9wXazRTf4Gm+O+o75IVKO5P8hWtyZemiJFcQfTC1MjfVJKXi1soVio0IIIAAAtMqwK/UtPLz5ggggAAC4xWINApQj3N+mHiADR398r0ndkVNCqL70wmoChQEEEBgbgsMDY/InuYu6yJHTHr4+7ZUW3WazOPKEwusuuAVTfC73Ez7TYoLn7E+uD3rCCCAAAIITJcAHYDTJc/7IoAAAghMSMBtFKDGA7z+7FIpXZJsHXuHyfZ753N75ZC5wYtW6ASMJsR2BBBAYHYLVLX0SP/giHURmyuaZG9zt1X34RMLJTUh1qoLXlm2OEkWJccFV7OOAAIIIIDAjBOgA3DGfSScEAIIIIBANAG3UYDxvhj58ntXyuKgG7JNu5rk4W0Hoh3av/1wJ2C7aIB4CgIIIIDA3BFo6eo3iT/6rQvqNeEifr211qoryEiUC9ZkW3XBK1mp8ZJv2lEQQAABBBCYDQJ0AM6GT4lzRAABBBCwBNxGAWrDRUlx8uWLVkq8z/6Z+/XWGnnJQ2ZgPYZmB95+oEPaewd1lYIAAgggMMsF+oeGQ0b56SU99MZ+aQv6rtfEH76F9m+I8/KzUuNCRps7t7OMAAIIIIDATBOI/Ks2086U80EAAQQQQMAh4DYKUJuVmGnAN567XJw5g3UC8I+f3i27GjodR4q8OGQyQe4wnYA6YoSCAAIIIDC7BSqbuq0Mv3o1jR198sib9ujwdYUZov/CFY35p78vy7NTZaGmCKYggAACCCAwSwToAJwlHxSniQACCCBgC0QbBait1xcvlo+vL7R27B8akX9/5B3ZXtdu1UdaGTG9hhWNXdJgbhIpCCCAAAKzU6C+vS8k669eya/MyPBB87AnUGJMD5+O/gtX4syo8mPy0yQ3PSHcZuoQQAABBBCY0QJ0AM7oj4eTQwABBBBwE4g2ClD3/cDx+XJ2+RLrMNoJ+M1Hd8jrtW1WfaQVzR2iI0dqD/ZEakI9AggggMAMFdAYf9UtdoIPPdV3zAjvF/cetM76wmNyROP/BZe0RJ8cW5AeNSlI8H6sI4AAAgggMFME6ACcKZ8E54EAAgggMG4BHQWonYBuRTMDX3tWach0Lh3x8Z3Hd8pLQTd/bsfa19or+9t63ZqwDQEEEEBgBgmMmGHcFY2doqO5nUXr79tS5aySlHifXHnCUqtOVzTZx5q8NNERgBQEEEAAAQRmqwC/YrP1k+O8EUAAAQT8Avnpif6bNjeOWNNR+KULV8jJJc/dgukAAEAASURBVIutZsPmBvD7T+6S53Y3W/VuKzUtPdJMTEA3IrYhgAACM0ag2ozc7u4fDjmfTRVNUmW+z53lI+9ZKikJPmeVv9NPY/7pwyQKAggggAACs1mADsDZ/Olx7ggggAAC/iDs5TkpEhMlGLuOFrzpvHI5c7k9HVhHhfzEJAZ5ckeDZ809JiZgRx/ZgT2D0RABBBCYBoGD3QOisf+CS8/AkPzXS7VWtU77PX91jlWnK0WZSVF/X0J2ogIBBBBAAIEZKEAH4Az8UDglBBBAAIHxCSTExkjxkqSoO2kn4efOKZPzV2VbbXVm2J2b98qf3rIzQVqNHCvaabirvlP6BkNHlTiasYgAAgggME0C+v28p6kr7Ls/9HqdtPfaD3H+8rSikI6+9MRYWZISH/YYVCKAAAIIIDDbBOgAnG2fGOeLAAIIIBBWIDs1wcRpigu7zVm50Ezj+syZJXLJ2lxntX/5vi3V8uBr+0Pqw1VoDEENID84PBJuM3UIIIAAAtMkcMhkbtptRmoPObL7Bk6lzsRxfeRN+2HPicsy5LilGYEm/lcdVF6alWzVsYIAAggggMBsFqADcDZ/epw7AggggIAlULIkRRJio/+0aSynT59aJB88ocDaX1f+6+VaMzWsRvQGMlrpGxyRnWYkoAaTpyCAAAIIzAyBGhP3r7NvKORk+oeGTdzXChlyfGfHmN+DT55SFNI230wJ1tHlFAQQQAABBOaKQPS7pLlypVwHAggggMCcF9ApvuU5qSZYe/RL1U7Aj55UKB9fXxjS+EEzPey+F6o9dQLqTWZlc/hpZiEHpgIBBBBAYEoFWk3cv7q20Lh/+qb3PF8ltaZz0Fnee0yOaGefs+iDJI0JSEEAAQQQQGAuCdABOJc+Ta4FAQQQQMCfEXjZ4ujxAANUl68rkI0m9lNwefSternzub0y4mEkYFNn+EDzwcdkHQEEEEBg6gR0hF+kuH+bdjXKM7uarDcvXJQoHw3zEKg4M9mfYMpqzAoCCCCAAAKzXIAOwFn+AXL6CCCAAAKhAjqaIzMlejzAwJ4Xr82T688qleCBg0/taJTbn6301AlY3dJtppzZQeUDx+cVAQQQQGDqBapbekxc1tCQDDrq767nqqwT0FF+X7xghcT77Gm+i5PjZJH5R0EAAQQQQGCuCdABONc+Ua4HAQQQQMAvsDwrRTKSYj1rnGsyA9947nLRwO/OssmMGLnDQyeghpTa1dBFUhAnHssIIIDAURLoMA9gWroGQt5NswF///92yUBQwqZrzywNmfqrYSSKMr2PIA95MyoQQAABBBCYwQJ0AM7gD4dTQwABBBCYuMBCcyO3wsQDTE3weT7IGcuX+EeE6E2gs+i0sTs3R58OPDA0IhWmE9BLAhHn8VlGAAEEEDgygepmO7afHk2/i+/cXCl17XZMwAtW54h+3weX/IwEEn8Eo7COAAIIIDBnBOgAnDMfJReCAAIIIBAsoB15K3NTJSnOnuIV3M65vr54sfyNmRYW3An49M5G+bmHmIDtvYOyr7XXeUiWEUAAAQSmUKCxs0+6+kOz/j5pwjg8v6fFeueSJcn+LPBWpVnRKcH56ST+CHZhHQEEEEBg7gjQATh3PkuuBAEEEEAgjEBszEJZlZcq8ebmzmt5T9EiMxKwPKQTUGMC3uWhE1A7ADUTJQUBBBBAYGoFhk38heDMvvqOe5u75d4/V1lvrg+DvnB+ucT5Qn8PtGNQR45TEEAAAQQQmKsCob9+c/VKuS4EEEAAgXkroEHe1+SlmZs+7zd3JxUtli+aG8WYBfY+OqLk7uejTwfWTJSDQTGn5u0HwIUjgAACUyRQ19YrA0N24o+egSF/3L8hDc7qKJ89u0xy0hIcNYcXNfFHRhKJP0JgqEAAAQQQmFMCdADOqY+Ti0EAAQQQiCSQEKudgOn+aV6R2gTXn2SmA39BRwIGdQL+3zvaCVjlGutPM1HWmMyTFAQQQACBqRHQBB/aAegsGvfvtk2V0tjZ76yW963NlfUli606XdFBfyT+CGGhAgEEEEBgDgrQATgHP1QuCQEEEEAgvECimf61tiBd0hK9JwbRmIA6ZSy0E7BB7v6zeydgk7kBDReXKvzZUYsAAgggMB4BfcgSNMhPHn27XrZWHbQOU56dIp84eZlVF1jJz0gk8UcAg1cEEEAAgTktQAfgnP54uTgEEEAAgWABjQmo04Fz0uKDN0Vc11EjN4XpBHxie4Pc49IJaAaiSJWJQ0VBAAEEEJhcgY6+QWnpsmOtVjR0yv0v1FhvlBLv839/+8x3f3DR2LAFpgOQggACCCCAwHwQCP0lnA9XzTUigAACCMxrgQVmSm9pVor5lyxBs3sjupxsOgE/f/5y/3QxZ6PHo3QCdvYNiY4EpCCAAAIITI6ATvMNfrjSaToEf/hUhQzrkxdHueGcMlmSEv6BT3EmiT8cVCwigAACCMxxAToA5/gHzOUhgAACCEQW0GDwq81oQF+Mnegj0h6nlGTKTeeVh+0EvG9LdcSYgDpNTTNVUhBAAAEEjlyg9mCvdPcPjx5oxHT6/fSZPdIcNCLw8nX5csKyRaPtnAua+EP/URBAAAEEEJgvAnQAzpdPmutEAAEEEAgrkJ4YK8fka4Zgbz+Jp5RmyufDdAJq3KmHtx0I+x4DQyOyv9UOVB+2IZUIIIAAAq4C7T2DUtduf58+s7NJXqtts/ZblZsqH3lPoVUXWNF4sGVmBDgFAQQQQACB+STg7W5nPolwrQgggAAC804gKc7n7wTUm0Iv5VTTCfjX54aOBPz11hp5raY17CEOmBtWzVhJQQABBBCYmMDg8Ijsbuoyo63H9m/rGZBfvVg9VmGW0syDHX1QE6MpfoNKrBnxrZ2D4WICBjVlFQEEEEAAgTklQAfgnPo4uRgEEEAAgYkKJMTG+DsBNWC8l3JaWabceO5ycd5e6j3pj57eLXVt9ugUPZ7OAK5qISGIF1vaIIAAAuEEKpu6RUdUO4uGX+gesB+ufG5DadjpvdofuMJ0/un3PQUBBBBAAIH5JkAH4Hz7xLleBBBAAIGIAv4MwWY6sE4L9lJOL1siH11vTzHrMTei//n4TukZGAo5RGv3IAlBQlSoQAABBKILNHT0ycFuO+vvq2bE9ZbKFmvnM8zDmXWF4eP+lZhpv2kJ3r7frYOyggACCCCAwBwQoANwDnyIXAICCCCAwOQJ6JQxnR6WmeItOPzlx+fLaWZKsLPUtffJj57aLSNhEn/sbuySnfWdTAd2grGMAAIIuAj0mgcr1S09VgsNqXD383utuuT4GPn0acVWXWClICNRslMTAqu8IoAAAgggMO8E6ACcdx85F4wAAgggEE1goekELM9OkYyk6CNFFixYINefXSpFmUnWYTUg/W9eqbXqAis6imXbvnbZ19oTtpMw0I5XBBBAYL4L6IOUisbOkEzqv325NiTr76dOKQo7gluz/S4L+o6e765cPwIIIIDA/BOgA3D+feZcMQIIIICABwHt2FuZkyqpCdFjAmo8qS9ftNJMLbPbPvR6nWzZY09PC7z1sLmprT3YK2/saxMNYk9BAAEEELAFDplsH5r0o7vfjvG3x9T9yWRed5Y1eWmyYUWWs8q/nGSSOy03D3QoCCCAAAIIzHcBOgDn+38BXD8CCCCAQEQBHQmo04F1Wlm0siQlXr54wQqJMR2HznKXmaLW1R8aDzDQpm9wRHaYKcE6xY2CAAIIIHBYQEf+7WrokpYu+wGJPjy5Y3OllQlYM/tee2aJ6IMbZ9GQDivMg5xw2YCd7VhGAAEEEEBgPgjQATgfPmWuEQEEEEBgwgK+mIWmEzDNZI2M/pO52oxA2Xh6sfVe2vn30Ov7rbrgFTPIRWoO2vGtgtuwjgACCMwXAX/nn5n2G5z0Q6//4TfqQuIBfvCEpZJnYvwFl5IlyZJoRgBSEEAAAQQQQEAk+t0MSggggAACCMxzgTjfQtHOPX2NVi5ckyMnFy+2mj36Vr00mgyWbkVvdDv6Bt2asA0BBBCY8wLa+bezoVM0a3pw2WZCJgTHVl26KFE+cFxecFPJSYuXrNT4kHoqEEAAAQQQmK8C0e9k5qsM140AAggggIBDQOP8aYypOJ89xczRZHTxL05ZZk05GzI3tL9+KXxCkNGdzEJNUJZL5zaWEUAAgbkuoNN7NSRCW09o51+DeYjyw6cqrKm/+m183VmloiO1nUXDNhRnJjurWEYAAQQQQGDeC9i/lvOeAwAEEEAAAQQiC+hUsmPy06NOKctJS5D3mpGAzrKlskUqzKgWt9LZN2TiXfW7NWEbAgggMCcF/CP/TOdfe29o51/f4LB85/GdIclAPvyepf4Yf04Qn4kHqHH/NIYrBQEEEEAAAQTGBOgAHLNgCQEEEEAAgagCOhLwmPy0qNmBNSZVclDsqftfrDGjV0zAP5dS29obtY3L7mxCAAEEZqVAZXNX2M6/EfOd+dNn9sg+893oLCeXLJYPnlDgrPIvl5q4f/o9TUEAAQQQQAABW4AOQNuDNQQQQAABBKIKxJrpZjodODMlLmLblASfuTldam3XuFZbqw5adcErmg24sZNRgMEurCOAwNwVqDVJkJo67Wy/gat98LX9Id+bhSbu3+c2lIVk/c1NTzDfy8T9C9jxigACCCCAgFOADkCnBssIIIAAAgh4FNDpZeXZKZJnbjgjlYuOyZHsoCD0D2ytkaHhkUi7+Ov3tfaIxsKiIIAAAnNdoLGzL2R0X+CaX6luld++si+w6n/V+H5fumhlyCi/JDPiumhxktWWFQQQQAABBBAYE6ADcMyCJQQQQAABBMYlsGDBAik20800C2W4oiMFP3HyMmtTQ0e/PL69waoLXhkYOiR1bfZ0t+A2rCOAAAKzXaDdJPuobOoOexmv17bKrSbph7OYr1y56bxyk+HXfvCi4f6WmwcyxP1zarGMAAIIIICALUAHoO3BGgIIIIAAAuMWKDSjTrKCRvoFDnKKiVOlIwWd5X9e3Sdb9x50jfV3oL1PBobcRwo6j8kyAgggMJsENNzBrsZOK6tv4Pw37WqUbz+2U/qDvgP/wjxQOW5pRqDZ6Kt+ByfH+0bXWUAAAQQQQACBUAE6AENNqEEAAQQQQGDcAmVZyZKeGBuyn44S/NSpRVZ9t7nx/d7/7ZJ//eN22dscfvSLTgHeZWIGMhXYomMFAQTmgMCgCYPwTn2HCYdghzrQJEm/NzH/frapUoKjIJyxfIm8/9i8kKvX7938jPCjsEMaU4EAAggggMA8FqADcB5/+Fw6AggggMDkCWhH34qcFEkMyvyr77AiJ1V0JGBweedAp9z8+zfNze4eae0JDYDf2TckO8xNMp2AwXKsI4DAbBbQBx/9g/YI5xHT43f3n6vkNy/Xhlyadv599uzSkKQfsTELpCw7OaQ9FQgggAACCCAQKkAHYKgJNQgggAACCExIwGdi/q3KTRW9KQ0u15ubV80cHFx0/MumXU3yt795XbYf6AjeLB29Q7KzvlP05piCAAIIzHaBJpPlvKXLfuCh4Q6+/+QueSJMfNQPHJcnN5xTJvr9GlxKs1Ik3hcTXM06AggggAACCIQRCP0lDdOIKgQQQAABBBDwJpAQGyMrTSegBqV3lqQ4n3z1/av9AeyXpMQ5N/mX+8xomB8+WSF9g8Mh29p7B2WnmQ5MJ2AIDRUIIDCLBLSjr7olNOzBnc9VyktVrdaV6FfoxtOK5C9OKTLfp0FfqGZbdlq8LE4O/S61DsIKAggggAACCIwK0AE4SsECAggggAACkyOQmhBrpqXZiT/0yDpN+LSyTPnPj6yTj60vlIRY+2dYO/oe3lYX9iTaTLZMOgHD0lCJAAKzRKCyuUsGg+L+PWtGQG+uaLauwGeeoHzeZPu9eG1ozD9tmJrgk5JMpv5aaKwggAACCCAQRcC+84jSmM0IIIAAAggg4E1gSUq8ZCSFJgXRveN8C+WKdQXyvY+u808Zdh7xj9sOyMFue3pcYLt2AmrWTEYCBkR4RQCB2SLQ2Nknrd2D1ukeaOuVu57fa9Xpg5F/vGSV/2GJteHdleT4GP/35sLgYdbhGlOHAAIIIIAAAqMCdACOUrCAAAIIIIDA5AosW5zkesCMpDj5zJklZmTgWLN+M0Xuv18JDYIfaKE30G/XdYhOpaMggAACs0Ggf2jYTP3tsU5VMwH/8KkK0e88Z7n+LBMvNT/dWTW6rJ2Dq3LTwsYDHG3EAgIIIIAAAgiEFaADMCwLlQgggAACCBy5QHK8T8LF+3MeeemiJDlvZbazSp7Z2RQ2TlagUVf/kLy5v126zSsFAQQQmOkClU3dMhQ09fdXW2ukKqhT8FzzXXha2ZKwlxPnWyCrTSIlHUFNQQABBBBAAIHxC/ALOn4z9kAAAQQQQMCzQKEZBegc4Rduxw+/Z6nJZDn2k6z5fvXm2K3oCEAdCRhpurDbvmxDAAEEjpZAY0efaPgCZ3mlulUefaveWSUFGYmy8fQiqy6w4jOZ1XXknyZZoiCAAAIIIIDAxATG7jYmtj97IYAAAggggICLgN6wZqfGu7QQEyswTi47Pt9qs21fu2zb12bVBa8MjxySXSY7cJ2Jo0VBAAEEZpqAP+vvQXvqrz60+NmmPdapxpoOvpvOLzcPQkI7+DTUn2ZW1xHVFAQQQAABBBCYuAAdgBO3Y08EEEAAAQQ8Ceg032jx6t93bF5I0pD7X6yJmvDjkBkuqLG1djd2RW3r6WRphAACCEySQI3p/HNO/dWHFj96ukI0jIGzfPrUIgkXM1Wn+x5TkC5pJrM6BQEEEEAAAQSOTIAOwCPzY28EEEAAAQSiCuhNbF56oms7HSn40ZMKrTZ68/xsRZNVF2mlqbNfth8gOUgkH+oRQODoCnT0DYp+LznLr1+qkXcOdDqr5OTixXLB6hyrTldSE3xyrOn8S2HkX4gNFQgggAACCExEgA7AiaixDwIIIIAAAuMUyM9IMJkrHel+w+y/oTxLNGags/zm5Vpp77XjZzm3O5c7+w4nBwkeXeNswzICCCAw1QKHzNDkvSbxh7O8WNkif9h2wFnlT5J03dmlJk6q/d2YnRYva0j4YVmxggACCCCAwJEK0AF4pILsjwACCCCAgAcBX8xCyTdB7t3KQjNP+JMnL7OatJrg+f/yh7c9J/vwJwcxGYKDR95YB2UFAQQQmEKBA+190jMwPPoO+1t75WfP2nH/Ysz33U3nlVsj/LQfsDQrWcqyUkS/DykIIIAAAgggMHkCdABOniVHQgABBBBAwFUgNy1B4nzuN7XHF2bIcWbam7PUtfXJLQ+/bTr1+pzVEZdNmC1/TMAaExuQggACCBxNgf6hYdlnOvwCpWdgSL77xE7pGxwJVPlfN55WLOU5qVbdKpPsI8d8T1IQQAABBBBAYPIF6ACcfFOOiAACCCCAQFgBHfGyJi9dFifHhd0eqLzeTIkLzhzcaGJp3fLwdjnQPnZjHWgf6XW/yQ68r5VOwEg+1COAwOQLaFIiTfahRacC37apUurMiEBn2bAiy8T9y3ZWmY6/eH9GdKuSFQQQQAABBBCYNAE6ACeNkgMhgAACCCAQXSAxLkZWmlEuxxSk+YPch9sjMyVevvaBYyQ/3R4J09I9IP9iOgFrTXIQr6X2YK80dtg33173pR0CCCAwHoG2ngFp6RoY3eXhN+pka9XB0XVdKFmSLNecUWLF/dNESeGyAFs7soIAAggggAACRyRAB+AR8bEzAggggAACExNIS4iVtWaqr3YGJplOweCiowT/6dI1IUlB2kxCkH/5w3apaLAzaQbv71yvbO6WVtN5SEEAAQSmSmDEjPrba75rAmV7Xbv82iQxchbN6Ps3F5SbUAj2LYh2CmqcVAoCCCCAAAIITJ0Av7RTZ8uREUAAAQQQiCqgHX3Hmo7A+NjQn+SMJNMJ+P7VUmpujp1Fs/zeYjoBn3ynwVkdcdnMwpNdpsOws89bNuGIB2IDAgggEEGgxoxMDsT502REd2zea6YAjzXW6KefP2+5ZKXaI5v1OzBaWISxo7CEAAIIIIAAAhMVCL3bmOiR2A8BBBBAAAEEJiSg2S4jTX9LNSMFbzadgCuDguVrjK07n9srtz9bKXqzHa1oSK6d9Z3S68jMGW0ftiOAAAJeBHSEsWb+DZQ/bKuT+qDQAx85qVCOW5oRaOJ/9cUskOIlSVYdKwgggAACCCAwNQJ0AE6NK0dFAAEEEEBgXAJLTNy/1ARf2H2S4nzyD5es8o8UDG7w9M5GMyX4bRN3qz94U8j64PAh2X6gw1OHYcjOVCCAAAJhBPQBxJ6mrtEtDabj78HX94+u68Ly7BS5fF2+Vacr+uAj3hcaAiGkIRUIIIAAAgggcMQCdAAeMSEHQAABBBBAYHIEijIjj4RJiI2Rv794lVx6XF7Im+1p6pb/7/dvisbcilb0Zr2qZSxOV7T2bEcAAQTcBLTzTx8uaNGsv/f+uWp0XesWmLm/mvRjoS44ij7wyEmzpwM7NrOIAAIIIIAAApMsQAfgJINyOAQQQAABBCYqoNN9s1LjIu4eY6YKf/KUIrnJxNGKDwqi39E3JP/+px3yYmVLxP0DGzRL50GSggQ4eEUAgQkK1LX1SlvPWGzRV6pb5bXaNutoF63J9Wf+dVaarzIpy0pxVrGMAAIIIIAAAlMsQAfgFANzeAQQQAABBMYjUGimxOnNsVs5rWyJ/OvlayU3aPSMxgX8wVMV8vSORrfd/ds0W+fQcPTYgVEPRAMEEJiXAt0mGVGtSfwRKH2Dw3LvlqrAqv81PTFWPnrSUqtOBwKWmSnBiWGyn1sNWUEAAQQQQACBSRWgA3BSOWfPwRaYv768/DvnnHNmz0VxpggggMAcENB4WPkZiVGvRDsKv3HFWjmh0A6qr1k3b99cKRqE363oVGDN2klBAAEExiugDxsqGrtEkwsFisb9azaji53lU6cWicYwdRbNaq4xTykIIIAAAgggcHQF6AA8ut68GwIIIIAAAlEFtAMwzhdlGKA5SnK8T7783pVyweqckGPe/2KN/PqlGn9MrpCN71Y0dPRLR9/Y9L1I7ahHAAEEnALVJo6oM6P4fjMV+A/bDjibyJq8NDmjLNOq0zin2UEjl60GrCCAAAIIIIDAlAnYj+Sm7G048EwV+NznPic33HBDxNNLTk6OuI0NCCCAAAJTI6Cx/goXJZnMmtGTdWhg/WvOKJaU+BiTedMe9feQWddpelefbgLwR5hXXGne47iC9Ijbp+YKOSoCCMxWgbaeAdGHB4GiiT/ufn6v6KjAQIkx30tXm+8lnW0SKEsXJXoa3RxozysCCCCAAAIITK4AHYCT6znrjpadnS1r166ddefNCSOAAAJzXSArNd5/k91lOvCiFb3J/tj6Zf4RgTryz1n+751G6R4Ylhs2lIkvJnTgv47i0dE7OqWYggACCLgJaNzQ4AcT+h3zdl2Htdv7TbbypeYhRqDkpSfwHRPA4BUBBBBAAIFpEgi9E5imE+FtEUAAAQQQQGBMQDv1VuWlSmqC92d1lx6XL9efXWpG3YwdR5e27GmR/3xil/QPDdsb3l3TTJ49A9E7GsPuTCUCCMwbgaqWHtH4oYHS0NEn979YHVj1v2Ymx8kHTygYrdOHGcUm7h8FAQQQQAABBKZXgA7A6fXn3RFAAAEEEIgoEGtG7GkcrcXmhtprOXdltnzh/HLxBU35fb22Tf7fIzv8U4KDj6Uz9yoauqyYXsFtWEcAgfkt0No9IE2dY1N/R8zU359t2mMeLIx1CKrQtWeVSkJsjB8rPnahlND5N7//w+HqEUAAAQRmjAAdgDPmo+BEEEAAAQQQCBXQ2H0rclJM4HzvWTNPKcmUr5jkIPE++2d+Z0On/Osft4vG8AouPWYq8Jv726Wxsy94E+sIIDDPBXTqb2WzHZP00bfqZUd9pyVz/qpsWefITF6cmSwa05SCAAIIIIAAAtMvYN8ZTP/5cAZHWeC3v/2trFmzRpKSkiQ1NVXKy8tl48aN8vTTTx/lM+HtEEAAAQQiCeh04LKsFBNTKzFSk5D645ZmyM3vW23iAh4eiRNoUG2m8N3y8HZrJE9gmwbx39PYLbsbO62A/oHtvCKAwPwUCJ76u7+1159l3KmRlRIvnzylaLRqUXLsuEYvj+7IAgIIIIAAAghMicACk7lrLGXXlLwFB52JAs6sbJHO74orrpB77rlH0tPTIzWJWL9v376I23TDgQMH5OSTT/a3qa2tlaVLl7q2ZyMCCCCAwGEBjbm114zE8frrXXOwx0z9fUfaegctwmwTl+vfPnisyR4cPsZggpm6V56TGnG7dTBWEEBgzgro1F/nSD99UPC1/30rJBnIP126xh+yQCF00N/xZiRgYCrwnMXhwhBAAIFZIqD354WFhf6z5f57lnxoU3CadABOAepsOGRycrJcdtllcv7558uqVaskJSVFmpqaZNOmTfKzn/1MWlpa/JexYcMGeeKJJyQ2NnZcl+WlgzFwQL6AAhK8IoAAAt4ENDNwhZnO2zdox96KtLd2Gv676QRsdMTv0rbrixfJ31ywwiQNCT9FT6uXZ6fIEjOyh4IAAvNPQDv7Xq9tNYk/xsYL/P61/fKbl2stjEvW5spfnlY8WqejlcksPsrBAgIIIDDtAnQATvtHMCNOgA7AGfExHP2TaGtrk4yMjLBv3NDQIJdccom89tpr/u0/+MEP5KabbgrbNlJlpJvJcO3pAAynQh0CCCDgLqAxuXQkYHNXaDy/cHseNKN4vvmnd6TWTN1zlqvPKJaL1uQ6q6xl7QQsN52AmXQCWi6sIDAfBOrbD484DlxrVUu3fPXBt6wQAfnpCfL/PnScxL0bc1RHDx9vQhBo/FIKAggggMDMEKADcGZ8DtN9FsQAnO5PYJreP1Lnn55OTk6O/Pd///foqL9bb7113GepnXpu/7Zu3TruY7IDAggggMCYgM9kCNYpuqVZyf7pdmNbwi9pJuF/uGR1yJTeX75QLXpTH6noVOOKxi7RaYAUBBCYXwL1ZvRwoPQPDcutT1VYnX/6gOBz55SNdv5pW836S+dfQI1XBBBAAAEEZo4AHYAz57OYUWdSWloqF154of+cdu/eLXV1deM6P43p5/YvLy9vXMejMQIIIIBAeIGctAQ5dmm6ibUV/SddOwH1Zt1ZBocPya1PVpjpxMPOamtZOwF3mSnH4bIHWw1ZQQCBOSOg/7/3muzggfKLLdVS1zbWIaj1lx+fb8IEpAaamJHCcZKRFDe6zgICCCCAAAIIzByB6HcLM+dcOZOjLKDZgQNl//79gUVeEUAAAQRmmEBSnE/W5Kd56gQ8cdkied+x9kOYOjPN7+7n97pelQkFJjvrO6U9KJmI605sRACBWSvgHP23de9BeXJHo3UtOtLvyhPHkrjFmCm/RZlJVhtWEEAAAQQQQGDmCNABOHM+ixl3JuOJ4zfjTp4TQgABBOaZQLwvRlbnpUm8h5GAn1hfKKXm5t1Znq1ols0VTc6qkOVAJ2BHn51ROKQhFQggMKsFdERwa/fh/89buvrl9s17rOuJN/H+Pn/uctFQBIFSuDhR9HuIggACCCCAAAIzU2DsV3tmnh9nNY0C27dvH333/Pz80WUWEEAAAQRmpkBCbIysMZ2AgWD8kc5Sb9pvOr9cEk17Z/n5c3tlf5udJMS5XZc1K2hNS09wNesIIDCHBDT5h5YR8//7j57eLd39Y1OBtf6q04slLyNRF/0lNcEnuSYcAQUBBBBAAAEEZq4AHYAz97OZ1jPbu3evPPHEE/5zKCsrk4KCgmk9H94cAQQQQMCbwFgnoHsGTo0deO1ZJdZB+4dG5D8e3RE11l9n35BrzEDroKwggMCsEtBO/iYz6k/Lg6/vlx1m6r+znFaaKRtWZI1WaSIQTUbEzJFREhYQQAABBBCYkQJ0AM7Ij2VqT+rhhx+WoaGhiG/S0NAgV155pQwMHM74eMMNN0RsywYEEEAAgZknkBh3eDpwbIx7J+DpZUvk3JXZ1gU0dvbLfzy20wr+bzV4d6X53Q6CcNuoQwCB2SvQZL4DhkxyIE3887tX91kXssQk+fjMmSVWZ1+BGQmocUgpCCCAAAIIIDCzBfi1ntmfz5Sc3ec//3kZHBz0d/KddtppUlxcLImJidLc3CzPPPOM3Hbbbf5lffMzzzxTbrzxxik5Dw6KAAIIIDB1AnpDrjEBd5qb+P7BkYhvtPH0Iqlq6Za9zd2jbXT5+/+3S75y8UrxLQz/rLCla0CWLiLg/ygaCwjMEQFN/jE0MiI/eWa3mMGAo0VH+v31ueWSHD92+6APG7QDkIIAAggggAACM19g7Bd85p8rZziJAnV1dXLrrbf6/0U6rI4CvPPOOyU+Pj5SE+oRQAABBGawgN6oH1eQLnuauuVg9+FR3cGnq0H7/+69K+Vr//u26Oi/QNm2v11uf7ZSPrehzBrtE9jeMzBs4oINWZ0BgW28IoDA7BRo6xnwj/59ofKgNHSMfR/o1WjG35W5qdaF6dTfhSb7LwUBBBBAAAEEZr4AHYAz/zOa9DO89957ZdOmTbJlyxaprKz0j/br6OiQlJQUKSwslNNPP102btwoOjqQggACCCAwuwU04YfetGtyj9qDPXLIMaIncGUZSXHyD5es8ncCany/QNlsMgMvTo6Tj69fFqiyXnUasHM0kLWRFQQQmHUCOvrvkPmSePiNOuvcS0zW8A+us+NB56YnSFpCrNWOFQQQQAABBBCYuQJ0AM7cz2bKzmzDhg2i/ygIIIAAAvNHQKfpaabOCjMleGAotBcwLz3RjARcJd/443bRZCCB8tDrdZKRGCcXr80NVI2+NptpwEWZyaPrLCCAwOwV6BscNgmABuVNM/q3xjwscBbt/HOO9NNM48sWEwLAacQyAggggAACM10gfGCfmX7WnB8CCCCAAAIIjFtAR+scW5AhKY4YXs6DLM9OkZvOL5fgGX33bqmSJ7bXO5v6lwdMR2F772BIPRUIIDC7BHoGhqTShArQEcLBo//yzEi/9xQtGr0gjQW4PCtFYoK/KEZbsIAAAggggAACM1GADsCZ+KlwTggggAACCEyRgI7c0SnB8bHh/wQ4cdkiufbM0pB3v+v5Knns7dBOwBayAYdYUYHAbBHQOJ6a7feN2nZ/Z74mAHqrrsM6/fcfl2eN/lu6KFHSk5j6ayGxggACCCCAwCwQCP/X/yw4cU4RAQQQQAABBCYmoJ2Aq3PTxBcTPnj/uauy5SPvWRpy8Hv+XCV/euuAVa/JRTRmGAUBBGaPQJfp+NtR3yHb9rWLZvQOlIe32bH/0hNj5azlWYHN/pigZP8e5WABAQQQQACBWSVAB+Cs+rg4WQQQQAABBCZHIDEuRlbkpIZM9w0c/UMm4+eHw3QC3relWv64bawTcHD4kD9uWGA/XhFAYGYLaDKgN03HX2u3PX2/0SQAeaGyxTr5i4/JFX1goCXBjBouM1l/KQgggAACCCAwOwXoAJydnxtnjQACCCCAwBEL6OieUhPLK1K50nQCfvSkwpDNv3yx2ooTptmAKQggMLMFRkYO+ZMA7WvtDXuij7xVb2UJjzcdfxesyfG31Xh/GjpAs4pTEEAAAQQQQGB2CvArPjs/N84aAQQQQACBSRHISo2XwsWJEY/1wRMK5BPrQzsBf7W1Rl6pbvXv12oyhw6bzgUKAgjMTIHB4RHZfqBDNHN3uNLRNyhP72i0Np1vQgEEEgaVLEmWpDiftZ0VBBBAAAEEEJhdAnQAzq7Pi7NFAAEEEEBg0gU0pld2WnzE4162rkA+ecqykO1/eDdemHb+aSxACgIIzDyB3oFheWt/u3T2DUU8uSe2N8iA6SQMlBiT6veSY/P8q7kmC7A+KKAggAACCCCAwOwWoANwdn9+nD0CCCCAAAKTIlCSmeyP8RXpYJcely8fDxoJuKO+U5o6D0//ZRpwJDnqEZg+gfbeQZPVt136Bsc694LPRjvvgzN8n1aWKUtS4kVjhRZnJgXvwjoCCCCAAAIIzEIBOgBn4YfGKSOAAAIIIDDZAgtNjK+y7BQxA38ilveZEUHJpkPAWf68p9m/qh0NdW290j807NzMMgIITJOATuvdaTrph0yinnBlT1OX/OipCrnpgddCRgdeetzh0X8FGYnmO8HlSyHcgalDAAEEEEAAgRkpQDCPGfmxcFIIIIAAAggcfYG0hFjJTUuQA+19Yd881iQAOKU0U55yxAp7bvf/3959wMdR3P0fH9uSLMuW3HvHBmOaMaaG3gMECL2EJCQECEkIySs8BJIASYA/ARLCA4FQHgIhCS2EDqH3aooptsGmueDeu2zJvv98x8x677R3ujudpLvVZ14v+fb2dmdn3rterX43ZaE5YswAu307M33RajPDzjBaXVlmetvWQz06VzBpQKQkKxFoXoGVa+td8C91bM5EImHGf7HYPD5xjpk6b2VkIcYM6mqGftUiuFeXishtWIkAAggggAACpSdAALD0zhklRgABBBBAoNkEBveoMktWr0vbZXD3kb2SAoCaUVRBPwUMlGx8wSxfU+9+vli4yvS0gUB1IWT20GY7ZWSMQJLA6nX15mM74Udqy7/auvXmOtvi790ZS5O2D78p79AumPl7YHda/4VtWEYAAQQQQKDUBegCXOpnkPIjgAACCCBQQIEOtivwZr27pM1xy37Vpqdt2RdOagUYlTQxsMYI/MBNQFAXtQnrEECggAIK8n1kg391Kd1+Nc7fbx+ZlDb4V2Fb9x4wuo+58pgx7v9/ZXl714q3gEUjKwQQQAABBBBoZQECgK18Ajg8AggggAACxSbQtVO56ZtmVuD2djwwtQIMp9c+W2Q2qOlfmrTWTkAwafZyM8uOEaguiCQEECi8gMbfnGyDf+vqk/+PTV+0ylz40ETXRT/1qOqmf5Kd3Of6k3cwp+2xmdGMv0q0/kuV4j0CCCCAAAKlL0AX4NI/h9QAAQQQQACBgguoS+9SO7GHgnepSQHAh9+fHaxW6yK1Otp6QNdgXeqC4n4z7BiBy1bXmZF2spGKMr6DTDXiPQL5CmywzW0/mrOiwf/XCTOWmGttt9/UWYC7dCwzp35tmB3Ts4cpa5/8f5HWf/meBfZDAAEEEECguAWSf+MXd1kpHQIIIIAAAgi0kIC6Ao/oFd0VeIgdJ1A/4fRqmm7A4W20rNmCP5y11L2mfsZ7BBDIT2Du8lqzZl3yDNxPTZ5rrnpqSoPgnyb6ueTIbVxL3tTgn47OzL/5nQP2QgABBBBAoNgFCAAW+xmifAgggAACCLSSQNeqcjO818bJPVKLkNoN+E07s+i6+oatBVP303t1UVSLwfk2aEFCAIGmCdSv32Bm2+714fTghFnmtlenuUl5wus1hufvj9w66Oob/kzLHTX2X3XH1NW8RwABBBBAAIEYCBAAjMFJpAoIIIAAAgg0l4DGBBvRp7OxQ/8lpd1H9DThVatt66MJM5ckbZPpjboEf7ZglZlmZwpmXMBMUnyGQGaB2Utrg0k/9H/pvndmmnventlgJwXtf3XoaFNdWd7gM79iUDdm/vUWvCKAAAIIIBA3AQKAcTuj1AcBBBBAAIECC/SprjSb23H7wkHAnl06mi37VycdKdtuwOGd5iyrNR/PXWHUiomEAAK5CWjijznLNrb+U/DvXhv4+8+7sxpkcvQOA82P9xlhyu1sv+kSrf/SybAeAQQQQACBeAikfwqIR/2oBQIIIIAAAggUQMAF/Gz3QTs0YJD2GNk7WNbChBlLzcq19Unrsnmz1E4MMtHOElxblzyGWTb7sg0CbVngyyVr7AzcxrWivXP8DPPge5sm5/EumuzjuHGDbQA/9J/Xf2hf9X+6Z5eKr4L80duENmcRAQQQQAABBEpUgABgiZ44io0AAggggEBLC3SrqrCt/mqMJghR2mW4ZhDdFDCot5GI8XYswHySJjD4dP7KfHZlHwTapID+zyxYsdYF/+54fbp59IM5DRxO22O4OXjrfg3Wa0VVRQczrFeV2WFod7NF3+qMXYMjM2AlAggggAACCJSUAAHAkjpdFBYBBBBAAIHWFejaadPEIJ07lpmxQ7olFUhjj/3j9Wlm8uxlZr2aJuWQVtTWm4Ur1+awB5si0HYFZixe7Sb5uPutmeaJSXOTIBSWP2OvzcwBo/smrdebzh07mG0G1pgxg7uZ/l07ZewW3GBnViCAAAIIIIBAyQqUlWzJKTgCCCCAAAIItIpAz84VZlqHdnbcvoTRxAJvTds0+cfyNXXm8Ylz3Y8CDWMHdzf7juptthrQNauyKqjRw7Y0bB9qWZjVjmyEQBsSWFFbZxavWmc++HKpefj95G6/6ul71t4jzJ6bJ3fRF083O7O3Wvv5VrxtiIyqIoAAAggg0OYFaAHY5i8BABBAAAEEEMhNQMG57jZIp6QAX41tFRiVVq1db175dKG55LGPzA0vfGpW2hZ+jaW1dRvMnOW1jW3G5wi0aQEFyhUEvPHFz5IcFDf/yb4jI4N/fWvsxD12HE+Cf0lkvEEAAQQQQKDNCBAAbDOnmooigAACCCBQOIHedhZgpYqy9uan+400fao3vk93hJc/WWjOve9981YWYwTOXrrG1DErcDpK1rdhgQ22W/1cO3P2Mjtxzv+98oVZYl/D6du7DjVfG9ErvMotD+1ZZTbrrZm8N43Z2WAjViCAAAIIIIBArAXoAhzr00vlEEAAAQQQaB6Bmk5lLvi3rn6D2dp2773mhO3NtEWrzTvTF9ufJW459cjLbPfgq5+Z6iYP+d7uw43GE4xK6lqs2U2H9+oc9THrEGhzAqvs7Nrz7YQfi+wYmXX2/8dLnyxoMOHOdoO6moNSJvxQi8CRfbrYWX4zB+jbHCgVRgABBBBAoA0KEABsgyedKiOAAAIIINBUAbUkUivAWba1npLeK2Cnn2PHDXaTebzx+SLzwIRZZrWdrTSc3rStACfNXm5+bLsqbm8nIohK82w34H41laaTnamUhEBbFEgkEi7op/8L6k7vk97f/to0/9a9drET8vzQjvvXPtTCr9yO0znKdvmtrowOtCdlwBsEEEAAAQQQiL0AXYBjf4qpIAIIIIAAAs0j0Kt64ziAUbn3ssHBb2w3wFx17BizQ8pMwdp+pW3R9Kenppj3Zm6aQCScj419mOmLV4VXsYxAmxFQ8O/T+SvN5wtWJQX/NLP29c9/amrtWJnhdMaemwXjcmq9uuarZS7Bv7ASywgggAACCLRtAQKAbfv8U3sEEEAAAQTyFqiqKDOa6TdT6mFnDD73oFFuYgK1UgqnehvMuPrpqebDWcvCq4PlJavq3FhnwQoWEGgDAhrnb8q8FbYV7bqk2mr9v9+ZaT6xgcFw0izbOw3vEayqsq1mtxlYQ+vZQIQFBBBAAAEEEJAAAUCuAwQQQAABBBDIW0At/RpL6h68+8hetjXgdmbHod2TNtd4Zn98coqZPDs6CKhWgAp8kBBoCwL1dvKbj+YuNwp++zTfdvm99+2Z5uy7J5iH3pvtV7tXzez7nd2GBeuqK8tsy78a07Esc2A+2IEFBBBAAAEEEGgzAgQA28yppqIIIIAAAggUXqBnlwo7/l92+XarqjA/P2ALs6cNBobTOhv0uNIGAT+2gY/UpLHP1BqKIGCqDO/jJqCZrz+as8IsX1PvqvbWtMXmsscmm3Puec+Npbl4VXKLQE3w8RM7jmZl+cZgn1rbbtW/xpR14PE+btcG9UEAAQQQQKAQAjwhFEKRPBBAAAEEEGijAmppVJPDJAPtbdRCkxXsNqJnkthaO5vwlU9MMZ/YYF9qWrq6znV71LhoJATiKKDZtDUxjsbGVLrzzemue/xEuy5dOn7HwXaG32r3cW87HucWfbsY/f8iIYAAAggggAACUQIEAKNUWIcAAggggAACWQtkmgwkKhMFKX60zwizc2jcMm23pm69+cMTH5s5yzbOLBzeV62fNCkCQcCwCstxEViwcq1Z89Vs2Wr598gHc9JWbTM70/ZP99vcHLn9QLdN7+qOZkTvLm4m7rQ78QECCCCAAAIItHkBAoBt/hIAAAEEEEAAgaYJ9Ozc0XTIseVRWfv25mzbfXFcypiAq20Q5KYXPzcbIlr7aVKEzxYQBGza2WLvYhRYunpj995FNhB400ufNSiiJts5eOt+5g9Hb2suO2rboAVtHzsG4Mg+BP8agLECAQQQQAABBBoIEABsQMIKBBBAAAEEEMhFQMG/7lXluezittVYZefsv7nZfnC3pH015t9Tk+YmrfNvFqxYZz5fuMq/5RWBkhdYbye5WVFb78a5/MvznxqNexlOJ9iuvjecPM6c+rVhZmjPzsFH/bpWupZ/wQoWEEAAAQQQQACBDAIEADPg8BECCCCAAAIIZCeQzWzAUTmVfxUE7GO7MYbT3W/NNPPs7KdRaf7ytXa8tGXmyyWrzTI7PqBmTiUhUKoCy9bU2a7txjzw3iw7EU7yGJi72G7yR24/wFSUJT+y97fBv+G2KzAJAQQQQAABBBDIViD5aSLbvdgOAQQQQAABBBAICXSzLQAH9+hkajqVmRx7A7tZTM/ca7NQbsZoUpCbX4ruCqwNNVPqzMVrzOQ5y83b05eY92cuNdNoGZhkyJvSEFAAUDNg/+fdL5MK3MvOsH36npslje3XqaKDC/wNI/iXZMUbBBBAAAEEEGhcoKzxTdgCAQQQQAABBBDILNCuXTszqHuV/TGuK+MKO5vpchvY0Ay+fmbTTDlsNaCrOWB0X/PMR/OCzRTce/aj+ebArfoG66IW1HpKYwfqR90iK8s7RG3GOgSKUmDm4lXmL8996loB+gIqiH62neijc8eNAfWeNhjYp6Yypxm3fV68IoAAAggggAACEqAFINcBAggggAACCBRUQLP8du2kFoFVZttBXc3mfbs06MIYdcCTdx5i1OopnO4cP90stBMjZJsUcCQhUCoCa9bV2+DfZ2aRneU6nI4dN9hs0bfaDOtV5SbKGdmnmuBfGIhlBBBAAAEEEMhZgABgzmTsgAACCCCAAAK5CGh8QE30Mah7p4zdg9W9UV0ew6m2boO5xXYFTkTMChzezi8v+Wo2Vf+eVwSKWeDlTxaa8dMWJxVxq/415sgxA1wwvH/XTkaT5ZAQQAABBBBAAIGmCvBE0VRB9kcAAQQQQACBRgU0U7BaBI6xgUB1Z0yXthvUzew7qnfSxx/MWmaeSDMrcNKG9o26HWtWVRICpSDwwtQFScXsYrv8/njfkTbot/H/S9KHvEEAAQQQQAABBJogQACwCXjsigACCCCAAAK5CWh8PnVt1Fh96dIpuw41PTonBwnveH26ue3VLxqd8VexP02qQEKg2AXUqvXDL5clFXMfG/zWtc9YlkksvEEAAQQQQACBAggQACwAIlkggAACCCCAQG4Cw3pWuXECo/aqqigzp+0xvMFHT02eZy55bLJZnDJeWuqGdANOFeF9MQroOv1k/oqkoik4rtZ/A7t1SlrPGwQQQAABBBBAoKkCBACbKsj+CCCAAAIIIJCzgGYN1uQgleXRjyI7DOluvrn9gAb5Tp230vz6gQ/Nx3OXN/jMr2AiEC/BazELvDdjqdEYl+G0eZ8uLvjHuH9hFZYRQAABBBBAoBAC0U/dhciZPBBAAAEEEEAAgQwC5XZyg1H9qo3GB4xKJ+w0xJy19whTbltEhdNS28X30kc/Mre8/Ll55qN5Zuq8FTaQsj7YZF39BrNybX3wngUEilFg/LQlScXqW9PR9LVd4/vVpO8en7QDbxBAAAEEEEAAgRwEynLYlk0RQAABBBBAAIGCCqi7r1o9TbFBvKiJfvfaorebPOTPT081C1auDY693m783Mfzg/daUABljJ1E5Jhxg8xS271SEyqQEChGgbr1G8xEO7lNOG3Rp9oMsRPltE8TEA9vyzICCCCAAAIIIJCrAC0AcxVjewQQQAABBBAoqEB3O+mBZghOl4b36mwuO2obs93Aruk2cevnLV9rNE7g3175wgYAmQgkIxYftqqAJqpRy9Vw2nZQV9OrS8fwKpYRQAABBBBAAIGCCRAALBglGSGAAAIIIIBAvgKa9KBXl+SZf8N5VVeWm19+fcvIcQHD22n5rWmLzZyltUZdgUkIFKPAZ/NXmvkrNrVoVRn3G92nGItKmRBAAAEEEEAgJgL0jYnJiaQaCCCAAAIIlLrAZr27mDV1y8yqtZvG8wvXSV0jNS7g7iN7mXemLzHTF682MxatNrOXrUnqPrwhYcyEmUvMmCFdTZ9qxlMLG7JcHAJvpYz/V1XRwYwd3L04CkcpEEAAAQQQQCCWAgQAY3laqRQCCCCAAAKlJ6DJQLboW+3GRqtbb6N4adKg7lVGPz6ppd/VT08x73+5aUw1BQi/OXYgAUCPxGvRCKxeV28mz0mexXrrATVpJ8MpmoJTEAQQQAABBBAoaQG6AJf06aPwCCCAAAIIxEugsryDnRSk2rRLnvg3YyUrytqbXYb3TNrm/S+XmgW2i+UGNQckIVBEAhqf8pOU8f92GELrvyI6RRQFAQQQQACBWAoQAIzlaaVSCCCAAAIIlK5A16pyNxtqLjUYO6SbCccMa+s2mA9si8AVtfW5ZMO2CDS7wHw7Wc3nC1clHWfn4T2S3vMGAQQQQAABBBAotAABwEKLkh8CCCCAAAIINFlgQCOTgqQeoFtVhRnZp0vSanUDXrJ6XdI63iDQmgJr69fbrupLzfpQy1QFrgkAtuZZ4dgIIIAAAgi0DQECgG3jPFNLBBBAAAEESk5ghJ0UpHPHDlmXe8ehyd0o352xxCxelTzTataZsSECzSCgbulT5q5IynlYr85Gs1yTEEAAAQQQQACB5hQgANicuuSNAAIIIIAAAnkLaNZfTQpS0ym7OcvGDU3uRrl41To72cIKs2Zd9KzCeReMHRHIU2C+DQBOTRn/b9uBXfPMjd0QQAABBBBAAIHsBQgAZm/FlggggAACCCDQwgKaFGTrAV3Nlv2qTVVF5taAA7pVmn41lUklfNd2A1bARV0vSQi0psAyO/lHrQ1GT52/MqkY41JariZ9yBsEEEAAAQQQQKBAAgQACwRJNggggAACCCDQfALdO1eY7QZ1NSP6dDaa9TcqtbNTB+84LLkb8Ns2ALjaBl0mzlpuVq1lQpAoN9a1jMCClbVmnp0AZPmauqQD7rZZ8gzWSR/yBgEEEEAAAQQQKJBA9BN0gTInGwQQQAABBBBAoFACCvD1qa40Ywd3MyN6dzZq8de9c7npZFsG2t7CLo0bkhwAnLF4tZm/vNasq99gJs1ebpbYbsEkBFpaoH79BrNo5boG3X+7diq3k9d0bunicDwEEEAAAQQQaIMC2Q2q0wZhqDICCCCAAAIIFKeAxgbsk9LVN5FIuJZ+eq2uLDMraje19tNkIF/fpr+beXWK7Q483E660Ddl/+KsKaWKi8BCG/zTxL+p4/9t1b/GtG/P9/FxOc/UAwEEEEAAgWIW4ImjmM8OZUMAAQQQQACBrATUOrBzxzI7YUi52SGlFaC6Aftk44Pm8wWrzLSFq8wy2xWztm692aDIDAmBZhSYv6LW5Z46/t+YwUwA0ozsZI0AAggggAACIQFaAIYwWEQAAQQQQACB0hboYccKVDfgF6cuCCry0ZzlZqUd/6+LDRD6NGdZrdGPTxVl7UxFhw5ufMGOdoxBTT6i147l9qesg+ng+xj7HXhFIEsBjT25au1620K13nxpu6SH045MABLmYBkBBBBAAAEEmlFg05NwMx6ErBFAAAEEEEAAgZYQUABwWztZSHmHdqZu/caWfWrg9/7MpWb3kb3SFmFdfcKOE2i7Da9tuIny2m5Qt7STjzTcgzUIbBKYv2LjRfWpnf033Na0zAaVx6a0Vt20F0sIIIAAAggggEBhBegCXFhPckMAAQQQQACBVhRQy72eXSoRt7i7AABAAElEQVTMNgOSu1a+PX1x3qVSIHHaolV578+ObVdA3csXrtwYAEwd/2+4ncime1VF28Wh5ggggAACCCDQogIEAFuUm4MhgAACCCCAQHMLuG7Aw5JnA35/5jLbInBD3ofWDK4+kJN3JuzY5gQW2Vmn620AWZPTfPDlsqT6bzNAE4B8NX110ie8QQABBBBAAAEECi9AALDwpuSIAAIIIIAAAq0o4McBDBdhjZ3s49pnPzFr69eHV+e0rIlDmhJEzOlgbBwLgQVfdf993wb/PrFdgMNp+8HJQerwZywjgAACCCCAAAKFFiAAWGhR8kMAAQQQQACBVhWoqigz/bt1Mlv07ZJUDs0GfNljH5kVtXVJ67N947oC2yAgCYFsBDTDtGaaVjfgu8bPSNpFQep9RvVOWscbBBBAAAEEEECgOQUIADanLnkjgAACCCCAQKsI9LBjq52yy1A3GUi4AGqF9duHJ5kFKzbNABz+vLHlhbYr8GLbrZOEQGMCs5eucZu88ulCMyNl9t/jxg0y3W0QkIQAAggggAACCLSUAAHAlpLmOAgggAACCCDQYgI97EQgm/etNhccMtp0ruiQdNzZy2rNRQ9NMl/k2Zrvi4Ur6QqcJMqbVAF1NVf333X1G8y/35mZ9PHg7p3MXpv3ttdlWdJ63iCAAAIIIIAAAs0pwJNHc+qSNwIIIIAAAgi0ikCXjmWmoqy9Gd2/xlx8+NbmD098nNRyb6ntmvmrBz40FR3am5pOZaamsty+2p9Ku2xfq+37rnb9xtdyM7h7lctPlVlXnzDT7azAI/tUt0rdOGjxC8xeWmtsz1/z1OS5dvKY5BajJ+48xHS211kHJgAp/hNJCRFAAAEEEIiRAAHAGJ1MqoIAAggggAACmwQ0ztpc29pvcI8q8/sjtjZX2CDgzCUbu2X6rdbZmYEVoEkN0vjP/asCiv9z8Cg7ruDGoN+CFetM35o6FyD02/CKgATU6m/+8lqzcm29efC9WUkoW9mA9NjB3Rq0Sk3aiDcIIIAAAggggEAzCNAFuBlQyRIBBBBAAAEEWl9AAUCfenbp6FoCKgCTT1Iw589PTzVLV29qzeVneM0nP/aJr4DG/lPrv4ds8G/V2uRZp0/eZYhp166dqbIBZRICCCCAAAIIINCSAgQAW1KbYyGAAAIIIIBAiwmoO295h3bB8TrboMv5h2xpDt66n+v6G3yQ5YK6DV/33KdmvaI7NmkyEM3wSkLAC6j13zzb+m/hyrXmyUlz/Wr3uutmPcyI3htnpu7C+H9JNrxBAAEEEEAAgeYX4OvH5jfmCAgggAACCCDQCgJqaaWZVucvXxscvdyO+Xfq14aZk3YebJasqjPLa7/6WVPvllfYIN+y2nqz8bXOLLLdg9X6z6fJc5ab++ykDifsNMROBJIwCgqGWxr67XhtmwJzlm1s/ffvt2e668MrdLDX4gk7DvFvbQvA5Ilpgg9YQAABBBBAAAEEmkmAAGAzwZItAggggAACCLS+QI+q5ACgL1HHsg6mX1f9VPpVka8KEF5w/4dJE4g8+N5sN8PwDkO6u5ZeBAAj6drcyjo7nuQ8G2zWBDEvf7Iwqf77j+4TXGuanEaBaBICCCCAAAIIINCSAjx9tKQ2x0IAAQQQQACBFhXoamf0LQt1A8714Jod+Gf7b27UgiucbnjhU6MxAJfYbsD1NvBDQmCOnflX3cPvfmumCXcMryxvb47eYVAApAllSAgggAACCCCAQEsLEABsaXGOhwACCCCAAAItJtC+fTszoFunJh1vczvz77d23dR9U5lpcof/fXaqm/F1kQ0Cktq2gILAc+3YfxNnLTPvzVyahHH4dgOMAtE+VVXQ/ddb8IoAAggggAACLSdAALDlrDkSAggggAACCLSCwEAbANysd2c7+2r+B/+6nThk5+E9kjL4bMEq8883Z7iWgEkf8KbNCCQSCTvW3wYzy878q9c7x89Iqns3G/g7dNv+SetoAZjEwRsEEEAAAQQQaCEB+iC0EDSHQQABBBBAAIHWE+hbU2nKbGvAT+evNPlM3KsJRc7cazMzY9Fq19LL10Qzve47qrcZ2aeLqSynZZd3ieurZn6e/VWwr95eSPV2Ihif3vh8kfli4Sr/1r0eM25Q0nXRu7qjm5gmaSPeIIAAAggggAACLSBAC8AWQOYQCCCAAAIIIND6Aj27dDRb9qsxHWwgMJ9UVVFmfnbA5nYCh+T9H/tgDq0A8wEtsX3WrFvvAsgr7CzRtXUbkoJ/6gJ8jx37L5wG2Alm9h3VJ1jVvXO5GWFbopIQQAABBBBAAIHWECAA2BrqHBMBBBBAAAEEWkWga1W52WpATYMgXraFGdqzszlizMCkzV/7bJH5eM7ypHW8iZeAJveYOm+Fm+QjqmbPfDTPzLeTwoTTiTsNCYLN1ZVlZos+1bYbenLwOLw9ywgggAACCCCAQHMK0AW4OXXJGwEEEEAAAQSKTkBjsG0zsKudyKPerLdjuG2wk/husK8K8vhXLeszvdbZbp5q/eXTQVv3NY+8P9uss62+lLTdQ/b9riN6mmo7azApfgJfLFxpVoeugXANV6+rN/dPmBVeZbbo28XsOKy7W6dJP7bsV200IQ0JAQQQQAABBBBoLQECgK0lz3ERQAABBBBAoNUENF5ftmP2aaKHqfNWGo3/plRjg3x723H/np48Lyj/sx/NN9Ps+IDb2sAiKV4Cc5fV2i7e6Wd6fuT9OUbdgsPp5J2HutZ+FWXtzZb9q01ZBzrdhH1YRgABBBBAAIGWFyAA2PLmHBEBBBBAAAEESkhA3TY3t5N8TLbdfH2g59Bt+ptnbADQTwGxpm69udeOAbeN7V5MN88SOrmNFHVFbZ2ZvmjjxB5afuXThWbp6jo7BuB697O2foOZMGNpUi47Du1uRtkWfxorcqv+NaZjGZPDJAHxBgEEEEAAAQRaRYAAYKuwc1AEEEAAAQQQKCUBdd9UN85Js5e7rqD97AQPOw3vYcZ/sTioxmMfzjE/2W+k0YzDpNIXqLNdvNXy0/YCN/OW15pLH5tsFq5M3xJQNdYQfxr7T2lIzyrTyXb/JSGAAAIIIIAAAsUgQACwGM4CZUAAAQQQQACBohdQN05151QQcK2dBfbw7fonBQDVRfjON2eYw+x6UukLaNzHdbaF3/wsg3+qsWb9Hdi9kx0Lssz0qSYQXPpXATVAAAEEEEAgPgIEAONzLqkJAggggAACCDSzgLpzju5XY4OAy8xIO6vrqL7VZoqdHdYnTQaxi20ZSDdgL1Larwr+XZJFyz/VsleXCnPcuEGuFeBmvTuXdsUpPQIIIIAAAgjEToAAYOxOKRVCAAEEEEAAgeYUULdOjfE22bYE/MaY/mbKU5sCgDMXrzYffLnMjBncrTmLQN4tILBgRXTwb4Dt/j3aju2nSWQ6lrc3lTYoXNOp3Iwd0s1NENPffl5VwSN2C5wiDoEAAggggAACOQjwdJIDFpsigAACCCCAAAISqLYzAQ/qUWXq7QBxCgjNtjPF+vToB7PNNswG7DlK8nXhyrWRY/4Nsef814eNdoG+qIpp1t9B3auiPmIdAggggAACCCDQqgIEAFuVn4MjgAACCCCAQKkKKPC3cMVac6gd8+//Xv4iqMZE2zLwlFvfDN6zEA+BxoJ/quVQO/FHBzthDAkBBBBAAAEEECg2gfbFViDKgwACCCCAAAIIlIKAxvkb1quz2XNkb9cFtBTKTBnzE8gm+NetqtyOA9gxvwOwFwIIIIAAAggg0MwCBACbGZjsEUAAAQQQQCC+Al3t2G+a9fXgrfrGt5JtvGaDG+n2Kx41+htug8EkBBBAAAEEEECgWAXoAlysZ4ZyIYAAAggggEBJCKjb5zfHDjRT5q4wH8xaVhJlppDZCWzRt4v5xYGjgjH/bKNPU96hvZvpVx191QpUr72qO7pJQbLLla0QQAABBBBAAIGWFyAA2PLmHBEBBBBAAAEEYiSggNDIPl3MBYeONktWrzO169bHqHZttyqa0KNH5woX5JNClZ39Wee5c0cen9vuVUHNEUAAAQQQKF0BnmBK99xRcgQQQAABBBAoEoE+tgXYAjshiEtMAlskZ6VwxehnJ3wZarsCt2eCj8KhkhMCCCCAAAIItKgAYwC2KDcHQwABBBBAAIE4CvgJQdRFlBQfgYqydmbLftVufD+Cf/E5r9QEAQQQQACBtihAC8C2eNapMwIIIIAAAggUXKCL7Ro6sFsns3R1XcHzJsOWF+hY3t4M69nZqCswCQEEEEAAAQQQKHUBAoClfgYpPwIIIIAAAggUjYBmjB3co2iKQ0EQQAABBBBAAAEEEHACfKXJhYAAAggggAACCCCAAAIIIIAAAggggECMBQgAxvjkUjUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAHINIIAAAggggAACCCCAAAIIIIAAAgggEGMBAoAxPrlUDQEEEEAAAQQQQAABBBBAAAEEEEAAAQKAXAMIIIAAAggggAACCCCAAAIIIIAAAgjEWIAAYIxPLlVDAAEEEEAAAQQQQAABBBBAAAEEEECAACDXAAIIIIAAAggggAACCCCAAAIIIIAAAjEWIAAY45NL1RBAAAEEEEAAAQQQQAABBBBAAAEEECAAyDWAAAIIIIAAAggggAACCCCAAAIIIIBAjAUIAMb45FI1BBBAAAEEEEAAAQQQQAABBBBAAAEECAByDSCAAAIIIIAAAggggAACCCCAAAIIIBBjAQKAMT65VA0BBBBAAAEEEEAAAQQQQAABBBBAAAECgFwDCCCAAAIIIIAAAggggAACCCCAAAIIxFiAAGCMTy5VQwABBBBAAAEEEEAAAQQQQAABBBBAgAAg1wACCCCAAAIIIIAAAggggAACCCCAAAIxFiAAGOOTS9UQQAABBBBAAAEEEEAAAQQQQAABBBAgAMg1gAACCCCAAAIIIIAAAggggAACCCCAQIwFCADG+ORSNQQQQAABBBBAAAEEEEAAAQQQQAABBAgAcg0ggAACCCCAAAIIIIAAAggggAACCCAQYwECgDE+uVQNAQQQQAABBBBAAAEEEEAAAQQQQAABAoBcAwgggAACCCCAAAIIIIAAAggggAACCMRYgABgjE8uVUMAAQQQQAABBBBAAAEEEEAAAQQQQIAAINcAAggggAACCCCAAAIIIIAAAggggAACMRYgABjjk0vVEEAAAQQQQAABBBBAAAEEEEAAAQQQIADINYAAAggggAACCCCAAAIIIIAAAggggECMBQgAxvjkUjUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAHINIIAAAggggAACCCCAAAIIIIAAAgggEGMBAoAxPrlUDQEEEEAAAQQQQAABBBBAAAEEEEAAAQKAXAMIIIAAAggggAACCCCAAAIIIIAAAgjEWIAAYIxPLlVDAAEEEEAAAQQQQAABBBBAAAEEEECgDAIEWkOgvr4+OOycOXOCZRYQQAABBBBAAAEEEEAAAQQQQKBwAuG/ucN/ixfuCORUCgIEAEvhLMWwjAsWLAhqtfPOOwfLLCCAAAIIIIAAAggggAACCCCAQPMI6G/xYcOGNU/m5FrUAnQBLurTQ+EQQAABBBBAAAEEEEAAAQQQQAABBBBomkC7hE1Ny4K9EchdoLa21nz44Ydux969e5uyMhqj5q6Yfg818fYtK8ePH2/69++ffmM+aXMCXB9t7pTnVGGuj5y42tzGXB9t7pTnVGGuj5y42tzGXB9t7pTnVGGuj5y4ct5Y3X59L7xtt93WVFZW5pwHO5S+AFGX0j+HJVkD3XB22mmnkix7qRVawb9BgwaVWrEpbwsJcH20EHSJHobro0RPXAsVm+ujhaBL9DBcHyV64lqo2FwfLQRdoofh+mieE0e33+ZxLaVc6QJcSmeLsiKAAAIIIIAAAggggAACCCCAAAIIIJCjAAHAHMHYHAEEEEAAAQQQQAABBBBAAAEEEEAAgVISIABYSmeLsiKAAAIIIIAAAggggAACCCCAAAIIIJCjAAHAHMHYHAEEEEAAAQQQQAABBBBAAAEEEEAAgVISIABYSmeLsiKAAAIIIIAAAggggAACCCCAAAIIIJCjAAHAHMHYHAEEEEAAAQQQQAABBBBAAAEEEEAAgVISIABYSmeLsiKAAAIIIIAAAggggAACCCCAAAIIIJCjQLuETTnuw+YIIIAAAggggAACCCCAAAIIIIAAAgggUCICtAAskRNFMRFAAAEEEEAAAQQQQAABBBBAAAEEEMhHgABgPmrsgwACCCCAAAIIIIAAAggggAACCCCAQIkIEAAskRNFMRFAAAEEEEAAAQQQQAABBBBAAAEEEMhHgABgPmrsgwACCCCAAAIIIIAAAggggAACCCCAQIkIEAAskRNFMRFAAAEEEEAAAQQQQAABBBBAAAEEEMhHgABgPmrsgwACCCCAAAIIIIAAAggggAACCCCAQIkIEAAskRNFMRFAAAEEEEAAAQQQQAABBBBAAAEEEMhHgABgPmrsgwACCCCAAAIIIIAAAggggAACCCCAQIkIEAAskRNFMRFAAAEEEEAAAQQQQAABBBBAAAEEEMhHgABgPmrsgwACCCCAAAIIIIAAAggggAACCCCAQIkIEAAskRNFMUtX4Le//a1p165dTj/aJzVNmzYt6zxOPfXU1N0j3991113moIMOMv369TOVlZVm6NCh5pRTTjGvv/565PZRK1evXm2uvPJKs9NOO5kePXqYzp07my233NL84he/MNOnT4/aJXKdttU+2ld5KC/ledVVVxkdI85pn332yfrcZuMwceJEc+aZZ5oRI0aYTp06md69e5s999zT3Hjjjaa+vj6bLNw2//3vf81RRx1lBg0aZDp27Ohe9V7rs006no6r46scKo/KpfJNmjQp22zMwoULzUUXXWS22247U1NT4360rHWLFi3KOp9S3FD/96+77jpzzDHHmM0339xUVVW5/686L9/85jfN3Xff3eh55f5Rime++Mrclu/TxXQ23n77bfP73//e/f729+cuXbqYLbbYwnzve98zr7zySqPFvf3227P+vaNtG0vF9izw2muvuecZPdfo+UbPOQcffLDRc0/cU7bPnHr2aCzF8TmgUM9IjdkV4+e5PG/66+iFF15Iqgr3jiQO3iBQegIJEgIINKvAxRdfnLB3hpx+7rzzzgZl+uKLL7LO47vf/W6D/cMr7IN64tBDD02bX/v27RM2CBneJXL5k08+SdiARNp8bKAm8cgjj0TuG1758MMPJ7RtOif7R01Cx4pr2nvvvdPWPdWkMYObb745UVFRkTa/nXfeObFgwYKM2axfvz5x2mmnpc1DZfrBD36Q0HaZko5jg7hp87FBxcQtt9ySKQv32RtvvJGwf7ylzad///6JN998s9F8SnGD3/zmNwn7EJ627v76kLMNzqStIveP+N4/0p70An/Q1u/TBebMOzv7ZUqj9wPdF77zne8k1q5dm/Y4t912W1b5KC9tmykV27OAnrv0HOPvj6mvhx12WGLNmjWZqlTSn6XWN917PXukS3F9DijEM1I6s1JYn8vzpq4b/T/68ssvk6rGvSO+946kE82b2AqY2NaMiiFQJALz5s1LfPjhhxl/3nvvvSAApkCYAnSpKfwH/KWXXpoxv9Rf1ql5nXjiicGD8b777pt48MEHE+PHj0/ceuutCds6K/jspptuSt01eL98+fKEAnP+wfL0009PPPvsswn7rXvisssuS9jWCO4z21opMWHChGC/1IV33303YVuFuW21j/ZVHspLefr8dSwdM47JP5DtuOOOGc+rrqNM6bHHHgv+6Onbt2/i2muvdYEx+w1+4uijjw4s99hjj4RtmZc2q/PPPz/YduzYsQnbYsJdH3rVe39OLrjggrR5KH8dx2+r46scCtSpXH369HGf6eHy8ccfT5vPjBkzErbloNu2rKwscd555yVeeukl96NlrdMxlN/MmTPT5lOqH/hArG0Vm7CtcxO32T/EbeuehG0BlPjHP/6RFGBVMH7FihWRVeX+Ed/7R+QJL/BK7tMFBm1Cdv539IABAxLnnHNO4r777nP3Z9tyP3H11VcnBg4cGNx3TzrppLRH0r3E35+ffPLJjL97lixZkjafYnsWsC3Og3rJSs81er7Rc46ed3ydM9mkrWyJfODreNZZZ2U8r59//nnaGsXxOaBQz0hp0UrgA53zxv4mueeee4L/JwceeGCDWnHvSH9fbYDFCgSKUIAAYBGeFIrU9gQUAPEPbPqDPyqF/4DXL998kwJr/liHH354g0CQWm0NGTLEbdOtW7fE4sWLIw914YUXBvnYLsANtnn11VeD4Eymb5l9awYFchT4S03K25dX3+rHMfkAYCanxuq9bt26xGabbeasFET+9NNPG+zyox/9KLBMdw1NmTIlOG8KSKYGo1etWpXQep0TnbN0LTP1R5c/bzpuatJ+vtXnyJEjE3V1dambuPff/va3g3zuvffeBtuEH1Qba/naYOcSWKEg5xVXXJE2+K1A6/HHHx8Y/e53v4usFfcPk4jr/SPyhBd4JffpAoM2ITu1XtN9L92XOPodHv5y7sUXX4w8WviPeN0f8k3F9Cxgh4NIdO3a1d0P9RyT2tpdZnru8b+bnn/++XyrXdT7+frle8+L43NAoZ6RivrEF6hweu7w15C+aExN3DtMIq73jtRzzft4ChAAjOd5pVYlJhBukZfuYb1Qf8Afcsgh7he7gjfpWkyppZf/5R8V3NODlH/IHj16dNquoHactyAffQOfmtQazB9H20YldUPRMbSdApI6dtxSIQKA4UDY5ZdfHkmk4F337t2d5VZbbRW5jVoM+HOiFiVRSev9NlHBPe3jz5kdyzGh40YlldPnExXcmzNnTtCi0Y7dFJWFW6fPlI9aE2qftpbs+IhBt+9tt902svrcP+J7/4g84QVcyX26gJgtlJWG3vD31rPPPjvyqIX4I77YngX0ZYmvt55jopKeezp06OC201AocUzeIN8AYByfAwr1jBTH6yVcJz1z+1bE6pUT9fzGvcO4YZTCbiwjUEoCTAJif0uSEGhNAdt9xjz00EOuCMOHD3eTJTRXeWz3QGNbALrsDzjgADepQ9SxbHdNN8mCPnvggQcabGK/+TLLli1z622rK2MDLw220YrwZCRR+dguOcF+Grg8KilvO5aR+2jp0qVGxyY1FAhbht3DW2ryCNtazK2aPHmymTp1avhjfSEUXIuajGXXXXdN+ty/0fpRo0a5t7p2tV84Kd+PPvrIrdLxdNyoFC5n1PVhxxwzGzZscLumuz70oc9H22qftpZ69uzpJkdRvT/77LNmqz73j2ajLeqMw/eWdP8PuU8X1ym0XV2DAjXnPaHYngX8tWpblxs9x0QlTZqi5x8lPQ/pvkbaJBDX5wB/baim/plhU603LjX2jJS6fRzf6//ErFmzXNWOPfbYtM9vTa07946mCrI/AvkLRP/Vnn9+7IkAAjkK/Pvf/zZ2MGq3l+3u6GblyzGLrDd/6623jP3G3m1vW52l3c9OIhEEf7SP7Z6ZtG14hsFM+diuosHDg+0SnJSH3vh8NOvvuHHjGnzuV4SPEZWP364tv3pLBeY022G6lMnSthIzs2fPdruGt4vKy3+uB0XNMBtOvixa57cLf+6XVU7bVc29jTqv2eYTPkZUPv54cX61g/276tmWLc1WTe4fzUZb1Bn7/4fcp4v6NCUVzt8PtLI57wn+2tBxwvdhvQ+nlngW0LON7WngDrvbbrsZPcekS76sctKMyqRNAnF9DvDXalOekTYpxXfpjjvuCCrnv3wPVhRwwZ8PZen/P0Zlz70jSoV1CDRNgABg0/zYG4EmC+Tzy/a6664zdtw0U1lZaWxXXLP11lubH/7wh8YO1J6xPGr15ZNaeGVK/nM7Zo6x47UlbZptPrabsSundvYtwsIZ+XWqi7ZNl3xZ9LnfJ922pbz+448/NrvssouxXZ3duVVLhSOPPNLoGkkNwobruXLlSmO7NblVYavwNn45/HmqZbbnVXkVOh+V33Y18cV0r748usYzBTXtLMBBi9XUOiVlGNM38+fPD/5f2K7XjdaS+0ejRGwQEvD/p7hPh1CKfNEOJRKUMJt7glp22klFXNCsV69e7gtAOwN50BIoyCxlwd+jtTr8OyFlM/f7XdePkr+ewtv4dU25xtTy3HZfdNlmKos2CH/ujx0uT1yW9QWzHe7DfRFbXV1t7ERRRr02MvWkyPacNuaYTz7N9RxQqGekuFwX6eohJ98bY+jQoWafffZJt2mwnntHQMECAiUjQACwZE4VBY2jgFpOvfzyy65qu+++u7Ez1mVVTQX61K1H316rC7EetOyMva4VnQKB4W//wxna2YGDtwouZUqDBw8OPvbBJb/C56MWIQpWZUo+HzsYd1K5amtrjR27zO3aWFnsuHVGx1JKLYtbGZN/7IzRrgWDulfrHKp1nbq06oF9++23j/zDSVX350PLjVn686FtUy1bMx91OwofX+Xz7xurk7b19Uqtkz6Le7rqqquMAvVKvot3pjpz/8ikw2dhAe7TYY3SWNZQCH/4wx+CwmZzT3jhhReMHT/VfdFkJ9IwdtxHc9lll7kv8PRskS75e3QxPAv4sqisjf3O8L8vtG2cf2fo2VABTvUyUXDHTg7mvlDcb7/9zFFHHRUM5SIHnwrlmE8+zfUckE9Z5BHna8Of7/Drf/7zn+CL2FNOOSWrHkncO8KCLCNQGgLpm9yURvkpJQIlLWBn1wrGT1OQp7GkYJse2vStnL7JVQtAPbQ/9dRTxs666h7w9LCuMW3+9a9/NcguPNaNHdy3wefhFT7gpnV6cAwnn09jeWif1Hw6duzosvJ56E22+aiFWGpZXGYl/o/Gz9p///2NHZDcjBkzxmhMN/koUKPzqQd4PchrXCd1cbKzGybVOBfL1PMRzqhY88n2+lBd4nh9hM9R6rL+UL/mmmvcav3RawdvT90keM/9I573j+AEN8NCLvcEHV73l7jep5uBt1my/POf/xx0hdU4eJmG17Azx7ux8tRl1gfFPv/8c6NAwH333WcUANaXiu3atTNnnHFGg/L66yOXe7Qy0X260M8CvizKv7HyZPo9qP1LPWksuyOOOMI9V6i1ozz0Jaxaht54441GQV6NiaceBk8//bQpLy8Pqlwox0Ln09g5VQX8eU19DihUWQKkmC7k0iOJe0fbe96M6WXfJqtFALBNnnYqXSwC//znP11RFMhr7Ft6dc9Ri7DUCRXGjh3rAkc//vGP3cDWM2bMMHfeeac54YQT3ANguK56mPcp0/g42sY/nGvZj1GoZSWfT2N5aNt0+fg8tE0u+aSWRfuXerr//vsjW1Luueeexs6ya04//XTz97//3aiF4M9+9jOj7cMpF8t050P5FWs+bf36CJ/r8LKuBw3SrdZ/+gNd10jq/cFvz/1j4xcPcbx/+HPcHK+53BN0fH9/wbk5zkbjeSrAc/7557sN+/TpY/7617+m3UlfJuqLR907wmmnnXZyzw+PPvqoCw5q+Imf//zn7nkidSgGf33kco/WscLXh89D63PJJ5yH9s0lH3+dar/UfLSu1JOeFaN6Zxx44IHGzgptDjnkEDNhwgQXENQ18tOf/jSocqEcC50P10ZwipplQa0k1ZpPSRO9+fGZ3YqUf7h3bASJ470j5VTzNqYCdAGO6YmlWrkL6CG4qT+333571gd+4403gllY9S2sxjnLlPTwk+6Pe+2nFoE+oKj3GucrNSnQ6JOfDMS/T30NdyPu1KlT0sc+n8by0E7p8vF5aJtc8kkti/ZvidTUa0P7p7s+oh7UfZ30zfz//d//BbPuanwWPdyHUy6W6c6H8ivWfNr69RE+135ZLRoOO+ywoJu0uvypW1e6xP1j4yQprXX/SHdein19LvcE1cXfX3Bu+TM7adIk10NAXwjovGn8NwUB0yU9c+j3Urr0jW98w1x00UXu49WrV7teBqnb+usjl3u08ghfHz4Prc8ln3Ae2jeXfPx1qv1S89G6Uk+Znin69u3rWnf6Vn+pz4qFcix0PlwbzXtV6u8HDR+g1FiPJO4dG89FHO8dG2vGv3EXIAAY9zNM/YpWIJem9tlWQi3GNOCzkmbY8r/M/f4aBNqn1C4Sfr1/DU/IkNr1wufTWB7KK10+Pg9tk0s+qWXR/nFPmiDltNNOC6oZHuBdK3OxTHc+ijkfro/g1LsFtazQlwbvvPOOe3/uueea8847L3mjPN5x/8gDLea75HJvEYW/v7TF+3RrXgqaufWggw4yS5YscbP+3n333WavvfZqcpHU7dcHCVN/7yhzf33kco/WfuHrw+eh9bnkE85D++aSj79OtV9qPloX96Tum2oNqKRxAWfPnh1UuVCOhc6HayM4Rc2yoCGJlNQ6Vj2Impq4dzRVkP0RaD4BugA3ny05l5hAIWaC02yk2SR9k3nPPfe4TfVt7MEHH5zNblltowCgxotTkEDjvPTu3TvYLzwwtpr777jjjsFnqQvhwY/92EB+G+Wjscf0EL106dLIriZ+W5+PyhHudqNvhzXWncoYHqDZ7xd+1R81/oE9tSzh7ZpzuSWvj6h6+MCuPkttAThw4MBgl8Ys/fnQDqmWqddHkGnEQi75aFbJdMnnoz8yw8fX9nqvbq6N1Unb+nxS66TPWiK11PWh1j0aLsDP4viDH/zAaBKQQiXuH4WSjEc+pXafjod6brVQ8OaAAw5wQRzdR//2t7+5LwhyyyV6a7Ug1O9pTdiV+ntHexTTs0D490djvzP87wvVobV+Z+jYrZl0r3/88cddEXRuNUyEUqEcU/NpzeeAQj0jOaAY/vP222+7vxtUNbX81cR7TU3cO5oqyP4INJ8AAcDmsyXnEhPQQMktlTS2zuLFi93hvvWtb7lv7At1bP9tfVR+4SDSxx9/HLVJsM5/rtZn6l4cTspHg4QraTuNFxKVFKzQbMVKo0ePbrCJ8tEsyPoGWtvqWFHJl0WfReUTtU+h17Xk9RFV9kznVd+0648Y/VETtorKJ/x5qmU+14eO0Vg+msE4XfLlUfn9AN5+W5VHrdw0K/LcuXNN6vhTfrs5diIczYatlFoWv01zv7bE9aEWvd/+9rfNI4884qqjb+kzzdCZT50zXWf5XB/cP/I5C8W1Tyndp4tLrvlLo8CcWnJp4g4ldef8zne+U9ADN3ZPKJZnAY1Z1qFDB7N+/fom/R4sKF4RZ5buvOZzn1c1U3/3pubTms8BhXpGKuLT2aSihXskNdb9N5cDpbvGlIeuD+4duWiyLQKFE6ALcOEsyQmBrAXCv2wL/bCu1n9Kam2nb+7DSYN7+4GUo7rz+G3VQlFjFCppHz9WjP98jz328ItuEOngTcqCvlX0Lfd23333lE+N8floG9+dscFGdkW4rFH5RO0Tt3X+vKpe/pv6cB295ZQpU1ywLPxZeDmT5fDhw4O8w9uF9/fLL730klvUN+vDhg3zq92rL4veZMpHQb2pU6e6faLOa7b5hI8RlY87QAz+OfPMM4269ikdfvjhbsxPzSBdyOSvM+4fhVQt7bz8/0Pu08V1HvXFiHoP+P+zGgdUk4EVMmnmWAUZlTL93tHn4fuw3odTSzwL6Nlm5513dod9/fXXM44n6Muq+1ymnhDhOsRt2V83qlf43Mb1OcDfx5ryjBS3a0D10SQ//rlCPXU0QUwhEveOQiiSBwLNJJAgIYBAiwrYh+mEDagl7H/pxJgxYwp6bDvun8tXee+///6Redtf7m4b2zInYVuMRW5z1113BflceeWVDbaxA2gn7CDAbhv7rW/CtkxqsI1W2IBFkM/48eMbbGO7EQefa9uoZL/NT+gYqpMd2Dphg5NRm8V6nX1ACwzkYGd6blBf26U8sLz88ssbfK4V9g/4hO3a4baz375GbnPWWWcF+dg/oiK30XqVQz92luLIbfw569Gjhztu1EYqp8/n3nvvbbCJbdmXsAEut439Q7fB536FPlM+2lb7xDHZWTgDK/3ftl38C15N7h8FJ41Fhtyni+806l5uv+wI7gm//vWvm6WQl156aXCMSy65pMExiu1Z4IorrgjKq+eYqKTnHttS0G136KGHRm0S+3W2xWjCBkydwYgRIxrUN47PAYV6RmqAVeIrHnrooeD/zDnnnFOw2nDvKBglGSFQcAFT8BzJEAEEMgrYLjrBL9s//elPGbcNf2hnf00baNN2n3zySWLIkCFB3rZpfXj3YPnZZ58NtjniiCMStutt8JkW7Ld2QT4KuNmuykmf+zcXXnhhkE9UkPC1115LKMiowMzee+/td2vwaicecNtoW+2TmpS3DxJdfPHFqR+X/PvnnnsuYcc4TFsPBTy/+93vBga25VfkttrODuzttqupqUnYbtUNtlOwzlvedtttDT7XCvvtePDHkW0ZkbCzPyZtp/dar3x0zmwLvqTP/Ztbb701OJZtleJXB68qn8qpfEaOHJlQkDMq2S6vQT52VssGmyhw6OskpzgmXfe+jl/72tcSdjD0nKvJ/cMk4nj/yPlCyHOHtn6fzpOtWXZT0M1O+BHcE/L5o91OGpJ49913M5bPDjUQBInsbJcJO65e5PbF9CxgxxQOvpwcOnRoQl+4hpOed/Q71N9P7Viq4Y9jsfzwww+n/X2qCtqW94mxY8cGBlHPoXF8DijUM1IsLpJQJY455pjgWrA9cUKfRC9y79j45Xcc7x3RZ5y1cRQgABjHs0qdilrAdql1v2wVPNGDWLbJB0rsbJ+J++67L2G76CYmTJiQsIM4J/QHgJ3JLvglbicJyJjtiSeeGGy77777JvQN4FtvvZWwg4cn9G2wfzi244ulzceOuZawY+4E29oZvxIKZql12P/7f/8vKI/+cFA50yX9EaJtdEzVQfsqD+WlPH1ZdCwdM25JQSvV++STT07cfPPNCds1yXnZsRET11xzTUIt9byBHVQ5oW/u06XHHnssaDFnJ5dJKNis1jtPPPFEIvyQZ7vCNAj8hvM8//zzg2PqDwXbPcRdH3oN/+FwwQUXhHdLWtYfWuEWKjq+yqHyqFyqi+qlVnu6htMltXa03VLctvo/88tf/jIhG/1oWeuUj7ZJ16I1Xd6lsP7aa68NzoXtbp1QK70PP/ww409UK1kZKdDK/aMUznrxlbGt36eL6YwcffTRwT1hv/32S3zwwQcZ7wcK5qQm/fGqe8Juu+3mfufqd4eeAfSjllLHHXdcwo7fFRzn+uuvT80ieF9szwI33nhjUG49z+i5RvXSc46ed1Rv/Zx00klBHeK0oMCn7dKbOPvssxN33nmn+2JVz2BPP/10Qi1F7WQcgYGeBdK1Jo/jc0ChnpHicr3oC37bDd5dD9tss01W1eLeEd97R1YXABvFQoAAYCxOI5UoFQE7U2jw4JVr1xP/0NrYq7pupHug805qxaXjp8tLQZlsWsuo1aGdICRtPmrhpVYEjSV9Y+1bg0WVScE/HSuOKdy6L6ruft22226bmDRpUqMECiL6rj1+3/CrHSPJtfLMlJG6XX//+99Pe16V32mnnZbQdpmSWpP6gHe4DH5ZD5633HJLpizcZwp22wlA0pZHn2mbOCa1nvVe2b7qG/rUlO2+3D9S5XjvBdryfdobFMNrtv+X/XYKCKUm/0e83ybda1VVVSLTF4E+32J7FrjooouSApip9dPzz5o1a3zxY/Wq851a36j3+lIuU++DuD4HFOIZKS4XzF//+tfgWonqyRNVT+4d8b13RJ1v1sVTgABgPM8rtSpSgV/96lfBL1u1psol6Y8vtbjSN/76Vltj8Kn1k8ZYU5dMjRGmlkG5pH/9618JO4Oga42loJGdidW1RIvqipsuX3VH1Lg7KoO6DOsPhlGjRrnyTJs2Ld1uDdZrW9VBwT7lobyUp/LWeEdxTXYg7sSf//znhFpt6htYtdzTGJFqFajzbGd6Tajra2pX7Uweug5OP/101yW4srIyYSeDSeibfj3spetqG5Wfvi0/8sgjXWsCXR9qVaD3mVrspeaj491www3u+CqHyqOuyirfxIkTUzdP+17BxN/85jfOSDb6UVBU61K7eaXNpAQ/KFQAkPtHCZ78IixyW71PF9OpiArmZFoXFQBUq71//vOfCQ3PsMsuu7hhP/R7V/d5/Q7Sc8Zll12WmDdvXtZVL7ZngVdffdU9z+i5RvVSq3M976hVXJzTCy+8kPjd736X+PrXv+6ep/SMqGdFPVPpd6bGW87lGS+OzwGFekYq9etIQ4ro3qExMWfNmpVVdbh3ZMXERggUtUA7lc7+5ychgAACCCCAAAIIIIAAAggggAACCCCAQAwF2sewTlQJAQQQQAABBBBAAAEEEEAAAQQQQAABBL4SIADIpYAAAggggAACCCCAAAIIIIAAAggggECMBQgAxvjkUjUEEEAAAQQQQAABBBBAAAEEEEAAAQQIAHINIIAAAggggAACCCCAAAIIIIAAAgggEGMBAoAxPrlUDQEEEEAAAQQQQAABBBBAAAEEEEAAAQKAXAMIIIAAAggggAACCCCAAAIIIIAAAgjEWIAAYIxPLlVDAAEEEEAAAQQQQAABBBBAAAEEEECAACDXAAIIIIAAAggggAACCCCAAAIIIIAAAjEWIAAY45NL1RBAAAEEEEAAAQQQQAABBBBAAAEEECAAyDWAAAIIIIAAAggggAACCCCAAAIIIIBAjAUIAMb45FI1BBBAAAEEEEAAAQQQQAABBBBAAAEECAByDSCAAAIIIIAAAggggAACCCCAAAIIIBBjAQKAMT65VA0BBBBAAAEEEEAAAQQQQAABBBBAAAECgFwDCCCAAAIIIIAAAggggAACCCCAAAIIxFiAAGCMTy5VQwABBBBAAAEEEEAAAQQQQAABBBBAgAAg1wACCCCAAAIIIIAAAggggAACCCCAAAIxFiAAGOOTS9UQQAABBBBAIH+BYcOGmXbt2plTTz01/0yKZM9FixaZHj16uPq89dZbBSvV7bff7vKU07Rp0wqWb5wzSiQSZtttt3Vut912W5yrSt0QQAABBBBAoIgECAAW0cmgKAgggAACCCCAQHMIXHTRRWbJkiXm0EMPNTvttFNzHII8sxRQsPTXv/6121qvq1atynJPNkMAAQQQQAABBPIXIACYvx17IoAAAggggAACRS8wffp0c8stt7hyKhBIan2B448/3owaNcrMmTPHXH/99a1fIEqAAAIIIIAAArEXIAAY+1NMBRFAAAEEEEAgHwF1aVV3TXVzLeV0xRVXmLq6OrP77rubXXbZpZSrEpuyt2/f3vz85z939fnjH/9oamtrY1M3KoIAAggggAACxSlAALA4zwulQgABBBBAAAEEmiywdOlSc8cdd7h8TjnllCbnRwaFEzjuuONMeXm5WbBggbn77rsLlzE5IYAAAggggAACEQIEACNQWIUAAggggAACCMRBQIEljTGnQJMCTqTiEdCkLF//+tddgW699dbiKRglQQABBBBAAIFYChAAjOVppVIIIIAAAggg4AVmz55tzj//fLPDDjuYrl27umBY37593UysJ510kuviu3z5cr958JpuFmB1DdZEDtn+7LPPPkGeqQvPP/+8+e53v2s222wzU1VVZWpqaly5/ud//seo3E1N9957r8tCZejZs2fG7J577jkjj+HDh5tOnTq58gwdOtTsuuuu5txzzzX6vLG0YcMGc/PNN5uvfe1rpnv37qZz585mu+22M5dddplZvXp12t21n/LXcdRVuVevXu48devWzWy//fZu/YwZM9Lurw9UR50TvSp98skn5ic/+YnZfPPNXV30WdRMxZ9++qnrjquZeXV9qO46H5r9+e2333Z5pftHXXevvfZad8zevXu7Miuwp/H9DjnkEHP11VdHHtPnd8wxx7jFV1991cycOdOv5hUBBBBAAAEEECi8gB3bhoQAAggggAACCMRS4KWXXkrYoFrCPkFl/HnkkUca1N8Gv9w+NkCX9NkXX3yRMa/UY+29995J++vNmjVrEieeeGLGfGzwLPHwww832DfbFTY4lejYsaM7xoUXXphxt5/97GcZy6I62QBigzxuu+22YL9JkyYl9t9//+B9qsPOO++cWLlyZYM8tOLiiy9Ou5/PxwZIE/fff3/k/lopZ22r1wcffDAhP7+vf9W5C6errroqYVtHNtjOb2+Dhol0djZAm9hqq63S7uvz+MUvfhE+ZNLyxx9/HOxvA6dJn/EGAQQQQAABBBAopECZfTghIYAAAggggAACsRNYu3atsUE2o9Z91dXV5qyzzjL77ruv6dOnj1m3bp2xwSDz2muvmQceeCCnug8cONB8+OGHGfdRy7tLLrnEbaNWdOFkH+TMscceax577DG3+vDDDzeaFVatzjQ5xPjx482f/vQnoxZv2k6tw3bcccdwFlktv/XWW0YGSjvttFPafR599FFzzTXXuM/VWk9Oo0ePdq3hNIagDeyZZ555xpUrbSb2g9NPP9288cYbrkWj6tOvXz9XhyuvvNK8/vrrbv9LL73UXH755Q2yqa+vN/379zdHHXWU2W233ZxFZWWlaxWnc3TDDTcYGzw0J598snn33Xdd+Rpk8tUKuWm8Q7WotME7s+eee5oOHToYeXTp0iXYzQb/zHnnnefe+3qrtaBaHU6ZMsX85S9/ceXWeVSLxJ/+9KfBvlo4++yzzeTJk906He/oo482AwYMcMfS7L5qPfjQQw8l7ZP6ZosttnDHk/OLL77oDFO34T0CCCCAAAIIIFAQgUJGE8kLAQQQQAABBBAoFoFnn302aF0V1cLPl9POkJtYtmyZfxu8pmsBGGyQZsEGmhK2G6k7tg2kNchbLb3sQ5xrefbf//43MpfFixcntt56a7ed7RIbuU1jK+3sv0H9bffStJt/+9vfdtupvitWrEi73aJFixp8Fm4BqDr94x//aLCNWiJus8027hhqRSjv1KSWeTYom7o6eK/y28Cry8MG24L14QXfAlDlsIG4xPTp08MfJy2rtaJv+afWh7YLctLnerN+/fqEjqX8bOAwoXPik1pw+v0ztfDT9lFuPh+92qC0O8aWW24ZXs0yAggggAACCCBQUIH29qGGhAACCCCAAAIIxE5g7ty5QZ322muvYDl1oayszI29l7o+n/cat+/II480NkBkNBacDTwm5W2f4owNzLms1aLMTwKReiyNn6cWakpqAajx7HJNX375ZbCLWj2mS95JYySGW8ilbq/6ZEpqAaeWcKnJdkN2Y/FpvQ2GBa3mwttpvEVNVJIuDRo0yGhcRCXbLdrIMVP6wx/+YIYMGZJ2E7WwtIFI17LSBgDd2IGpG6s15nXXXWdUfrU+vO+++4JNbDDQ7a8Vma4tfd6Ymz83apHaWL2UHwkBBBBAAAEEEMhHgABgPmrsgwACCCCAAAJFL6AupT7Zlmp+sdleFfT75je/6SbvUFBRAaMRI0YkHU9dRj/77DO3Tt17M6VwYEldaHNNCxYscLuoK2xFRUXa3b2THS8xKFvajTN88K1vfSvtp+PGjQs++/zzz4PldAvqtq2AmLofT5w40f2oHkr+s3T7qq6NzXiswKySJuHQ5CDpkroDa3IQpfA50IQq3tS2ejTqwpxv8gFCdddWV2ASAggggAACCCDQHAIEAJtDlTwRQAABBBBAoNUF9thjDzeWnApiJ7kwdhIKN/6cWtRpDMBCp+9///tunDnlq5lhNd5gagrPKqux7hR8SvcTbo3nW+ml5pfpvVqpKak1Yab0ne98x32s1nm2q64bN1EBU82Om0uyXVjTbu6DXNrAdjOO3M522XXj6qk1oGbj1ZiIKo8CcPo544wzgv0WLlwYLKcuaBw/jR+YLuk4Pjh6wQUXpPX358Wfs/A5UKvAE044wR1Cgd6RI0e68QQff/zxnIN44fOzatWqdMVmPQIIIIAAAggg0CQBAoBN4mNnBBBAAAEEEChWAXUpVUsvTWihpEkgfvWrXxkFBtWyS91v77zzTmPHemtyFTRRxN133+3y+dGPfuQm0ojKdP78+VGrG123evXqRrdJ3cAHwdQyMVOyM/e6CS/suIXGjtdn7rnnHqNgpgJp6nr7wx/+0Lz//vuZsnCf+RZ6URuqO61PUd52LERjZ9R15VCArrGUqU7hgFpUPoU6B5okRBO4KKnM6rJ92GGHGbUO1KQrem/HlowqQtK6cF0ydYNO2ok3CCCAAAIIIIBAjgLMApwjGJsjgAACCCCAQOkIKKikGXsVCNSPurmqZZuCLk8++aT7ufrqq41abvmx2HKt3X/+8x+jceSUFEz73//937RZhINfKo9au2WT8ilb7969XdbqVqqx5TJ1df3xj3/sus0qIPr000+7cQcVvJo1a5a56aabjJ24xAVPNYtvoZNa82l2XwU51erx3HPPNQcffLDrPq2WgL6r7XPPPed8dfxMY+Vpxt9MKXwOLrrooka7C/u8Onfu7Bfda01NjRuPULM2a9bnF154wbz33nsuoKxWg/r54x//aB588EE3s3HSzqE3vqWmVqm+JAQQQAABBBBAoDkECAA2hyp5IoAAAggggEDRCCggpLH59KM0Z84c88QTT5jrr7/evPPOO+7nzDPPNA888EDOZZ4wYYJRF1oFpNQNVIEgjf+XLql1mE9qhagurs2VfADQznDrWqLpeJmSgozqKq0f7aNglkzU0k1BxMsuu8y1bNMkJ4VM6kLrx77T8Q444IDI7MOBssgNslwZPgdqcdfUc6Cu5fpRUvdmBQJvv/12c//99xu1NtQ4gxr3US0so9KSJUvcavn7VptR27EOAQQQQAABBBBoisCm/hhNyYV9EUAAAQQQQACBEhHQpBff+9733KQOmvlW6dFHH3WtAnOpgsaEUzBMLdfUckst+sJj3UXlNXbs2GC1xiJszuQnr9Axpk6dmtOh1GVXNura/Oyzzwb7KsBZ6KSJPpRkly74p8/9WHxabkrS2IK+pV2hz0F1dbXrFqxWoZrlWUkB51deeSVtkf252XrrrdNuwwcIIIAAAggggEBTBQgANlWQ/RFAAAEEEECgJAXU+mvvvfd2Zdcsrr4VWjaV0Vh5alE4c+ZMoxaGGv8v0yQYPk8F1TSunpK61Sqf5kp77rlnkLXGP8w3qcx+XL1Mk2/km7+fQVcWankYlRRk1Wy7hUg6X4ceeqjL6qmnnjIfffRRIbJtkIe6g/uUzk0zGk+ZMsVttssuu/jNeUUAAQQQQAABBAouQACw4KRkiAACCCCAAALFIPDyyy9nnMlWMwG/+OKLrqgae853mc2m7D/4wQ/Mm2++6TbVZA+aUCSbpJZ1mohE6fPPP3fdh9euXZt2VwWI1AU3nzR48GAzdOhQt6vGqUuXNOlHeCKK1O3U8s53Ux0+fHjqx01+r8lGlBTki2phqDH75D179uwmH8tnoNl/FQhUwPHYY481X375pf+owauO/69//StpG507f+002OGrFQou+pTOTbZ+PMODDjrIb84rAggggAACCCBQcIH0g9QU/FBkiAACCCCAAAIItJyAuq6qC6tawml21u22284F+RTsUrfLG2+80bz77ruuQKeddlrGsfvCpf7b3/7mAkJat99++5kDDzzQTJw4MbxJ0rImjwgHgDSrriba0Hh3//73v10ZNAahxpFT11QF/T7++GM3ltzDDz/sxoX7yU9+kpRntm/URfnaa681zz//fNqJQH75y1+6mX617V577WW22GILozIvWrTIdV297rrr3OEUMFMgrtDp+OOPd0FRBULVNVtjD8pUFuoerONrrMbdd9/dTU5SiOOre7Qm6Pj5z39uJk+e7MYBPOOMM9z57Nu3r2uZOW3aNNdNXGMUqhuvJpPxrTdnzJhh9t13Xzdz8VFHHWV23HFHM3DgQFc0mi89IQAABXtJREFUtQpVUNUHM7fffnuTrnWf717dq1cvNzt1IepGHggggAACCCCAQJQAAcAoFdYhgAACCCCAQCwE1MJLLbUytdZS4Ovyyy/Pur4K/vikmWnDY+359eFXdTPWxBA+aTZeBYjOOeccF4TUBBHnnXee/7jBaz4zAPtMTj/9dBcAVFBKLSIV4ItK6v7897//3f1Efd6xY0dXVgW6Cp0UVPvrX//qgovqBnzFFVe4n/BxTjjhBKO6ZBojMLx9Nsua7ESBTr1qxmO15NRPVNJMxFETdCh4qJ90Sd3CNRlIuhmY77rrLrer6qcu6SQEEEAAAQQQQKC5BAgANpcs+SKAAAIIIIBAqwqce+65rtXfM888YzRbr7qQalZWpX79+rkWd5rBV60DWzop2HPDDTeYs846y9xyyy0uQKjA4sqVK426I6vF4Lhx48whhxxivvGNb+RdPM1wu9tuu7mWbHfeeWdkAFCtAzWByUsvveRaRmpyE3X5raqqMiNGjDAay07l1OQZzZXU8m/UqFEuAKeJORSQVKu4MWPGuFaBaiUYDqIWqhwKKh5xxBHmpptuMuqyq/H4dGwFPNWiT8FdtUbUTL4qj09qVaryPPnkk+aNN95wY0HOmzfPtRzUZCYq99FHH21OPfVUl5ffL/z6+uuvmy+++MKtki8JAQQQQAABBBBoToF2dtyRRHMegLwRQAABBBBAAAEEWk9AXVHVwkwTeSjIqAAjqfUF1J361ltvNQcffLB54oknWr9AlAABBBBAAAEEYi3AJCCxPr1UDgEEEEAAAQTausBxxx3nWhOqVV++E4q0dcNC11+B2DvuuMNl+7vf/a7Q2ZMfAggggAACCCDQQIAAYAMSViCAAAIIIIAAAvER0PhzGldP6eqrrzarVq2KT+VKtCYac7Kurs4oOJtugpASrRrFRgABBBBAAIEiFaALcJGeGIqFAAIIIIAAAggUUkCz6WpmX42nt9VWWxUya/LKQUCj7yggqwlPvv/975shQ4bksDebIoAAAggggAAC+QkQAMzPjb0QQAABBBBAAAEEEEAAAQQQQAABBBAoCQG6AJfEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAQIAJbEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAQIAJbEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAQIAJbEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAQIAJbEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAQIAJbEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAQIAJbEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAQIAJbEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAQIAJbEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAQIAJbEaaKQCCCAAAIIIIAAAggggAACCCCAAAII5CdAADA/N/ZCAAEEEEAAAQQQQAABBBBAAAEEEECgJAT+P8YsRpVCHtcLAAAAAElFTkSuQmCC\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            if (mpl.ratio != 1) {\\n\",\n       \"                fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n       \"            }\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        fig.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var backingStore = this.context.backingStorePixelRatio ||\\n\",\n       \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n       \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n       \"\\n\",\n       \"    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width * mpl.ratio);\\n\",\n       \"        canvas.attr('height', height * mpl.ratio);\\n\",\n       \"        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'] / mpl.ratio;\\n\",\n       \"    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n       \"    var x1 = msg['x1'] / mpl.ratio;\\n\",\n       \"    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x * mpl.ratio;\\n\",\n       \"    var y = canvas_pos.y * mpl.ratio;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"jpeg\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\", \\\"tif\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    var width = fig.canvas.width/mpl.ratio\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var width = this.canvas.width/mpl.ratio\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\" width=\\\"' + width + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\" width=\\\"640\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for s in symbols:\\n\",\n    \"    for d in dates:\\n\",\n    \"        _ = mkt_impact(s,d,plot=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/environment.py",
    "content": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as dates\nimport matplotlib\nimport subprocess\nimport os\nimport tqdm\nimport pickle\nimport utility\nfrom matplotlib.widgets import Button\nimport sys\n\nPROTOCOL_NAME = \"_ITCH50_\"\n\nclass ts:\n    \"\"\" Class to handle full depth order book\n    and messages data taken from the BookReconstructor\n    application. Looks for picked data in ../data/pk\n    if the data cannot not found calls the BookConstructor\n    application in order to reconstruct the book.\n\n    Parameters\n    ----------\n    date : string\n        date in mmddyyyy format\n    venue : string\n        NASDAQ venue (PSX,NASDAQ,BX)\n    stock : string\n        ticker in capital letter traded on the vanue\n    levels : int, optional\n        specify the maximum depth of the order book\n\n    \"\"\"\n    def __init__(self, date, venue, stock, PATH=os.path.dirname(os.path.realpath(__file__)), levels=5):\n        self.date = date\n        self.venue = venue\n        self.stock = stock\n        self.levels = levels\n        self.PKfileName = date+\".\"+venue+\"_\"+stock+\"_\"+str(levels)+\".pk\"\n        pk_folderPath = os.path.dirname(PATH)+'/data/pk/'\n\n        try:\n\n            PKfile = open(pk_folderPath+self.PKfileName,'rb')\n            [self.book, self.messages] = pickle.load(PKfile)\n\n        except:\n\n            print\n            print(\"Executing BookConstructor...\")\n\n            shellScriptPath = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))+\"/BookConstructor.sh\"\n\n            try:\n\n                subprocess.run([shellScriptPath, os.path.dirname(PATH)+\"/data/\", self.date, self.venue, self.stock, \"-n\", str(self.levels)])\n\n                bookDir = os.path.dirname(PATH)+\"/data/book/\"\n                messDir = os.path.dirname(PATH)+\"/data/messages/\"\n                nameFile = self.date+\".\"+self.venue+PROTOCOL_NAME+self.stock\n\n                self.book = pd.read_csv(bookDir+nameFile+\"_book_\"+str(levels)+\".csv\");\n                self.messages = pd.read_csv(messDir+nameFile+\"_message.csv\");\n\n                with open(pk_folderPath+self.PKfileName, 'wb') as file:\n                    print(\"pickling...\")\n                    pickle.dump([self.book, self.messages], file)\n\n            except:\n\n                print(\"No data found for: \", self. stock, self.venue, self.date)\n                print()\n                return\n\n        self.time = self.messages.time\n        self.ticksize = round(np.diff(np.unique(self.messages.price)).min(),6)\n\n        self.initial_param = {'ticksize':self.ticksize,'p0':self.messages.iloc[0].price}\n\n    def get_heat_map(self,t_boud,p_boud,n_t,n_p,levels):\n        \"\"\" Get the heat map and the mask parameter\n        in order to plot the order heatmap\n\n            Parameters\n            ----------\n            t_boud : list of int, optional\n                time bound, default value is from 9.00 am to 4.30 pm\n                in nanoseconds since 00:00\n            p_boud : list of int, optional\n                price bound, default value is from min to max of the\n                time series over the t_bound\n            n_t : int, optional\n                numbers of data points in the time discretization\n            n_p : int, optional\n                numbers of data points in the price discretization\n            levels : int, optional\n                maximum number of levels to dispalay in the book depth\n\n            Returns\n            -------\n            heat, array\n                n_t x n_p matrix with order volumes.\n\n            mask, array\n                boolean mask that limits time values\n                in order to be in the interval t_bound\n\n            tgrid2, array\n                n_t x n_p matrix result of meshgrid\n\n            pgrid2, array\n                n_t x n_p matrix result of meshgrid\n\n            \"\"\"\n\n        t_min, t_max = t_boud\n        p_min, p_max = p_boud\n        mask = (self.time > t_min) & (self.time <= t_max)\n        n_p = min(n_p,(p_max-p_min)/self.ticksize)*2\n\n        if(p_min==0): p_min = self.book['1_bid_price'].loc[mask].min()-0.1\n        if(p_max==999999): p_max = self.book['1_bid_price'].loc[mask].max()+0.1\n\n        tgrid = np.linspace(t_min, t_max, n_t)\n        bins = np.array(list(set(self.messages.price)))\n        bins.sort()\n        centers = (bins[1:]+bins[:-1])/2\n        pgrid = np.linspace(p_min, p_max, n_p)\n        # pgrid = bins[np.digitize(pgrid, centers)]\n        tgrid2, pgrid2 = np.meshgrid(tgrid, pgrid)\n        heat = np.zeros(tgrid2.shape)\n\n        max_levels = min(self.levels+1,levels)\n\n        for t,ti in tqdm.tqdm(zip(tgrid,range(len(tgrid)))):\n            cut = self.book.iloc[np.abs(self.time-t).idxmin()].iloc[1:]\n            for k in range(1,max_levels):\n                price = cut[str(k)+'_bid_price']\n                if not np.isnan(price) and p_boud[0]<=price<=p_boud[1]:\n                    p = np.abs(pgrid-price).argmin()\n                    vol = cut[str(k)+'_ask_vol']\n                    if not np.isnan(vol): heat[p][ti] += vol\n                price = cut[str(k)+'_ask_price']\n                if not np.isnan(price) and p_boud[0]<=price<=p_boud[1]:\n                    p = np.abs(pgrid-price).argmin()\n                    vol = cut[str(k)+'_ask_vol']\n                    if not np.isnan(vol): heat[p][ti] += vol\n\n        heat[heat==0] = np.nan\n\n        return heat, mask, tgrid2, pgrid2\n\n    def plot_liquidity_heatmap(self,t_boud=[34200000000000,57600000000000],\n                                p_boud=[0,999999],\n                                n_t=400,n_p=100,\n                                levels=100):\n\n        \"\"\" Plot the best bid and ask time series together with\n        the time series of the sizes of the orders.\n\n            Parameters\n            ----------\n            t_boud : list of int, optional\n                time bound, default value is from 9.00 am to 4.30 pm\n                in nanoseconds since 00:00\n            p_boud : list of int, optional\n                price bound, default value is from min to max of the\n                time series over the t_bound\n            n_t : int, optional\n                numbers of data points in the time discretization\n            n_p : int, optional\n                numbers of data points in the price discretization\n            levels : int, optional\n                maximum number of levels to dispalay in the book depth\n            \"\"\"\n\n        def refocus(event):\n\n            global cs, bar\n            heat, _, tgrid2, pgrid2 = self.get_heat_map(x_b, y_b, n_t, n_p, levels)\n            cs.remove()\n\n            cs = ax.pcolormesh(tgrid2, pgrid2, heat, cmap=cmap,\n                        norm=matplotlib.colors.LogNorm(vmin=vmin, vmax=vmax))\n            ax.grid()\n            plt.draw()\n\n        def on_lims_change(axes):\n            global x_b, y_b\n            [x_b,y_b] = ax.get_xlim(),ax.get_ylim()\n            print(x_b,y_b)\n\n        cmap = plt.cm.jet\n\n        heat, mask, tgrid2, pgrid2 = self.get_heat_map(t_boud,p_boud,n_t,n_p,levels)\n\n        fig, ax = plt.subplots()\n\n        plt.subplots_adjust(bottom=0.2)\n\n        plt.title(self.date + ' @ ' + self.venue + ': ' + self.stock)\n\n        plt.xlabel('ns since 00:00')\n        plt.ylabel('price')\n\n        ts_1, = ax.plot(self.time.loc[mask],\n                        self.book['1_bid_price'].loc[mask],\n                        c='crimson', linewidth=0.9)\n\n        ts_2, = ax.plot(self.time.loc[mask],\n                        self.book['1_ask_price'].loc[mask],\n                        c='teal', linewidth=0.9)\n\n        plt.legend((ts_1, ts_2), ('bid', 'ask'))\n\n        global cs, bar\n        vmin, vmax = [np.nanmin(heat),np.nanmax(heat)]\n\n        cs = ax.pcolormesh(tgrid2, pgrid2, heat,\n                            cmap=cmap,\n                            norm=matplotlib.colors.LogNorm(vmin=vmin, vmax=vmax))\n\n        bar = fig.colorbar(cs,ax=ax,\n                        orientation='vertical',\n                        fraction=.1,\n                        extend='both',\n                        extendfrac='auto')\n\n        bar.set_label('Order size', rotation=270)\n\n        ax.callbacks.connect('xlim_changed', on_lims_change)\n        ax.callbacks.connect('ylim_changed', on_lims_change)\n\n        axprev = plt.axes([0.868, 0.90, 0.1, 0.068])\n        bprev = Button(axprev, 'Refresh')\n        bprev.on_clicked(refocus)\n        ax.grid()\n        plt.show()\n\n    def get_data_up_to(self,t):\n        \"\"\" return book df up to time stamp t\n\n            Parameters\n            ----------\n            t : int\n                time of when to truncate the data\n            \"\"\"\n        return self.book[self.time<=t], self.messages[self.time<=t]\n\n    def get_data_at_time(self,idx):\n        \"\"\" return book and message data at index i\n\n            Parameters\n            ----------\n            idx : int\n                query the data at index idx\n        \"\"\"\n        return self.book.iloc[idx], self.messages.iloc[idx]\n\n    def get_last_execution(self,t,idx=1):\n        \"\"\" get the last execution happend before time stamp t\n\n            Parameters\n            ----------\n            t : int\n                time of when to truncate the data\n            idx : ind, optional\n                get idx-last execution message, defualt is 1\n\n        \"\"\"\n        types = ['E']\n        sliced = self.messages[self.time<=t]\n        if not sliced.loc[self.messages.type == 'E'].empty:\n            return sliced.loc[self.messages.type == 'E'].iloc[-idx]\n        else:\n            return pd.DataFrame(columns = self.messages.columns)\n\n    def plot_order_book(self,i,index='true',**kwargs):\n\n        \"\"\" plot the barplot of the order book at time or index i\n\n            Parameters\n            ----------\n            i : int\n                depending of the boolean index (dault=True) plots\n                the order book at index (time) i\n            index : boolean, optional\n                if True, i is the index\n                if False, i is the time\n            **kwargs :\n                xmin, xmax : float, specify the bounds for the xaxis\n                ymax : float, specify the upper bound for the yaxis\n                block : bool, wheter to block at the plot or continue the code\n        \"\"\"\n\n        [price, size, t, idx] = self.get_book_for_bar_plot(i,index)\n\n        color = ['red','green']*int(len(size)/2)\n\n        fig, ax = plt.subplots()\n\n        rect = plt.bar(price,size,width=self.ticksize/2,color=color)\n        fig.suptitle('time: '+str(t))\n        ax.grid(True)\n        plt.subplots_adjust(bottom=0.2)\n\n        if 'xmin' and 'xmax' in kwargs.keys():\n            plt.xlim((kwargs['xmin'],kwargs['xmax']))\n\n        if 'ymax' in kwargs.keys():\n            plt.ylim((0,kwargs['ymax']))\n\n        if 'block' in kwargs.keys():\n            plt.show(kwargs['block'])\n        else:\n            plt.show()\n        return fig\n\n    def get_book_for_bar_plot(self,i,index):\n\n        \"\"\" get the barplot data of the order book at time or index i\n\n            Parameters\n            ----------\n            i : int\n                depending of the boolean index (dault=True) plots\n                the order book at index (time) i\n            index : boolean\n                if True, i is the index\n                if False, i is the time\n        \"\"\"\n\n        if index:\n            data = self.book.iloc[i]\n            t = self.time[i]\n            idx = i\n        else:\n            data = self.book[self.book.time<i].iloc[-1]\n            t = int(data.time)\n            idx = data.index\n        price = data.iloc[1:-1:2]\n        size = data.iloc[2::2]\n        return price,size,t,idx\n\nif __name__ == '__main__':\n    argc = len(sys.argv)\n    if argc == 1:\n        date = '01302019'\n        venue = 'NASDAQ'\n        stock = 'INTC'\n    else:\n        date = sys.argv[1]\n        venue = sys.argv[2]\n        stock = sys.argv[3]\n    if argc == 5:\n        PATH = sys.argv[4]\n    else:\n        PATH = os.path.dirname(os.path.realpath(__file__))\n    a = ts(date,venue,stock,PATH,20)\n    a.plot_liquidity_heatmap()\n\n"
  },
  {
    "path": "examples/tree_from_forest.dot",
    "content": "digraph Tree {\nnode [shape=box] ;\n0 [label=\"X[22] <= 209.995\\nmse = 0.0\\nsamples = 725179\\nvalue = 0.0\"] ;\n1 [label=\"X[22] <= 140.005\\nmse = 0.0\\nsamples = 721809\\nvalue = 0.0\"] ;\n0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n2 [label=\"X[1] <= 599.5\\nmse = 0.002\\nsamples = 9496\\nvalue = -0.012\"] ;\n1 -> 2 ;\n3 [label=\"X[15] <= 1198.0\\nmse = 0.0\\nsamples = 6209\\nvalue = 0.01\"] ;\n2 -> 3 ;\n4 [label=\"X[11] <= 500.5\\nmse = 0.0\\nsamples = 4947\\nvalue = 0.004\"] ;\n3 -> 4 ;\n5 [label=\"mse = 0.0\\nsamples = 1135\\nvalue = -0.008\"] ;\n4 -> 5 ;\n6 [label=\"mse = 0.0\\nsamples = 3812\\nvalue = 0.007\"] ;\n4 -> 6 ;\n7 [label=\"X[7] <= 315.0\\nmse = 0.001\\nsamples = 1262\\nvalue = 0.036\"] ;\n3 -> 7 ;\n8 [label=\"mse = 0.0\\nsamples = 941\\nvalue = 0.048\"] ;\n7 -> 8 ;\n9 [label=\"mse = 0.0\\nsamples = 321\\nvalue = -0.0\"] ;\n7 -> 9 ;\n10 [label=\"X[9] <= 771.5\\nmse = 0.002\\nsamples = 3287\\nvalue = -0.053\"] ;\n2 -> 10 ;\n11 [label=\"X[3] <= 1099.5\\nmse = 0.001\\nsamples = 825\\nvalue = -0.001\"] ;\n10 -> 11 ;\n12 [label=\"mse = 0.0\\nsamples = 737\\nvalue = 0.008\"] ;\n11 -> 12 ;\n13 [label=\"mse = 0.001\\nsamples = 88\\nvalue = -0.071\"] ;\n11 -> 13 ;\n14 [label=\"X[17] <= 606.0\\nmse = 0.001\\nsamples = 2462\\nvalue = -0.071\"] ;\n10 -> 14 ;\n15 [label=\"mse = 0.0\\nsamples = 2182\\nvalue = -0.08\"] ;\n14 -> 15 ;\n16 [label=\"mse = 0.0\\nsamples = 280\\nvalue = 0.0\"] ;\n14 -> 16 ;\n17 [label=\"X[22] <= 179.195\\nmse = 0.0\\nsamples = 712313\\nvalue = 0.0\"] ;\n1 -> 17 ;\n18 [label=\"X[19] <= 530.5\\nmse = 0.0\\nsamples = 708842\\nvalue = 0.0\"] ;\n17 -> 18 ;\n19 [label=\"X[22] <= 149.06\\nmse = 0.0\\nsamples = 111438\\nvalue = -0.001\"] ;\n18 -> 19 ;\n20 [label=\"mse = 0.0\\nsamples = 2567\\nvalue = 0.013\"] ;\n19 -> 20 ;\n21 [label=\"mse = 0.0\\nsamples = 108871\\nvalue = -0.002\"] ;\n19 -> 21 ;\n22 [label=\"X[9] <= 702.5\\nmse = 0.0\\nsamples = 597404\\nvalue = 0.001\"] ;\n18 -> 22 ;\n23 [label=\"mse = 0.0\\nsamples = 265418\\nvalue = 0.001\"] ;\n22 -> 23 ;\n24 [label=\"mse = 0.0\\nsamples = 331986\\nvalue = 0.0\"] ;\n22 -> 24 ;\n25 [label=\"X[9] <= 71.0\\nmse = 0.0\\nsamples = 3471\\nvalue = 0.012\"] ;\n17 -> 25 ;\n26 [label=\"X[14] <= 162.605\\nmse = 0.0\\nsamples = 579\\nvalue = 0.045\"] ;\n25 -> 26 ;\n27 [label=\"mse = 0.0\\nsamples = 454\\nvalue = 0.05\"] ;\n26 -> 27 ;\n28 [label=\"mse = 0.0\\nsamples = 125\\nvalue = 0.029\"] ;\n26 -> 28 ;\n29 [label=\"X[15] <= 125.5\\nmse = 0.0\\nsamples = 2892\\nvalue = 0.006\"] ;\n25 -> 29 ;\n30 [label=\"mse = 0.0\\nsamples = 1540\\nvalue = 0.01\"] ;\n29 -> 30 ;\n31 [label=\"mse = 0.0\\nsamples = 1352\\nvalue = 0.001\"] ;\n29 -> 31 ;\n32 [label=\"X[1] <= 575.0\\nmse = 0.003\\nsamples = 3370\\nvalue = -0.02\"] ;\n0 -> 32 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n33 [label=\"X[15] <= 1112.5\\nmse = 0.0\\nsamples = 1841\\nvalue = 0.025\"] ;\n32 -> 33 ;\n34 [label=\"X[9] <= 71.0\\nmse = 0.0\\nsamples = 1089\\nvalue = 0.01\"] ;\n33 -> 34 ;\n35 [label=\"X[8] <= 163.475\\nmse = 0.001\\nsamples = 50\\nvalue = 0.039\"] ;\n34 -> 35 ;\n36 [label=\"mse = 0.0\\nsamples = 49\\nvalue = 0.045\"] ;\n35 -> 36 ;\n37 [label=\"mse = -0.0\\nsamples = 1\\nvalue = -0.15\"] ;\n35 -> 37 ;\n38 [label=\"X[11] <= 1011.0\\nmse = 0.0\\nsamples = 1039\\nvalue = 0.008\"] ;\n34 -> 38 ;\n39 [label=\"mse = 0.0\\nsamples = 150\\nvalue = -0.002\"] ;\n38 -> 39 ;\n40 [label=\"mse = 0.0\\nsamples = 889\\nvalue = 0.01\"] ;\n38 -> 40 ;\n41 [label=\"X[1] <= 356.0\\nmse = 0.0\\nsamples = 752\\nvalue = 0.048\"] ;\n33 -> 41 ;\n42 [label=\"X[5] <= 160.0\\nmse = 0.0\\nsamples = 33\\nvalue = 0.01\"] ;\n41 -> 42 ;\n43 [label=\"mse = 0.0\\nsamples = 6\\nvalue = 0.034\"] ;\n42 -> 43 ;\n44 [label=\"mse = 0.0\\nsamples = 27\\nvalue = 0.004\"] ;\n42 -> 44 ;\n45 [label=\"X[13] <= 856.0\\nmse = 0.0\\nsamples = 719\\nvalue = 0.05\"] ;\n41 -> 45 ;\n46 [label=\"mse = 0.0\\nsamples = 718\\nvalue = 0.05\"] ;\n45 -> 46 ;\n47 [label=\"mse = -0.0\\nsamples = 1\\nvalue = 0.02\"] ;\n45 -> 47 ;\n48 [label=\"X[17] <= 606.0\\nmse = 0.0\\nsamples = 1529\\nvalue = -0.075\"] ;\n32 -> 48 ;\n49 [label=\"X[19] <= 615.0\\nmse = 0.0\\nsamples = 1476\\nvalue = -0.078\"] ;\n48 -> 49 ;\n50 [label=\"X[5] <= 521.0\\nmse = 0.0\\nsamples = 32\\nvalue = 0.01\"] ;\n49 -> 50 ;\n51 [label=\"mse = 0.0\\nsamples = 1\\nvalue = -0.01\"] ;\n50 -> 51 ;\n52 [label=\"mse = 0.0\\nsamples = 31\\nvalue = 0.01\"] ;\n50 -> 52 ;\n53 [label=\"X[1] <= 704.5\\nmse = 0.0\\nsamples = 1444\\nvalue = -0.08\"] ;\n49 -> 53 ;\n54 [label=\"mse = 0.0\\nsamples = 1438\\nvalue = -0.08\"] ;\n53 -> 54 ;\n55 [label=\"mse = 0.0\\nsamples = 6\\nvalue = 0.0\"] ;\n53 -> 55 ;\n56 [label=\"X[15] <= 242.5\\nmse = 0.0\\nsamples = 53\\nvalue = 0.003\"] ;\n48 -> 56 ;\n57 [label=\"X[11] <= 550.0\\nmse = 0.0\\nsamples = 6\\nvalue = -0.035\"] ;\n56 -> 57 ;\n58 [label=\"mse = 0.0\\nsamples = 5\\nvalue = -0.04\"] ;\n57 -> 58 ;\n59 [label=\"mse = 0.0\\nsamples = 1\\nvalue = 0.01\"] ;\n57 -> 59 ;\n60 [label=\"X[17] <= 706.0\\nmse = 0.0\\nsamples = 47\\nvalue = 0.007\"] ;\n56 -> 60 ;\n61 [label=\"mse = 0.0\\nsamples = 7\\nvalue = 0.034\"] ;\n60 -> 61 ;\n62 [label=\"mse = 0.0\\nsamples = 40\\nvalue = 0.003\"] ;\n60 -> 62 ;\n}"
  },
  {
    "path": "examples/utility.py",
    "content": "import pandas as pd\n\ndef parseNanosecondsToDateTime(nanosecs,date=None):\n    \"\"\" Converts from nanosecs to pandas timestamp.\n    \"\"\"\n\n    time = pd.Timedelta(nanosecs, unit ='ns')\n\n    if date:\n        date_str = pd.Timestamp.strptime(date,'%m%d%Y')\n    else:\n        date_str = pd.Timestamp.strptime('01012001','%m%d%Y')\n    return date_str + time\n\ndef get_last_execution(message_df,t,idx=1):\n    \"\"\" get the last execution happend before time stamp t\n    \"\"\"\n    types = ['E']\n    sliced = message_df[message_df.time<=t]\n    if len(sliced.loc[message_df.type == 'E'])>=idx:\n        return sliced.loc[message_df.type == 'E'].iloc[-idx]\n    else:\n        return pd.DataFrame(columns = message_df.columns)\n\ndef normalize(vector):\n    return vector/len(vector)\n"
  },
  {
    "path": "gtests/Message_test.cpp",
    "content": "#include <gtest/gtest.h>\n#include <gmock/gmock.h>\n#include <Message.hpp>\n#include <utility.hpp>\n\nstruct Message_Test : public testing::Test{\n    Message *emptyMessage;\n    Message *fullMessage;\n    void SetUp(){\n        emptyMessage = new Message();\n        fullMessage = new Message('A',45749,28800006862979);\n    }\n    void TearDown(){\n        delete emptyMessage;\n        delete fullMessage;\n    }\n};\n\nTEST_F(Message_Test, emptynessTest){\n    ASSERT_EQ(1, emptyMessage->isEmpty());\n}\n\nTEST_F(Message_Test, getStringEmptyTest){\n\n    ASSERT_EQ(\",,,,,,,,,,,\\n\", emptyMessage->getString());\n}\n\nTEST_F(Message_Test, getStringAndSettersTest){\n    const char mpid[] = \"tmpd\";\n    fullMessage->setSide(1);\n    fullMessage->setRemSize(93);\n    fullMessage->setPrice(11.79);\n    fullMessage->setCancSize(10);\n    fullMessage->setExecSize(20);\n    fullMessage->setOldId(123456);\n    fullMessage->setOldSize(110);\n    fullMessage->setOldPrice(33.333333);\n    fullMessage->setMPID(*mpid);\n    ASSERT_EQ(\"28800006862979,A,45749,1,93,11.79,10,20,123456,110,33.3333,tmpd\\n\", fullMessage->getString());\n}\n\nTEST_F(Message_Test, getMPIDTest){\n    const char mpid[]=\"tmpd\";\n    fullMessage->setMPID(*mpid);\n    std::string mpid_string(fullMessage->getMPID());\n    ASSERT_EQ(\"tmpd\", mpid_string);\n}\n\nTEST_F(Message_Test, getSetMPIDTest){\n    const char mpid[]=\"tmpd\";\n    fullMessage->setMPID(*mpid);\n    emptyMessage->setMPID(*fullMessage->getMPID());\n    std::string mpid_string(emptyMessage->getMPID());\n    ASSERT_EQ(\"tmpd\", mpid_string);\n}\n"
  },
  {
    "path": "gtests/OrderBook_test.cpp",
    "content": "#include <gtest/gtest.h>\n#include <gmock/gmock.h>\n#include <OrderBook.hpp>\n\nstruct OrderBook_Test : public testing::Test{\n    OrderBook *book;\n    void SetUp(){\n        book = new OrderBook();\n        book->setTimeStamp(123456789);\n    }\n    void TearDown(){\n        delete book;\n    }\n};\n\nTEST_F(OrderBook_Test, NoOrdersTest){\n    ASSERT_EQ(\"123456789,,,,,,,,\\n\", book->getString(2));\n}\n\nTEST_F(OrderBook_Test, AddOrdersNotExisting){\n    book->modifySize(99.99,123,0);\n    book->modifySize(101.99,12,1);\n    book->modifySize(105.99,12,1);\n    ASSERT_EQ(\"123456789,99.99,123,101.99,12,,,105.99,12\\n\", book->getString(2));\n}\n\nTEST_F(OrderBook_Test, AddExistingOrders){\n    book->modifySize(99.99,123,0);\n    book->modifySize(101.99,12,1);\n    book->modifySize(101.99,-11,1);\n    book->modifySize(105.99,12,1);\n    book->modifySize(105.99,-12,1);\n    ASSERT_EQ(\"123456789,99.99,123,101.99,1,,,,\\n\", book->getString(2));\n}\n"
  },
  {
    "path": "gtests/OrderPool_test.cpp",
    "content": "#include <gtest/gtest.h>\n#include <gmock/gmock.h>\n#include <OrderPool.hpp>\n\nstruct OrderPool_Test : public testing::Test{\n    OrderPool *pool;\n    Order *order;\n    void SetUp(){\n        pool = new OrderPool();\n    }\n    void TearDown(){\n        delete pool;\n    }\n};\n\nTEST_F(OrderPool_Test, findOrder){\n    char mpid[]=\"tmpd\";\n    pool->addToOrderPool(12345,0,1000,99.977,mpid);\n    id_type id = 12345;\n    ASSERT_EQ(id, pool->searchOrderPool(12345).getId());\n    ASSERT_EQ(0, pool->searchOrderPool(12345).getSide());\n    ASSERT_EQ(1000, pool->searchOrderPool(12345).getSize());\n    ASSERT_EQ(99.977, pool->searchOrderPool(12345).getPrice());\n    std::string mpid_string(pool->searchOrderPool(12345).getMPID());\n    ASSERT_EQ(\"tmpd\", mpid_string);\n}\n\nTEST_F(OrderPool_Test, nonExistingOrder){\n    char mpid[]=\"tmpd\";\n    pool->addToOrderPool(12345,0,1000,99.977,mpid);\n    ASSERT_EQ(1, pool->searchOrderPool(1111).isEmpty());\n}\n"
  },
  {
    "path": "gtests/Order_test.cpp",
    "content": "#include <gtest/gtest.h>\n#include <gmock/gmock.h>\n#include <Order.hpp>\n#include <utility.hpp>\n\nstruct Order_Test : public testing::Test{\n    Order *emptyOrder;\n    Order *fullOrder;\n    void SetUp(){\n        price_type price = 23;\n        size_type size = 100;\n        id_type id = 1234;\n        side_type side = 0;\n        emptyOrder = new Order();\n        char mpid[] = \"tmpd\";\n        fullOrder = new Order(id, side, size, price, mpid);\n    }\n    void TearDown(){\n        delete emptyOrder;\n        delete fullOrder;\n    }\n};\n\nTEST_F(Order_Test, emptynessTest){\n    ASSERT_EQ(1, emptyOrder->isEmpty());\n}\n\nTEST_F(Order_Test, fullnessTest){\n    ASSERT_EQ(0, fullOrder->isEmpty());\n}\n\nTEST_F(Order_Test, negativeSizeTest){\n\n    // act\n    fullOrder->addSize(-75);\n\n    // assert\n    ASSERT_EQ(25, fullOrder->getSize());\n}\n"
  },
  {
    "path": "gtests/Reader_test.cpp",
    "content": "#include <gtest/gtest.h>\n#include <gmock/gmock.h>\n#include <Reader.hpp>\n#include <Message.hpp>\n#include <Writer.hpp>\n\nclass MockReader: public Reader{\npublic:\n    MOCK_METHOD1(readBytesIntoMessage, void(const long &size));\n    MOCK_METHOD0(getKey, char());\n    MockReader(const std::string &s):Reader(s){};\n    void mock_readBytes_A(){\n        setMessage(\"\\x00\\x00\\x00\\x00\\x1a\\x31\\x86\\x2d\\xb8\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\xb2\\xb5\\x53\\x00\\x00\\x00\\x5d\\x4d\\x41\\x52\\x54\\x49\\x4e\\x4f\\x20\\x00\\x01\\xcc\\x8c\");\n    }\n    void mock_readBytes_F(){\n        setMessage(\"\\x00\\x00\\x00\\x00\\x1a\\x31\\x86\\x2d\\xb8\\x83\\x00\\x00\\x00\\x00\\x00\\x00\\xb2\\xb5\\x53\\x00\\x00\\x00\\x5d\\x4d\\x41\\x52\\x54\\x49\\x4e\\x4f\\x20\\x00\\x01\\xcc\\x8c\\x74\\x6d\\x70\\x64\");\n    }\n    void mock_readBytes_E(){\n        setMessage(\"\\x00\\x00\\x00\\x00\\x1f\\x1b\\x50\\x4f\\xbd\\x72\\x00\\x00\\x00\\x00\\x00\\x03\\xfd\\x79\\x00\\x00\\x00\\xc8\\x5d\\x50\\x4b\\x47\\x20\\x20\\x20\\x20\\x20\\x00\\x01\\xcc\\x8c\");\n    }\n    void mock_readBytes_X(){\n        setMessage(\"\\x00\\x00\\x00\\x00\\xff\\xff\\xff\\xff\\xff\\xfe\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xfe\\xff\\xff\\xff\\xfe\");\n    }\n    void mock_readBytes_D(){\n        setMessage(\"\\x00\\x00\\x00\\x00\\xff\\xff\\xff\\xff\\xff\\xfe\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xfe\");\n    }\n    void mock_readBytes_U(){\n        setMessage(\"\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\xff\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\xff\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xff\\x00\\x00\\x00\\x01\\x00\\x00\\x2a\\xf8\");\n    }\n    void mock_readBytes_P(){\n        setMessage(\"\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\xff\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xff\\x53\\x00\\x00\\x00\\xff\\x4d\\x41\\x52\\x54\\x49\\x4e\\x4f\\x20\\x00\\x00\\x19\\x80\\x00\\x00\\x01\\x00\\x00\\x00\\x2a\\xf8\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\");\n    }\n};\n\nstruct Reader_Test : public testing::Test{\n    MockReader *mk_reader;\n    void SetUp(){\n        mk_reader = new MockReader(\"MARTINO \");\n    }\n    void TearDown(){\n        delete mk_reader;\n    }\n};\n\nTEST_F(Reader_Test, conversion_A) {\n    // Arrange\n    EXPECT_CALL((*mk_reader), getKey())\n    .Times(1)\n    .WillOnce(testing::Return('A'));\n\n    EXPECT_CALL((*mk_reader), readBytesIntoMessage(testing::_))\n    .Times(1)\n    .WillOnce(testing::InvokeWithoutArgs(&(*mk_reader), &MockReader::mock_readBytes_A));\n\n    // Act\n    Message createdMsg = mk_reader->createMessage();\n    // Assert\n    ASSERT_EQ(\"28800006862979,A,45749,1,93,11.79,,,,,,\\n\",createdMsg.getString());\n}\n\nTEST_F(Reader_Test, conversion_F) {\n    // Arrange\n    EXPECT_CALL((*mk_reader), getKey())\n    .Times(1)\n    .WillOnce(testing::Return('F'));\n\n    EXPECT_CALL((*mk_reader), readBytesIntoMessage(testing::_))\n    .Times(1)\n    .WillOnce(testing::InvokeWithoutArgs(&(*mk_reader), &MockReader::mock_readBytes_F));\n\n    // Act\n    Message createdMsg = mk_reader->createMessage();\n    // Assert\n    ASSERT_EQ(\"28800006862979,A,45749,1,93,11.79,,,,,,tmpd\\n\",createdMsg.getString());\n}\n\nTEST_F(Reader_Test, conversion_E) {\n    // Arrange\n    EXPECT_CALL((*mk_reader), getKey())\n    .Times(1)\n    .WillOnce(testing::Return('E'));\n\n    EXPECT_CALL((*mk_reader), readBytesIntoMessage(testing::_))\n    .Times(1)\n    .WillOnce(testing::InvokeWithoutArgs(&(*mk_reader), &MockReader::mock_readBytes_E));\n\n    // Act\n    Message createdMsg = mk_reader->createMessage();\n    // Assert\n    ASSERT_EQ(\"34202171981170,E,261497,0,,,,200,,,,\\n\",createdMsg.getString());\n}\n\nTEST_F(Reader_Test, conversion_X) {\n    // Arrange\n    EXPECT_CALL((*mk_reader), getKey())\n    .Times(1)\n    .WillOnce(testing::Return('X'));\n\n    EXPECT_CALL((*mk_reader), readBytesIntoMessage(testing::_))\n    .Times(1)\n    .WillOnce(testing::InvokeWithoutArgs(&(*mk_reader), &MockReader::mock_readBytes_X));\n\n    // Act\n    Message createdMsg = mk_reader->createMessage();\n    // Assert\n    ASSERT_EQ(\"281474976710654,D,18446744073709551614,0,,,4294967294,,,,,\\n\",createdMsg.getString());\n}\n\nTEST_F(Reader_Test, conversion_D) {\n    // Arrange\n    EXPECT_CALL((*mk_reader), getKey())\n    .Times(1)\n    .WillOnce(testing::Return('U'));\n\n    EXPECT_CALL((*mk_reader), readBytesIntoMessage(testing::_))\n    .Times(1)\n    .WillOnce(testing::InvokeWithoutArgs(&(*mk_reader), &MockReader::mock_readBytes_U));\n\n    // Act\n    Message createdMsg = mk_reader->createMessage();\n    // Assert\n    ASSERT_EQ(\"1099511628031,R,255,0,1,1.1,,,1099511628031,,,\\n\",createdMsg.getString());\n}\n\nTEST_F(Reader_Test, conversion_P) {\n    // Arrange\n    EXPECT_CALL((*mk_reader), getKey())\n    .Times(1)\n    .WillOnce(testing::Return('P'));\n\n    EXPECT_CALL((*mk_reader), readBytesIntoMessage(testing::_))\n    .Times(1)\n    .WillOnce(testing::InvokeWithoutArgs(&(*mk_reader), &MockReader::mock_readBytes_P));\n\n    // Act\n    Message createdMsg = mk_reader->createMessage();\n    // Assert\n    ASSERT_EQ(\"1099511628031,P,255,1,,0.6528,,255,,,,\\n\",createdMsg.getString());\n}\n"
  },
  {
    "path": "gtests/main_gtest.cpp",
    "content": "#include <gtest/gtest.h>\n#include <gmock/gmock.h>\n\nint main(int argc, char **argv){\n    testing::InitGoogleTest(&argc, argv);\n    return RUN_ALL_TESTS();\n}\n"
  },
  {
    "path": "include/BookConstructor.hpp",
    "content": "#ifndef BOOKCONSTRUCTOR_H_\n#define BOOKCONSTRUCTOR_H_\n\n#include <Message.hpp>\n#include <Order.hpp>\n#include <OrderPool.hpp>\n#include <OrderBook.hpp>\n#include <Reader.hpp>\n#include <Writer.hpp>\n\n\nclass BookConstructor{\nprivate:\n    Message message;\n    OrderBook book;\n    OrderPool pool;\n    Reader message_reader;\n    Writer messageWriter;\n    Writer bookWriter;\n    Writer parserWriter;\n    size_t levels;\npublic:\n    BookConstructor(const std::string &inputMessageCSV,\n                    const std::string &outputMessageCSV,\n                    const std::string &outputBookCSV,\n                    const std::string &_stock,\n                    const size_t &_levels);\n    ~BookConstructor();\n    void start(void);\n    void next(void);\n    bool updateMessage(void);\n    void updateBook(void);\n    void updatePool(void);\n    void WriteBookAndMessage(void);\n};\n\n#endif /* BOOKCONSTRUCTOR_H_ */\n"
  },
  {
    "path": "include/Message.hpp",
    "content": "#ifndef _MESSAGE_H_\n#define _MESSAGE_H_\n\n#include <cstring>\n#include <sstream>\n#include <iostream>\n#include <utility.hpp>\n\nclass Message{\nprivate:\n    char type = 0;\n    id_type id = ID_DEFAULT;\n    time_type timestamp = 0;\n\n    side_type side = SIDE_DEFAULT;\n    price_type price = PRICE_DEFAULT;\n    size_type remSize = SIZE_DEFAULT;\n    size_type cancSize = SIZE_DEFAULT;\n    size_type execSize = SIZE_DEFAULT;\n\n    id_type oldId = ID_DEFAULT;\n    price_type oldPrice = PRICE_DEFAULT;\n    size_type oldSize = SIZE_DEFAULT;\n    char mpid[5] = \"\";\n\npublic:\n    Message() = default;\n    Message(const char& type,const id_type &id,\n        const time_type &timestamp);\n\n    /**\n     * Setter for the message. Transforms the Nasdaq type definitions in ours.\n     *\n     * - NASDAQ   --> Custom\n     * - A,F      --> (A)dd\n     * - D,X      --> (D)elete\n     * - U        --> (R)eplace\n     * - E        --> (E)xecution\n     * - P        --> P, hidden execution\n     * - C        --> C, execution at different price\n     *\n     * @param[in] _type type string: according to the definition of NASDAQ\n     */\n    void setType(const char &);\n    void setId(const id_type &);\n    void setTimeStamp(const time_type &);\n    void setSide(const side_type &);\n    void setPrice(const price_type &);\n    void setRemSize(const size_type &);\n    void setCancSize(const size_type &);\n    void setExecSize(const size_type &);\n    void setOldId(const id_type &id);\n    void setOldPrice(const price_type &);\n    void setOldSize(const size_type &);\n    void setMPID(const char &);\n\n    char getType(void) const;\n    id_type getId(void) const;\n    time_type getTimeStamp(void) const;\n    side_type getSide(void) const;\n    price_type getPrice(void) const;\n    size_type getRemSize(void) const;\n    size_type getCancSize(void) const;\n    size_type getExecSize(void) const;\n    id_type getOldId(void) const;\n    price_type getOldPrice(void) const;\n    size_type getOldSize(void) const;\n    const char *getMPID(void) const;\n\n    bool isEmpty(void) const;\n\n    /**\n     * Get string representation for writing into the csv\n     *\n     * @return string representation of message.\n     *          If field is not being setted it is just an empty char\n     *          separated by commas.\n     *\n     */\n    std::string getString(void) const;\n    void print(void) const;\n};\n\n\n#endif/*_MESSAGE_H_*/\n"
  },
  {
    "path": "include/Order.hpp",
    "content": "#ifndef _ORDER_H_\n#define _ORDER_H_\n\n#include <iostream>\n#include <utility.hpp>\n#include <Message.hpp>\n\nclass Order{\n    id_type id = ID_DEFAULT;\n    side_type side;\n    size_type size = SIZE_DEFAULT;\n    price_type price = PRICE_DEFAULT;\n    char mpid[5] = \"\";\n\n    public:\n    Order() = default;\n    Order(id_type _id, side_type _side, size_type _size, price_type _price, const char *_mpid);\n\n    /**\n    * Add or subtract size to the order.\n    *\n    * @param[in] _size : size to add or detract (if size is negative) to the order\n    */\n    void addSize(size_type size);\n\n    // setters\n    // void setId(id_type _id);\n    // void setSide(side_type _side);\n    // void setSize(size_type size);\n    // void setPrice(price_type _price);\n\n    // getters\n    id_type getId(void) const;\n    side_type getSide(void) const;\n    size_type getSize(void) const;\n    price_type getPrice(void) const;\n    const char * getMPID(void) const;\n\n    void print(void) const;\n\n    /**\n    * Check whether the Order is unsetted or not.\n    *\n    * @return bool, 1 is unsetted (Empty), 0 if setted.\n    */\n    bool isEmpty(void) const;\n};\n\n#endif /*_ORDER_H_*/\n"
  },
  {
    "path": "include/OrderBook.hpp",
    "content": "#ifndef _OrderBook_H_\n#define _OrderBook_H_\n\n#include <map>\n#include <string>\n#include <sstream>\n#include <iostream>\n#include <utility.hpp>\n\n/**\n * Class containing list of buy and sell orders for a specific security\n * organized by price level into 2 ordered maps. An order book lists the number of\n * shares being bid or offered at each price point available, keeping track of time\n * of every change made.\n *\n */\n\nclass OrderBook{\n    time_type timestamp;\n    std::map<price_type, size_type> buySide;\n    std::map<price_type, size_type> sellSide;\n\n    public:\n    OrderBook(void) = default;\n\n    /**\n     * Make comma-separated string from information available\n     * in the OrderBook about the best bid/ask prices and corresponding sizes up to number of levels :\n     * \"1.BidPrice, 1.BidSize,1.AskPrice,1.AskSize,..,level.BidPrice, level.BidSize,level.AskPrice,level.AskSize\"\n     *\n     * @param[in] level up to what level to write the price/size tuple.\n     *\n     */\n    std::string getString(const size_t &) const;\n\n    /**\n     * Performs actions on the double map representing the OrderBook\n     *\n     * @param[in] price modify map corresponding to price\n     * @param[in] size add (or delete if size is negative) the size corresponding to price\n     * @param[in] side 0 for buy side and 1 for sell side.\n     *\n     */\n    void modifySize(price_type, size_type, side_type);\n    void setTimeStamp(const time_type &);\n\n    /**\n     * Check if the biggest bid price is less than smallest ask\n     *\n     * @return bool value of the check. 1 OK, 0 KO.\n     */\n    bool checkBookConsistency(void);\n};\n\n\n#endif /*_OrderBook_H_*/\n"
  },
  {
    "path": "include/OrderPool.hpp",
    "content": "#ifndef _ORDERPOOL_H_\n#define _ORDERPOOL_H_\n\n#include <unordered_map>\n#include <Order.hpp>\n#include <utility.hpp>\n\n/**\n * The class tracks all Order objects created.\n * When an “A” (or “F”) message comes in, it creates a Order object in the OrderPool.\n * When subsequently a message comes in indicating limit order cancellation (“X” and “D”)\n * or a limit order execution (“E”), the information about the price and size of the original\n * limit order is retrieved from the OrderPool using common order ID.\n *\n */\n\nclass OrderPool{\n\n    std::unordered_map<id_type,Order> pool;\n\n    public:\n    OrderPool() = default;\n\n    /**\n     * Look for the Order specified by the id in the OrderPool\n     *\n     * @param[in] idOrder : id relative to the order quaried\n     * @return Order with id equals to idOrder.\n     *\n     */\n    Order searchOrderPool(id_type);\n\n    /**\n     * Initialize and add an Order to the OrderPool\n     *\n     * @param[in] idOrder id of the order to add\n     * @param[in] side side of the order to add (0 for buy and 1 for sell)\n     * @param[in] size size of the order to add to the pool\n     * @param[in] price limit price of the order to add\n     *\n     */\n    void addToOrderPool(id_type, bool, size_type, price_type, const char *);\n\n    /**\n     * Delete size of an order in the OrderPool.\n     *\n     * If the remaining size is zero then order is deleted from the OrderPool.\n     * size is always subtracted from the order.\n     *\n     * @param[in] idOrder id of the order to modify\n     * @param[in] size size to subtract from the order.\n     *\n     */\n    void modifyOrder(id_type, size_type);\n\n    /**\n     * Check whether the OrderPool map is empty\n     *\n     * @return book, 1 if empty, 0 if not.\n     */\n    bool isEmpty(void) const;\n\n    /**\n     * Prints id of all orders in the OrderPool.\n     *\n     * It's used at the end to check if the OrderPool is empty (it should be).\n     *\n     */\n    void printIds(void) const;\n};\n\n#endif /*_ORDERPOOL_H_*/\n"
  },
  {
    "path": "include/Reader.hpp",
    "content": "#ifndef READER_H_\n#define READER_H_\n\n#include <fstream>\n#include <iostream>\n#include <string>\n#include <time.h>\n#include <inttypes.h> // PRIux\n#include <cstring>\n#include <Message.hpp>\n#include <Writer.hpp>\n\nclass Reader{\nprivate:\n    std::string fileName;\n    std::ifstream file;\n    // std::string messageToFilter = \"AFEXDU\"; // QC\n    std::string stock;\n    unsigned count = 0;\n    char message[64];\n    bool validFile = 0;\n    time_t start;\n    // bool debug = 0;\n    // Writer parserWriter;\npublic:\n\n    /**\n     * Constructor for Reader class\n     *\n     * If unable to open file to read print to standard error a Message.\n     * If file has been opened correctly, write it to standard optput.\n     *\n     * @param[in] _fileName destination csv files to update.\n     * @param[in] _stock For performace reasons, the Reader class will discard directly all messages clearly related to other stocks\n     */\n    Reader(const std::string &fileName, const std::string &stock);\n\n    /**\n     * Alternative Constructor for Reader class\n     *\n     * Constructor used in tests where we do not need a fileName.\n     *\n     * @param[in] _stock For performace reasons, the Reader class will discard directly all messages clearly related to other stocks\n     */\n    Reader(const std::string &_stock);\n    bool isValid(void) const;\n    virtual ~Reader();\n\n    /**\n     * Reads bytes from the stream and create a message\n     *\n     * Main function of the class. Creates a Message object from the file stream and return a message to the BookConstructor class.\n     *\n     *@return Message created from the read bytes.\n     *@warning Dead code is still present in the method. Might be used to parse the entire input ITCH50 file for debug purposes\n     *\n     */\n    Message createMessage(void);\n    bool eof(void);\n\n    /**\n     * Progress updates\n     *\n     * Writes to standard output a progress message with the number of messages analyzed up to now and average number of messages per second since the beginnning.\n     *\n     */\n    void printProgress(void);\n\n    /**\n     * Reads n bytes from the opend file\n     *\n     * Reads from the file into the message c-string attribute of the Reader class the specified number of bytes.\n     *\n     *@params[in] size Number of bytes to read from the stream.\n     *\n     */\n    virtual void readBytesIntoMessage(const long &);\n\n    /**\n     * Skips n bytes from the stream\n     *\n     * Discard from the file the specified number of bytes. Used mainly in the tests.\n     *\n     *@params[in] size Number of bytes to discard from the stream.\n     *\n     */\n    virtual void skipBytes(const long &);\n    void setMessage(const char*);\n    virtual char getKey(void);\n    std::string getFileName(void) const;\n    std::string getStock(void) const;\n};\n\n#endif\n"
  },
  {
    "path": "include/Writer.hpp",
    "content": "#ifndef WRITER_H_\n#define WRITER_H_\n\n#include <iostream>\n#include <string>\n#include <fstream>\n\nclass Writer{\n    std::string fileName;\n    std::ofstream file;\n\n    public:\n\n    /**\n     * Constructor for Writer class\n     *\n     * If unable to open file to read print to standard error a message.\n     * If file has been opened correctly, write it to standard optput.\n     *\n     * @param[in] _fileName destination csv files to update.\n     */\n    Writer(const std::string& fileName);\n    Writer() = default;\n    ~Writer();\n\n    /**\n     * Writes string to stream\n     *\n     * It used to write the Message and the OrderBook strings to the outfiles.\n     * @param[in] stringToWrite string to write to the csv.\n     */\n    void writeLine(const std::string &);\n    std::string getFileName(void) const;\n};\n\n#endif\n"
  },
  {
    "path": "include/utility.hpp",
    "content": "#ifndef UTILITY_H\n#define UTILITY_H\n\n#include <iostream>\n#include <climits>\n// #include <netinet/in.h>\n\n/**\n * Declaration of default values.\n *\n * @file utility.cpp\n */\n\ntypedef double price_type;\ntypedef long size_type;\ntypedef uint64_t id_type;\ntypedef long long time_type;\ntypedef bool side_type;\n\nextern id_type ID_DEFAULT;\nextern side_type SIDE_DEFAULT;\nextern size_type SIZE_DEFAULT;\nextern price_type PRICE_DEFAULT;\n\n/**\n * Utility function for swapping 16 bits from little endian to big endian format.\n *\n * Since the binary file is written in big endian and most Unix systems are little endian, we defined this utility functions to swap endianess. Uses binary masks to perform this operation.\n *\n * @param[in] value unsigned 16 type corresponding to the 16 bits in big endian to swap into little endian.\n * @return uint16_t value of the swapped number\n * @warning Assumes that the machine is little endian and hence the swapping is indeed necessary. Otherwise no swapping is needed. This check is not performed.\n */\nuint16_t bswap_16(uint16_t value);\n\n/**\n * Utility function for swapping 32 bits from little endian to big endian format.\n *\n * Since the bynary file is written in big endian and most Unix systems are little endian, we defined this utility functions to swap endianess. Uses binary masks to perform this operation.\n *\n * @param[in] value unsigned 16 type corresponding to the 32 bits in big endian to swap into little endian.\n * @return uint32_t value of the swapped number\n * @warning Assumes that the machine is little endian and hence the swapping is indeed necessary. Otherwise no swapping is needed. This check is not performed.\n */\nuint32_t bswap_32(uint32_t value);\n\n/**\n * Utility function for swapping 64 bits from little endian to big endian format.\n *\n * Since the bynary file is written in big endian and most Unix systems are little endian, we defined this utility functions to swap endianess. Uses binary masks to perform this operation.\n *\n * @param[in] value unsigned 64 type corresponding to the 64 bits in big endian to swap into little endian.\n * @return uint64_t value of the swapped number\n * @warning Assumes that the machine is little endian and hence the swapping is indeed necessary. Otherwise no swapping is needed. This check is not performed.\n */\nuint64_t bswap_64(uint64_t value);\n\n/**\n * Utility function for parsing 16 bits data.\n *\n * This reads from a char array pointer (C-style) 16 bits and return the swapped corresponding number\n *\n * @param[in] a char pointer to the 16 bits to parse\n * @return uint16_t number corresponding to the swapped data (16 bits) pointed by the char array\n * @warning Assumes that the machine is little endian and hence the swapping is indeed necessary. Otherwise no swapping is needed. This check is not performed.\n */\nuint16_t parse_uint16(char * a);\n\n/**\n * Utility function for parsing 32 bits data.\n *\n * This reads from a char array pointer (C-style) 32 bits and return the swapped corresponding number\n *\n * @param[in] a char pointer to the 32 bits to parse\n * @return uint32_t number corresponding to the swapped data (32 bits) pointed by the char array\n * @warning Assumes that the machine is little endian and hence the swapping is indeed necessary. Otherwise no swapping is needed. This check is not performed.\n */\nuint32_t parse_uint32(char * a);\n\n/**\n * Utility function for parsing 64 bits data.\n *\n * This reads from a char array pointer (C-style) 64 bits and return the swapped corresponding number\n *\n * @param[in] a char pointer to the 64 bits to parse\n * @return uint64_t number corresponding to the swapped data (64 bits) pointed by the char array\n * @warning Assumes that the machine is little endian and hence the swapping is indeed necessary. Otherwise no swapping is needed. This check is not performed.\n */\nuint64_t parse_uint64(char * a);\n\n/**\n * Utility function for parsing 48 bits data (for time stamp)\n *\n * This reads from a char array pointer (C-style) 48 bits and return the swapped correpsonding number\n *\n * @param[in] a char pointer to the 48 bits to parse\n * @return uint64_t number corresponding to the swapped data (48 bits) pointed by the char array\n * @warning Assumes that the machine is little endian and hence the swapping is indeed necessary. Otherwise no swapping is needed. This check is not performed.\n */\nuint64_t parse_ts(char * a);\n\n/**\n * Simple utility function for get the file name from a path string\n *\n * @param[in] path string of the path of the file. Should also work for the separator \"\\\\\" (WINDOWS).\n * @return nameFile string of the file name.\n */\nstd::string getFileName(const std::string& s);\n\n#endif /* UTILITY_H_ */\n"
  },
  {
    "path": "src/BookConstructor.cpp",
    "content": "#include <BookConstructor.hpp>\n\n/**\n * Class Initializer.\n *\n * Principal class for the reconstruction of the order book.\n * The constructor also writes the headers to the output files.\n *\n * @param[in] inputMessageCSV decompressed binary ITCH50 file to read from.\n * @param[in] _stock selected stock.\n * @param[in] _levels selected number of levels for order book.\n * @param[out] outputBookCSV, outputMessageCSV destination files to write order book and stream message.\n */\nBookConstructor::BookConstructor(const std::string &inputMessageCSV,\n    const std::string &outputMessageCSV,\n    const std::string &outputBookCSV,\n    const std::string &_stock,\n    const size_t &_levels):\n    message_reader(inputMessageCSV, _stock),\n    messageWriter(outputMessageCSV),\n    bookWriter(outputBookCSV),\n    levels(_levels){\n        messageWriter.writeLine(\"time,type,id,side,size,price,cancSize,execSize,oldId,oldSize,oldPrice,mpid\\n\");\n        std::string bookHeader = \"time,\";\n        for(size_t i = 1; i<=levels; i++){\n            std::string num = std::to_string(i);\n            bookHeader += num+\"_bid_price,\"+num+\"_bid_vol,\"+num+\"_ask_price,\"+num+\"_ask_vol,\";\n        }\n        bookWriter.writeLine(bookHeader.substr(0, bookHeader.size()-1)+'\\n');\n    }\n\n/**\n * Class deconstructor.\n *\n * For debug purposes print to std output orders still present at closure. There shouldn't be any.\n */\nBookConstructor::~BookConstructor(){\n    if(!pool.isEmpty()){\n        std::cout << \"Id's of orders remaining in the book after the market closure: \";\n        pool.printIds();\n    }\n}\n\n/**\n * Start Book reconstruction.\n *\n * calls iteratevely the next method untile the Reader has completed the reading.\n */\nvoid BookConstructor::start(void){\n    while(!message_reader.eof() and message_reader.isValid()){\n        next();\n    }\n}\n\n/**\n * Process next message. Retain only message affecting the OrderBook (type A,P,D,R,E,C).\n * Reads the message from the Reader interface then if necessary, complete message information retriving information from OrderPool, then updates the OrderBook and OrderPool according to the type of message recived.\n * At the end the Writer writes the book and message (enriched with all additional information) to the two output files.\n *\n */\nvoid BookConstructor::next(){\n    message = message_reader.createMessage();\n    if(!message.isEmpty()){\n        bool validMessage = updateMessage();\n        if(validMessage){\n            updatePool();\n            updateBook();\n            WriteBookAndMessage();\n        }\n    }\n}\n\n/**\n * Complete message information with missing field.\n *\n * Once a message is readed by the reader this metod retrives missing informations from the\n * order pool, this behaviour depends on the type of the message.\n * Example : Execution messages miss Price -> retrieve order price from the OP through order ID.\n *\n * A,P: all the informations are already present, stop.\n * D: MPID,size and price information have to be retrived from the Pool.\n * R: MPID,oldSize and oldPrice information have to be retrived from the Pool.\n * E: MPID,size and price have to be retrived from the Pool.\n * C: MPID,size and original price have to be retrived from the Order Pool.\n *\n */\nbool BookConstructor::updateMessage(void){\n    char typeMsg = message.getType();\n\n    if(typeMsg == 'A' or typeMsg == 'P'){\n        return true;\n    }\n\n    // Matching (R)eplace message is done by comparing its oldID with the IDs of preceding processed messages.\n    id_type messageId = (typeMsg == 'R') ? message.getOldId() : message.getId();\n\n    // Find order in pool corresponding to the same ID as currently processing message.\n    Order matchedOrder = pool.searchOrderPool(messageId);\n    if(matchedOrder.isEmpty()){\n        return false;\n    }\n\n    message.setMPID(*matchedOrder.getMPID());\n    message.setSide(matchedOrder.getSide());\n\n    if(typeMsg == 'D'){\n        if (message.getCancSize() == SIZE_DEFAULT){\n            // CancSize has default value, meaning the complete deletion has taken place.\n            // Retrive the information about size to be canceled from matched order.\n            message.setCancSize(matchedOrder.getSize());\n        }\n\n        size_type remainingSize = matchedOrder.getSize() - message.getCancSize();\n        message.setRemSize(remainingSize);\n        message.setPrice(matchedOrder.getPrice());\n    }\n\n    else if(typeMsg == 'R'){\n        message.setOldSize(matchedOrder.getSize());\n        message.setOldPrice(matchedOrder.getPrice());\n    }\n\n    else if(typeMsg == 'E'){\n        message.setPrice(matchedOrder.getPrice());\n        size_type remainingSize = matchedOrder.getSize() - message.getExecSize();\n        message.setRemSize(remainingSize);\n    }\n\n    else if(typeMsg == 'C'){\n        size_type remainingSize = matchedOrder.getSize() - message.getExecSize();\n        message.setRemSize(remainingSize);\n        message.setOldPrice(matchedOrder.getPrice());\n    }\n\n    else{\n        std::cerr << \"Unexpected type of message has been found while updating message! \" << typeMsg << std::endl;\n        return false;\n    }\n    return true;\n}\n\n/**\n * Update OrderBook with the current message.\n *\n * Updates the OrderBook double map accordingly to the type of the message.\n * A: Add the Order to the pool. If key in the map (price) is already there just add the size. Otherwise add the key with corresponding size.\n * R: Replace existing order in the pool, hence cancel completely the existing size and create a new one.\n *\n */\nvoid BookConstructor::updateBook(void){\n    book.setTimeStamp(message.getTimeStamp());\n    char typeMsg = message.getType();\n\n    if(typeMsg == 'A'){\n        book.modifySize(message.getPrice(),message.getRemSize(),message.getSide());\n    }\n    else if(typeMsg == 'R'){\n        // Replace existing order in the pool.\n\n        // Completely cancel the existing order.\n        book.modifySize(message.getOldPrice(),-message.getOldSize(),message.getSide());\n        // Add new order.\n        book.modifySize(message.getPrice(),message.getRemSize(),message.getSide());\n    }\n    else if(typeMsg == 'D'){\n        // Cancel order. Totally or partially.\n        book.modifySize(message.getPrice(),-message.getCancSize(),message.getSide());\n    }\n    else if(typeMsg == 'E'){\n        // Execute order.\n        book.modifySize(message.getPrice(),-message.getExecSize(),message.getSide());\n    }\n    else if(typeMsg == 'C'){\n        // Execute order at different price.\n        book.modifySize(message.getOldPrice(),-message.getExecSize(),message.getSide());\n    }\n    else if(typeMsg=='P'){\n        // Execute hidden order. It does not affect the book.\n    }\n\n    else{\n        std::cerr << \"Unexpected type of message has been found while updating book! \" << typeMsg << std::endl;\n    }\n\n    if(!book.checkBookConsistency()){\n        std::cerr << \"There is a bid price greater than ask.\" << std::endl;\n    }\n}\n\n/**\n * Update OrderPool with the current Message.\n *\n * Using the message attribute in the BookConstructor class to updates the pool.\n * - A: Add order to OrderPool.\n * - R: Delete order and add new one.\n * - D: Delete (partially or totally) order.\n * - E: Execute (partially or totally) order.\n * - C: Execute order at different price.\n * - P: Execute hidden order. Does not affect the book.\n *\n */\nvoid BookConstructor::updatePool(void){\n    char typeMsg = message.getType();\n\n    if(typeMsg == 'A'){\n        pool.addToOrderPool(message.getId(), message.getSide(), message.getRemSize(), message.getPrice(), message.getMPID());\n    }\n    else if(typeMsg=='R'){\n        pool.modifyOrder(message.getOldId(), message.getOldSize());\n        pool.addToOrderPool(message.getId(), message.getSide(), message.getRemSize(), message.getPrice(), message.getMPID());\n    }\n    else if(typeMsg=='D'){\n        pool.modifyOrder(message.getId(), message.getCancSize());\n    }\n    else if(typeMsg=='E'){\n        pool.modifyOrder(message.getId(), message.getExecSize());\n    }\n    else if(typeMsg=='C'){\n        pool.modifyOrder(message.getId(), message.getExecSize());\n    }\n    else if(typeMsg=='P'){\n    }\n    else{\n        std::cerr << \"Unexpected type of message has been found while updating pool! \" << typeMsg << std::endl;\n    }\n}\n\n/**\n * Write in output OrderBook state and message stream through Writer class.\n *\n */\nvoid BookConstructor::WriteBookAndMessage(void){\n    bookWriter.writeLine(book.getString(levels));\n    messageWriter.writeLine(message.getString());\n}\n"
  },
  {
    "path": "src/Message.cpp",
    "content": "#include \"Message.hpp\"\n\nMessage::Message(const char& _type,\n    const id_type& _id,\n    const time_type& _timestamp):\n        type(_type),\n        id(_id),\n        timestamp(_timestamp){};\n\nvoid Message::setType(const char& _type){\n    if(_type =='A' || _type == 'F'){\n        // Messages related to adding new order.\n        // The only difference between them:\n        // \"A\"  - The market participant behind the order is anonymous.\n        // \"F\"  - ID of the market participant behind the order is available\n        // The information is irrelevant therefore we do not distinguish between them.\n        type = 'A'; // (A)dd\n    }\n    else if (_type == 'D' || _type == 'X'){\n        // Messages related to cancellation of the order:\n        // \"D\" - complete cancellation; the order related to this message has to be deleted from the book\n        // \"X\" - partial cancellation\n        type = 'D'; // (D)elete\n    }\n    else if (_type == 'U'){\n        // The message is sent whenever existing order has been completelly canceled and replaced by the new one.\n        type = 'R'; // (R)eplace\n    }\n    else if (_type == 'E'){\n        // The message is sent whenever an order on the book is executed in whole or in part.\n        type = _type; // (E)xecute\n    }\n    else if (_type == 'P'){\n        // The message indicates when a match occurs between non-­­displayable order types.\n        // Since no Add Order Message is generated when a non-­displayed order is initially received\n        // it does not affect the book.\n        type = _type; // execute hidden message\n    }\n    else if (_type == 'C'){\n        // The message is sent whenever an order on the book is executed\n        // in whole or in part at a price different from the initial display price.\n        type = _type;\n    }\n    else{\n        std::cerr << \"Message with wrong type (\" << _type << \") has been found!\"<< std::endl;\n    }\n}\n\nvoid Message::setId(const id_type& _id){\n    id = _id;\n}\n\nvoid Message::setTimeStamp(const time_type& _timestamp){\n    timestamp = _timestamp;\n}\n\nvoid Message::setSide(const side_type& _side){\n    side = _side;\n}\n\nvoid Message::setPrice(const price_type& _price){\n    price = _price;\n}\n\nvoid Message::setRemSize(const size_type& _size){\n    remSize = _size;\n}\n\nvoid Message::setCancSize(const size_type& _size){\n    cancSize = _size;\n}\n\nvoid Message::setExecSize(const size_type& _size){\n    execSize = _size;\n}\n\nvoid Message::setOldId(const id_type& _id){\n    oldId = _id;\n}\n\nvoid Message::setOldPrice(const price_type& _price){\n    oldPrice = _price;\n}\n\nvoid Message::setOldSize(const size_type& _size){\n    oldSize = _size;\n}\n\nvoid Message::setMPID(const char& _mpid){\n    strncpy(mpid, &_mpid, 4); mpid[4] = 0;\n}\n\n// getters\nchar Message::getType() const{\n    return type;\n}\n\nid_type Message::getId() const{\n    return id;\n}\n\ntime_type Message::getTimeStamp()const{\n    return timestamp;\n}\n\nside_type Message::getSide()const{\n    return side;\n}\n\nprice_type Message::getPrice()const{\n    return price;\n}\n\nsize_type Message::getRemSize()const{\n    return remSize;\n}\n\nsize_type Message::getCancSize()const{\n    return cancSize;\n}\n\nsize_type Message::getExecSize()const{\n    return execSize;\n}\n\nid_type Message::getOldId()const{\n    return oldId;\n}\n\nprice_type Message::getOldPrice()const{\n    return oldPrice;\n}\n\nsize_type Message::getOldSize()const{\n    return oldSize;\n}\n\nconst char * Message::getMPID()const{\n    return mpid;\n}\n\nbool Message::isEmpty()const{\n    return (id==ID_DEFAULT);\n}\n\nstd::string Message::getString(void)const{\n    std::ostringstream string_builder;\n    if(!isEmpty()){\n        string_builder  << timestamp << \",\";\n    }\n    else{\n        string_builder  << \",\";\n    }\n\n    if(!isEmpty()){\n        string_builder  << type  << \",\"\n                        << id << \",\"\n                        << side << \",\";\n    }\n    else{\n        string_builder  << \",\"\n                        << \",\"\n                        << \",\";\n    }\n    if(remSize!=SIZE_DEFAULT){\n        string_builder << remSize;\n    }\n    string_builder << \",\";\n\n    if(price!=PRICE_DEFAULT){\n        string_builder << price;\n    }\n    string_builder << \",\";\n\n    if(cancSize!=SIZE_DEFAULT){\n        string_builder << cancSize;\n    }\n    string_builder << \",\";\n\n    if(execSize!=SIZE_DEFAULT){\n        string_builder << execSize;\n    }\n    string_builder << \",\";\n\n    if(oldId!=ID_DEFAULT){\n        string_builder << oldId;\n    }\n    string_builder << \",\";\n\n    if(oldSize!=SIZE_DEFAULT){\n        string_builder << oldSize;\n    }\n    string_builder << \",\";\n\n    if(oldPrice!=PRICE_DEFAULT){\n        string_builder << oldPrice;\n    }\n    string_builder << \",\";\n    string_builder << mpid;\n    string_builder << std::endl;\n\n    return string_builder.str();\n}\n\nvoid Message::print() const {\n    std::cout << \"Message type   :\" << type <<std::endl;\n    std::cout << \"Id             :\"<< id << std::endl;\n    std::cout << \"timestamp      :\" << timestamp << std::endl;\n    std::cout << \"Side           :\" << side << std::endl;\n    std::cout << \"Price          :\"<< price << std::endl;\n    std::cout << \"Remaining size :\" << remSize << std::endl;\n    if (type == 'D'){\n        std::cout << \"Deletion size  :\" << cancSize << std::endl;\n    }\n    if (type == 'E'){\n        std::cout << \"Execution size :\" << execSize << std::endl;\n    }\n    if (type == 'U'){\n        std::cout << \"Old Id         :\" << oldId << std::endl;\n        std::cout << \"Old size       :\"<< oldSize << std::endl;\n        std::cout << \"Old price      :\" << oldPrice << std::endl;\n    }\n    if (type == 'F'){\n        std::cout << \"MPID :\" << mpid << std::endl;\n    }\n    std::cout << \" \" << std::endl;\n}\n"
  },
  {
    "path": "src/Order.cpp",
    "content": "#include \"Order.hpp\"\n\nOrder::Order(id_type _id,\n            bool _side,\n            size_type _size,\n            price_type _price,\n            const char *_mpid):\n        id(_id),\n        side(_side),\n        size(_size),\n        price(_price){\n            strncpy(mpid, _mpid, 4); mpid[4] = 0;\n        }\n\nid_type Order::getId(void) const{\n    return id;\n}\n\nbool Order::getSide(void) const{\n    return side;\n}\n\nsize_type Order::getSize(void) const{\n    return size;\n}\n\nprice_type Order::getPrice(void) const{\n    return price;\n}\n\nconst char * Order::getMPID()const{\n    return mpid;\n}\n\nvoid Order::addSize(size_type _size){\n    size+=_size;\n    if(size<0){\n        std::cerr << \"Updated order has negative size\" << std::endl;\n        print();\n    }\n}\n\nvoid Order::print() const{\n    if (id != ID_DEFAULT){\n        std::cout << \"Id             :\" <<id<< std::endl;\n        std::cout << \"side           :\" << side <<std::endl;\n        std::cout << \"size           :\" << size <<std::endl;\n        std::cout << \"price          :\" <<price<<std::endl;\n    }\n    else{\n        std::cerr << \"Trying to print an empty order\" << std::endl;\n    }\n}\n\nbool Order::isEmpty() const{\n    return (id == ID_DEFAULT);\n}\n"
  },
  {
    "path": "src/OrderBook.cpp",
    "content": "#include <OrderBook.hpp>\n\nstd::string OrderBook::getString(const size_t &level) const {\n\n    size_t buyDepth = buySide.size(); // The number of bid prices available in the book\n    size_t sellDepth = sellSide.size(); // The number of ask prices available in the book\n    std::ostringstream string_builder;\n    string_builder << timestamp << \",\";\n\n    // The best bid price is the smallest value stored in ordered map\n    std::map<price_type,size_type>::const_reverse_iterator it_buy = buySide.rbegin();\n\n    // The best ask price is the biggest value stored in ordered map\n    std::map<price_type,size_type>::const_iterator it_sell = sellSide.begin();\n\n\n    for (unsigned i = 0; i < level; i++){\n        if( i < buyDepth){\n            string_builder << it_buy->first << \",\"<< it_buy->second << \",\";\n            ++it_buy;\n        }\n        else{ // no prices on buy side left\n            string_builder << \",,\";\n        }\n\n        if (i < sellDepth){\n            string_builder << it_sell->first << \",\" << it_sell->second << \",\";\n            ++it_sell;\n        }\n        else{ // no prices on sell side left\n            string_builder << \",,\";\n        }\n    }\n\n    // Removing the last character from string stream: comma\n    string_builder.seekp(-1, std::ios_base::end);\n    string_builder << std::endl;\n\n    return string_builder.str();\n\n }\n\nvoid OrderBook::modifySize(price_type price, size_type size, side_type side){\n    if (side){ // modify sellSide\n        sellSide[price] += size;\n        if (sellSide[price] == 0 )\n            sellSide.erase(price);\n        else if(sellSide[price] < 0)\n            std::cerr << \"Negative size in order book is found! \" << std::endl;\n    }\n    else{ // modify buySide\n        buySide[price] += size;\n        if (buySide[price] == 0 )\n            buySide.erase(price);\n        else if(buySide[price] < 0)\n            std::cerr << \"Negative size in order book is found! \" << std::endl;\n    }\n}\n\nbool OrderBook::checkBookConsistency(){\n    if (!(buySide.empty() or sellSide.empty())){\n        return ((buySide.rbegin()->first) <= (sellSide.begin()->first));\n    }\n    else{\n        return 1;\n    }\n}\n\nvoid OrderBook::setTimeStamp(const time_type &t){\n    timestamp = t;\n}\n"
  },
  {
    "path": "src/OrderPool.cpp",
    "content": "#include <OrderPool.hpp>\n\nOrder OrderPool::searchOrderPool(id_type idOrder){\n    auto foundElement = pool.find(idOrder);\n    Order foundOrder;\n    if (foundElement != pool.end()){\n        foundOrder = foundElement->second;\n    }\n    return foundOrder;\n}\n\nvoid OrderPool::addToOrderPool(id_type idOrder, bool side, size_type size, price_type price, const char *mpid){\n    Order orderToAdd(idOrder, side, size, price, mpid);\n    pool[idOrder] = orderToAdd;\n}\n\nvoid OrderPool::modifyOrder(id_type idOrder, size_type size = 0){\n    pool[idOrder].addSize(-size);\n    if( pool[idOrder].getSize() == 0 ){\n        pool.erase(idOrder);\n    }\n}\n\nbool OrderPool::isEmpty(void) const{\n    return pool.empty();\n}\n\nvoid OrderPool::printIds(void) const{\n    for(auto items: pool){\n        std::cout << items.first << std::endl;\n    }\n};\n"
  },
  {
    "path": "src/Reader.cpp",
    "content": "#include <Reader.hpp>\n\nReader::Reader(const std::string &_fileName, const std::string &_stock):\n    fileName(_fileName), stock(_stock){\n        file.open(fileName);\n        if(!file.is_open()){\n            std::cerr << \"The input file: \" << fileName << \" cannot be open! \" << std::endl;\n            }\n        else{\n            std::cout << \"Opened \" << fileName << \" to read ITCH 5.0. messages.\" << std::endl;\n            validFile = 1;\n        }\n        start = time(0);\n    }\n\nReader::Reader(const std::string &_stock): stock(_stock){}\n\nvoid Reader::printProgress(void){\n    count ++;\n    if(count % 10000000 == 0){\n        std::cout << \"Processed \" << count/1000000 << \"Mio messages. \" << int(count/difftime(time(0), start)/10000)/100. << \" Mio messages per sec.\" << std::endl;\n    }\n}\n\nvoid Reader::readBytesIntoMessage(const long &size){\n    file.read(message, size);\n}\n\nvoid Reader::skipBytes(const long &size){\n    file.ignore(size);\n}\n\nchar Reader::getKey(void){\n    char key;\n    file.get(key);\n    return key;\n}\n\nMessage Reader::createMessage(void){\n    printProgress();\n    Message msg;\n    skipBytes(2);\n    char key = getKey();\n    char ticker[9];\n    strncpy(ticker, stock.c_str(), 8); ticker[8] = 0;\n    switch(key){\n        uint64_t timeStamp, orderId, oldOrderId, newOrderId;\n        uint32_t size, price, execSize, cancSize, newSize, newPrice;\n        char direction;\n        char mpid[5];\n\n        case 'S':\n            readBytesIntoMessage(11);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     eventCode = message[10];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%c\\n\", key, locateCode,trackingNumb,timeStamp,eventCode);\n            // }\n            break;\n        case 'R':\n            readBytesIntoMessage(38);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     strncpy(ticker, message+10, 8); ticker[8] = 0;\n            //     marketCategory = message[18];\n            //     financialStatus = message[19];\n            //     numberOfSharesInALot = parse_uint32(message+20);\n            //     roundLotsOnly = message[24];\n            //     issueClassification = message[25];\n            //     strncpy(subType, message+26, 2); subType[2] = 0;\n            //     autenticity = message[28];\n            //     shortIndicator = message[29];\n            //     ipoFlag = message[30];\n            //     indicatorLULD = message[31];\n            //     flagETP = message[32];\n            //     ETPLevarage = parse_uint32(message+33);\n            //     inverseETPFlag = message[37];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%s,%c,%c,%\" PRIu32 \",%c,%c,%s,%c,%c,%c,%c,%c,%\" PRIu32 \",%c\\n\", key,locateCode,trackingNumb,timeStamp,ticker,marketCategory,financialStatus,numberOfSharesInALot,roundLotsOnly,issueClassification,subType,autenticity,shortIndicator,ipoFlag,indicatorLULD,flagETP,ETPLevarage,inverseETPFlag);\n            // }\n            break;\n        case 'H':\n            readBytesIntoMessage(24);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     strncpy(ticker, message+10, 8); ticker[8] = 0;\n            //     tradingState = message[18];\n            //     reserved = message[19];\n            //     strncpy(reason, message+20, 4); reason[4] = 0;\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%s,%c,%c,%s\\n\",key,locateCode,trackingNumb,timeStamp,ticker,tradingState,reserved,reason);\n            // }\n            break;\n        case 'Y':\n            readBytesIntoMessage(19);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     strncpy(ticker, message+10, 8); ticker[8] = 0;\n            //     regSHO = message[18];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%s,%c\\n\",key,locateCode,trackingNumb,timeStamp,ticker,regSHO);\n            // }\n            break;\n        case 'L':\n            readBytesIntoMessage(25);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     strncpy(mpidIdentifier, message+10, 4); mpidIdentifier[4] = 0;\n            //     strncpy(ticker, message+14, 8); ticker[8] = 0;\n            //     primaryMarketMaker = message[22];\n            //     marketMakerMode = message[23];\n            //     makerParticipantState = message[24];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%s,%s,%c,%c,%c\\n\",key,locateCode,trackingNumb,timeStamp,mpidIdentifier,ticker,primaryMarketMaker,marketMakerMode,makerParticipantState);\n            // }\n            break;\n        case 'V':\n            readBytesIntoMessage(34);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     level1 = parse_uint64(message+10);\n            //     level2 = parse_uint64(message+18);\n            //     level3 = parse_uint64(message+26);\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \".%08\" PRIu64 \",%\" PRIu64 \".%08\" PRIu64 \",%\" PRIu64 \".%08\" PRIu64 \"\\n\",key,locateCode,trackingNumb,timeStamp,level1/100000000,level1%100000000,level2/100000000,level2%100000000,level3/100000000,level3%100000000);\n            // }\n            break;\n        case 'W':\n            readBytesIntoMessage(11);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     breachedLevel = message[10];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%c\\n\",\n            //         key,locateCode,trackingNumb,timeStamp,breachedLevel);\n            // }\n            break;\n        case 'K':\n            readBytesIntoMessage(27);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     strncpy(ticker, message+10, 8); ticker[8] = 0;\n            //     timeIPO = parse_uint32(message+18);\n            //     qualifierIPO = message[22];\n            //     priceIPO = parse_uint32(message+23);\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%s,%\" PRIu32 \",%c,%\" PRIu32 \".%04\" PRIu32 \"\\n\",\n            //         key,locateCode,trackingNumb,timeStamp,ticker,timeIPO,qualifierIPO,\n            //         priceIPO/10000,priceIPO%10000);\n            // }\n            break;\n        case 'J':\n            readBytesIntoMessage(34);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     strncpy(ticker, message+10, 8); ticker[8] = 0;\n            //     auctionCollarPrice = parse_uint32(message+18);\n            //     upperAuctionCollarPrice = parse_uint32(message+22);\n            //     lowerAuctionCollarPrice = parse_uint32(message+26);\n            //     auctionCollarExtension = message[30];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%s,%\" PRIu32 \".%04\" PRIu32 \",%\" PRIu32 \".%04\" PRIu32 \",%\" PRIu32 \".%04\" PRIu32 \",%c\\n\",\n            //         key,locateCode,trackingNumb,timeStamp,ticker,\n            //         auctionCollarPrice/10000,auctionCollarPrice%10000,\n            //         upperAuctionCollarPrice/10000,upperAuctionCollarPrice%10000,\n            //         lowerAuctionCollarPrice/10000,lowerAuctionCollarPrice%10000,\n            //         auctionCollarExtension);\n            // }\n            break;\n        case 'h':\n            readBytesIntoMessage(20);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     strncpy(ticker, message+10, 8); ticker[8] = 0;\n            //     marketCode = message[18];\n            //     haltAction = message[19];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%s,%c,%c\\n\",\n            //         key,locateCode,trackingNumb,timeStamp,ticker,marketCode,haltAction);\n            // }\n            break;\n        case 'A':\n            readBytesIntoMessage(35);\n            timeStamp = parse_ts(message+4);\n            orderId = parse_uint64(message+10);\n            direction = message[18];\n            size = parse_uint32(message+19);\n            strncpy(ticker, message+23, 8); ticker[8] = 0;\n            price = parse_uint32(message+31);\n            // if(debug){\n            //    locateCode = parse_uint16(message);\n            //    trackingNumb = parse_uint16(message+2);\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \",%c,%\" PRIu32 \",%s,%\" PRIu32 \".%04\" PRIu32 \"\\n\",\n            //         key,locateCode,trackingNumb,timeStamp,orderId,\n            //         direction,size,ticker,\n            //         price/10000,price%10000);\n            // }\n            msg.setType(key);\n            msg.setTimeStamp(static_cast<time_type>(timeStamp));\n            msg.setId(static_cast<id_type>(orderId));\n            msg.setSide(static_cast<side_type>(direction == 'S'));\n            msg.setRemSize(static_cast<size_type>(size));\n            msg.setPrice(static_cast<price_type>(price)/10000);\n            break;\n        case 'F':\n            readBytesIntoMessage(39);\n            timeStamp = parse_ts(message+4);\n            orderId = parse_uint64(message+10);\n            direction = message[18];\n            size = parse_uint32(message+19);\n            strncpy(ticker, message+23, 8); ticker[8] = 0;\n            price = parse_uint32(message+31);\n            // if(debug){\n            //    locateCode = parse_uint16(message);\n            //    trackingNumb = parse_uint16(message+2);\n            strncpy(mpid, message+35, 4); mpid[4] = 0;\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \",%c,%\" PRIu32 \",%s,%\" PRIu32 \".%04\" PRIu32 \",%s\\n\",\n            //     key,locateCode,trackingNumb,timeStamp,orderId,\n            //     direction,size,ticker,\n            //     price/10000,price%10000,mpid);\n            // }\n            msg.setType(key);\n            msg.setTimeStamp(static_cast<time_type>(timeStamp));\n            msg.setId(static_cast<id_type>(orderId));\n            msg.setSide(static_cast<side_type>(direction == 'S'));\n            msg.setRemSize(static_cast<size_type>(size));\n            msg.setPrice(static_cast<price_type>(price)/10000);\n            msg.setMPID(*mpid);\n            break;\n        case 'E':\n            readBytesIntoMessage(30);\n            timeStamp = parse_ts(message+4);\n            orderId = parse_uint64(message+10);\n            execSize = parse_uint32(message+18);\n            // if(debug){\n            //    locateCode = parse_uint16(message);\n            //    trackingNumb = parse_uint16(message+2);\n            //     matchNumber = parse_uint64(message+22);\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \",%\" PRIu32 \",%\" PRIu64 \"\\n\",\n            //     key,locateCode,trackingNumb,timeStamp,orderId,\n            //     execSize,matchNumber);\n            // }\n            msg.setType(key);\n            msg.setTimeStamp(static_cast<time_type>(timeStamp));\n            msg.setId(static_cast<id_type>(orderId));\n            msg.setExecSize(static_cast<size_type>(execSize));\n            break;\n        case 'C':\n            readBytesIntoMessage(35);\n            timeStamp = parse_ts(message+4);\n            orderId = parse_uint64(message+10);\n            execSize = parse_uint32(message+18);\n            price = parse_uint32(message+31);\n            // if(debug){\n            //    locateCode = parse_uint16(message);\n            //    trackingNumb = parse_uint16(message+2);\n            //     matchNumber = parse_uint64(message+22);\n            //     printable = message[30];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \",%\" PRIu32 \",%\" PRIu64 \",%c,%\" PRIu32 \".%04\" PRIu32 \"\\n\",\n            //     key,locateCode,trackingNumb,timeStamp,orderId,\n            //     execSize,matchNumber,printable,\n            //     price/10000,price%10000);\n            // }\n            msg.setType(key);\n            msg.setTimeStamp(static_cast<time_type>(timeStamp));\n            msg.setId(static_cast<id_type>(orderId));\n            msg.setExecSize(static_cast<size_type>(execSize));\n            msg.setPrice(static_cast<price_type>(price)/10000);\n            break;\n        case 'X':\n            readBytesIntoMessage(22);\n            timeStamp = parse_ts(message+4);\n            orderId = parse_uint64(message+10);\n            cancSize = parse_uint32(message+18);\n            // if(debug){\n            //    locateCode = parse_uint16(message);\n            //    trackingNumb = parse_uint16(message+2);\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \",%\" PRIu32 \"\\n\",\n            //     key,locateCode,trackingNumb,timeStamp,orderId,cancSize);\n            // }\n            msg.setType(key);\n            msg.setTimeStamp(static_cast<time_type>(timeStamp));\n            msg.setId(static_cast<id_type>(orderId));\n            msg.setCancSize(static_cast<size_type>(cancSize));\n            break;\n        case 'D':\n            readBytesIntoMessage(18);\n            timeStamp = parse_ts(message+4);\n            orderId = parse_uint64(message+10);\n            // if(debug){\n            //    locateCode = parse_uint16(message);\n            //    trackingNumb = parse_uint16(message+2);\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \"\\n\",\n            //     key,locateCode,trackingNumb,timeStamp,orderId);\n            // }\n            msg.setType(key);\n            msg.setTimeStamp(static_cast<time_type>(timeStamp));\n            msg.setId(static_cast<id_type>(orderId));\n            break;\n        case 'U':\n            readBytesIntoMessage(34);\n            timeStamp = parse_ts(message+4);\n            oldOrderId = parse_uint64(message+10);\n            newOrderId = parse_uint64(message+18);\n            newSize = parse_uint32(message+26);\n            newPrice = parse_uint32(message+30);\n            // if(debug){\n            //    locateCode = parse_uint16(message);\n            //    trackingNumb = parse_uint16(message+2);\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \",%\" PRIu64 \",%\" PRIu32 \",%\" PRIu32 \".%04\" PRIu32 \"\\n\",\n            //     key,locateCode,trackingNumb,timeStamp,oldOrderId,\n            //     newOrderId,newSize,newPrice/10000,newPrice%10000);\n            // }\n            msg.setType(key);\n            msg.setTimeStamp(static_cast<time_type>(timeStamp));\n            msg.setId(static_cast<id_type>(newOrderId));\n            msg.setOldId(static_cast<id_type>(oldOrderId));\n            msg.setRemSize(static_cast<size_type>(newSize));\n            msg.setPrice(static_cast<price_type>(newPrice)/10000);\n            break;\n        case 'P':\n            readBytesIntoMessage(43);\n            timeStamp = parse_ts(message+4);\n            orderId = parse_uint64(message+10);\n            direction = message[18];\n            size = parse_uint32(message+19);\n            strncpy(ticker, message+23, 8); ticker[8] = 0;\n            price = parse_uint32(message+31);\n            // if(debug){\n            //    locateCode = parse_uint16(message);\n            //    trackingNumb = parse_uint16(message+2);\n            //    matchId = parse_uint64(message+35);\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \",%c,%\" PRIu32 \",%s,%\" PRIu32 \".%04\" PRIu32 \",%\" PRIu64 \"\\n\",\n            //     key,locateCode,trackingNumb,timeStamp,orderId,\n            //     direction,size,ticker,price/10000,price%10000,matchId);\n            // }\n            msg.setType(key);\n            msg.setTimeStamp(static_cast<time_type>(timeStamp));\n            msg.setId(static_cast<id_type>(orderId));\n            msg.setSide(static_cast<side_type>(direction == 'S'));\n            msg.setExecSize(static_cast<size_type>(size));\n            msg.setPrice(static_cast<price_type>(price)/10000);\n            break;\n        case 'Q':\n            readBytesIntoMessage(39);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     size64 = parse_uint64(message+10);\n            //     strncpy(ticker, message+18, 8); ticker[8] = 0;\n            //     price = parse_uint32(message+26);\n            //     matchId = parse_uint64(message+30);\n            //     crossType = message[38];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \",%s,%\" PRIu32 \".%04\" PRIu32 \",%\" PRIu64 \",%c\\n\",\n            //     key,locateCode,trackingNumb,timeStamp,size64,\n            //     ticker,price/10000,price%10000,matchId,crossType);\n            // }\n\n            // Check how to use Q.\n            break;\n        case 'B':\n            readBytesIntoMessage(18);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     matchId = parse_uint64(message+10);\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \"\\n\",\n            //     key,locateCode,trackingNumb,timeStamp,matchId);\n            // }\n            break;\n        case 'I':\n            readBytesIntoMessage(49);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     pairedShares = parse_uint64(message+10);\n            //     imbalanceShares = parse_uint64(message+18);\n            //     imbalanceDirection = message[26];\n            //     strncpy(ticker, message+27, 8); ticker[8] = 0;\n            //     fairPrice = parse_uint32(message+35);\n            //     nearPrice = parse_uint32(message+39);\n            //     referencePrice = parse_uint32(message+43);\n            //     crossType = message[47];\n            //     priceVariationIndicator = message[48];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%\" PRIu64 \",%\" PRIu64 \",%c,%s,%\" PRIu32 \".%04\" PRIu32 \",%\" PRIu32 \".%04\" PRIu32 \",%\" PRIu32 \".%04\" PRIu32 \",%c,%c\\n\",\n            //         key,locateCode,trackingNumb,timeStamp,pairedShares,\n            //         imbalanceShares,imbalanceDirection,ticker,\n            //         fairPrice/10000,fairPrice%10000,\n            //         nearPrice/10000,nearPrice%10000,\n            //         referencePrice/10000,referencePrice%10000,\n            //         crossType,priceVariationIndicator);\n            // }\n            break;\n        case 'N':\n            readBytesIntoMessage(19);\n            // if(debug){\n            //     locateCode = parse_uint16(message);\n            //     trackingNumb = parse_uint16(message+2);\n            //     timeStamp = parse_ts(message+4);\n            //     strncpy(ticker, message+10, 8); ticker[8] = 0;\n            //     interest = message[18];\n            //     sprintf(str,\"%c,%\" PRIu16 \",%\" PRIu16 \",%\" PRIu64 \",%s,%c\\n\", key, locateCode,trackingNumb,timeStamp,ticker,interest);\n            // }\n            break;\n        default:\n            if(!eof()){\n                std::cerr << \"Type \" << key <<\" not found: abort\" << std::endl;\n                validFile = 0;\n            }\n            break;\n    }\n    // if(debug){\n    //     parserWriter.writeLine(std::string(str));\n    // }\n    if(strcmp(ticker,stock.c_str())){\n        return Message();\n    }\n    return msg;\n}\n\nbool Reader::eof(void){\n    return file.eof();\n}\n\nbool Reader::isValid(void) const{\n    return validFile;\n}\n\nstd::string Reader::getFileName(void) const{\n    return fileName;\n}\n\nstd::string Reader::getStock(void) const{\n    return stock;\n}\n\nvoid Reader::setMessage(const char* str){\n    memcpy(message, str, 64);\n}\n\nReader::~Reader(){\n    if (file.is_open()){\n        file.close();\n        std::cout << \"File \" << fileName << \" has been closed\" << std::endl;\n        std::cout << \"Finished, processed \" << count << \" messages in \" << difftime(time(0),start) << \"seconds.\"  << std::endl;\n    }\n}\n"
  },
  {
    "path": "src/Writer.cpp",
    "content": "#include <Writer.hpp>\n\nWriter::Writer(const std::string& _fileName):fileName(_fileName){\n  file.open(_fileName);\n  if(!file.is_open()){\n      std::cerr << \"The output file: \" << fileName << \" cannot be open! \" << std::endl;\n      }\n  else {\n      std::cout << \"Opened \" << fileName << \" for writing.\" << std::endl;\n  }\n}\n\nvoid Writer::writeLine(const std::string &stringToWrite){\n  file << stringToWrite;\n}\n\nWriter::~Writer(){\n  if (file.is_open())\n     {\n         file.flush();\n         file.close();\n         std::cout << \"File \" << fileName << \" has been closed.\" << std::endl;\n    }\n}\n\nstd::string Writer::getFileName(void) const{\n  return fileName;\n}\n"
  },
  {
    "path": "src/main.cpp",
    "content": "#include <iostream>\n#include <string>\n#include <BookConstructor.hpp>\n\n/**\n * Declaration of main function.\n *\n * @file main.cpp\n */\n\n/**\n * main\n *\n * Interface to use the BookRecustruction Class\n * WARNING: this is lightweight and do not perform checks for file existence, good formatting\n * of inputs ecc..\n *\n * @param[in] argc An integer argument count of the command line arguments\n * @param[in] argv An argument vector of the command line arguments:\n * @param[in] path to the unzipped ITCH raw data binary file\n * @param[in] path to the directory to which the output book csv file will be written to (add trailing backslash!)\n * @param[in] path to the directory to which the output message csv file will be written to (add trailing backslahs!)\n * @param[in] integer this is the depth of the orderbook, for bid and ask side\n * @param[in] name string of the stock you want the reconstruct the book\n *\n */\n\nint main(int argc, char * argv[]){\n\n    if(argc<5){\n        std::cerr << \"The program should be called with: ./BookConstructor path_input_file dir_book dir_messages levels stock\" << std::endl;\n        return 1;\n    }\n\n    std::string pathFile = argv[1];\n    std::string outBookDirectory = argv[2];\n    std::string outMessageDirectory = argv[3];\n    size_t levels = std::stoul(argv[4]);\n    std::string stock = argv[5];\n    std::string nameFile = getFileName(pathFile);\n    std::string outBookFileName = outBookDirectory+nameFile+\"_\"+stock+\"_book_\"+std::to_string(levels)+\".csv\";\n    std::string outMessageFileName = outMessageDirectory+nameFile+\"_\"+stock+\"_message.csv\";\n    std::string stockPadded = stock;\n    stockPadded.insert(stockPadded.end(), 8 - stockPadded.size(), ' ');\n\n    BookConstructor BookConstructor(pathFile,\n        outMessageFileName,\n        outBookFileName,\n        stockPadded,\n        levels);\n\n    BookConstructor.start();\n\n    return 0;\n}\n"
  },
  {
    "path": "src/utility.cpp",
    "content": "#include <utility.hpp>\n\nside_type SIDE_DEFAULT = 0;\nid_type ID_DEFAULT = LLONG_MAX;\nprice_type PRICE_DEFAULT = -1;\nsize_type SIZE_DEFAULT = -1;\n\nstd::string getFileName(const std::string& path) {\n\n   char sep = '/';\n\n#ifdef _WIN32\n   sep = '\\\\';\n#endif\n\n   size_t i = path.rfind(sep, path.length());\n   if (i != std::string::npos) {\n      return(path.substr(i+1, path.length() - i));\n   }\n\n   return(\"\");\n}\n\nuint16_t bswap_16(uint16_t value){\n    return static_cast<uint16_t>(((value) & 0xff) << 8 | ((value) >> 8));\n}\n\nuint32_t bswap_32(uint32_t value){\n    return ((static_cast<uint32_t>(bswap_16(static_cast<uint16_t>((value) & 0xffff))) << 16) |\n    static_cast<uint32_t>(bswap_16(static_cast<uint16_t>((value) >> 16))));\n}\n\nuint64_t bswap_64(uint64_t value){\n    return ((static_cast<uint64_t>(bswap_32(static_cast<uint32_t>((value) & 0xffffffff))) << 32) |\n    static_cast<uint64_t>(bswap_32(static_cast<uint32_t>((value) >> 32))));\n}\n\nuint16_t parse_uint16(char * a){\n    return bswap_16(*(reinterpret_cast<uint16_t *>(a)));\n    // return ntohs(*(reinterpret_cast<uint16_t *>(a)));\n}\n\nuint32_t parse_uint32(char * a){\n    return bswap_32(*(reinterpret_cast<uint32_t *>(a)));\n    // return ntohl(*(reinterpret_cast<uint32_t *>(a)));\n}\n\nuint64_t parse_uint64(char * a){\n    return bswap_64(*(reinterpret_cast<uint64_t *>(a)));\n    // return ntohl(*(reinterpret_cast<uint64_t *>(a)));\n}\n\nuint64_t parse_ts(char * a){\n    return (((static_cast<uint64_t>(bswap_16(*(reinterpret_cast<uint16_t *>(a))))) << 32) |\n    static_cast<uint64_t>(bswap_32(*(reinterpret_cast<uint32_t *>(a+2)))));\n}\n"
  },
  {
    "path": "tmp/readFile.cpp",
    "content": "#include <iostream>\n#include <fstream>\n\n/*\n* This takes: 100s\n* for 360Mio ~ 4'764'426'114 bytes\n* 3.6 Mio Messages/sec.\n*/\n\nint main(){\n    std::ifstream myfile;\n    char c;\n    char buffer[30];\n    myfile.open(\"../data/binary/01302019.NASDAQ_ITCH50.gz\");\n    while (!myfile.eof()){\n        myfile.read(buffer,1);\n    }\n    myfile.close();\n    return 0;\n}\n"
  },
  {
    "path": "tmp/test_htohs.cpp",
    "content": "#include <stdio.h>\n#include <iostream>\n#include <netinet/in.h>\n\nint main()\n{\n    const char a[2] = {'\\x09','\\x64'};\n    unsigned b = *((unsigned int *)a);\n    std::cout << b << std::endl;\n    std::cout << ntohs(b);\n    \n}\n"
  }
]